Microsoft Word - September_ITAL_Park_proofed.docx


Evaluation	
  of	
  Semi-­‐Automatic	
  	
  
Metadata	
  Generation	
  Tools:	
  A	
  Survey	
  	
  
of	
  the	
  Current	
  State	
  of	
  the	
  Art	
  

	
  
Jung-­‐ran	
  Park	
  

	
  and	
  	
  
Andrew	
  Brenza	
  

	
  

INFORMATION	
  TECHNOLOGY	
  AND	
  LIBRARIES	
  |	
  SEPTEMBER	
  2015	
  	
  

	
   	
   	
   	
  

22	
  

ABSTRACT	
  

Assessment	
  of	
  the	
  current	
  landscape	
  of	
  semi-­‐automatic	
  metadata	
  generation	
  tools	
  is	
  particularly	
  
important	
  considering	
  the	
  rapid	
  development	
  of	
  digital	
  repositories	
  and	
  the	
  recent	
  explosion	
  of	
  big	
  
data.	
  Utilization	
  of	
  semi-­‐automatic	
  metadata	
  generation	
  is	
  critical	
  in	
  addressing	
  these	
  
environmental	
  changes	
  and	
  may	
  be	
  unavoidable	
  in	
  the	
  future	
  considering	
  the	
  costly	
  and	
  complex	
  
operation	
  of	
  manual	
  metadata	
  creation.	
  To	
  address	
  such	
  needs,	
  this	
  study	
  examines	
  the	
  range	
  of	
  
semi-­‐automatic	
  metadata	
  generation	
  tools	
  (N	
  =	
  39)	
  while	
  providing	
  an	
  analysis	
  of	
  their	
  techniques,	
  
features,	
  and	
  functions.	
  The	
  study	
  focuses	
  on	
  open-­‐source	
  tools	
  that	
  can	
  be	
  readily	
  utilized	
  in	
  libraries	
  
and	
  other	
  memory	
  institutions.	
  The	
  challenges	
  and	
  current	
  barriers	
  to	
  implementation	
  of	
  these	
  tools	
  
were	
  identified.	
  The	
  greatest	
  area	
  of	
  difficulty	
  lies	
  in	
  the	
  fact	
  that	
  the	
  piecemeal	
  development	
  of	
  most	
  
semi-­‐automatic	
  generation	
  tools	
  only	
  addresses	
  part	
  of	
  the	
  issue	
  of	
  semi-­‐automatic	
  metadata	
  
generation,	
  providing	
  solutions	
  to	
  one	
  or	
  a	
  few	
  metadata	
  elements	
  but	
  not	
  the	
  full	
  range	
  of	
  elements.	
  
This	
  indicates	
  that	
  significant	
  local	
  efforts	
  will	
  be	
  required	
  to	
  integrate	
  the	
  various	
  tools	
  into	
  a	
  
coherent	
  set	
  of	
  a	
  working	
  whole.	
  Suggestions	
  toward	
  such	
  efforts	
  are	
  presented	
  for	
  future	
  
developments	
  that	
  may	
  assist	
  information	
  professionals	
  with	
  incorporation	
  of	
  semi-­‐automatic	
  tools	
  
within	
  their	
  daily	
  workflows.	
  	
  

INTRODUCTION	
  

With	
  the	
  rapid	
  increase	
  in	
  all	
  types	
  of	
  information	
  resources	
  managed	
  by	
  libraries	
  over	
  the	
  last	
  
few	
  decades,	
  the	
  ability	
  of	
  the	
  cataloging	
  and	
  metadata	
  community	
  to	
  describe	
  those	
  resources	
  has	
  
been	
  severely	
  strained.	
  Furthermore,	
  the	
  reality	
  of	
  stagnant	
  and	
  decreasing	
  library	
  budgets	
  has	
  
prevented	
  the	
  library	
  community	
  from	
  addressing	
  this	
  issue	
  with	
  concomitant	
  staffing	
  increases.	
  
Nevertheless,	
  the	
  ability	
  of	
  libraries	
  to	
  make	
  information	
  resources	
  accessible	
  to	
  their	
  
communities	
  of	
  users	
  remains	
  a	
  central	
  concern.	
  Thus	
  there	
  is	
  a	
  critical	
  need	
  to	
  devise	
  efficient	
  
and	
  cost	
  effective	
  ways	
  of	
  creating	
  bibliographic	
  records	
  so	
  that	
  users	
  are	
  able	
  to	
  find,	
  identify,	
  
and	
  obtain	
  the	
  information	
  resources	
  they	
  need.	
  	
  

One	
  promising	
  approach	
  to	
  managing	
  the	
  ever-­‐increasing	
  amount	
  of	
  information	
  is	
  with	
  semi-­‐
automatic	
  metadata	
  generation	
  tools.	
  Semi-­‐automatic	
  metadata	
  generation	
  tools	
  

	
  

Jung-­‐ran	
  Park	
  (jung-­‐ran.park@drexel.edu)	
  is	
  Editor,	
  Journal	
  of	
  Library	
  Metadata,	
  and	
  Associate	
  
Professor,	
  College	
  of	
  Computing	
  and	
  Informatics,	
  Drexel	
  University,	
  Philadelphia.	
  	
  
Andrew	
  Brenza	
  (apb84@drexel.edu)	
  is	
  Project	
  Assistant,	
  College	
  of	
  Computing	
  and	
  Informatics,	
  
Drexel	
  University,	
  Philadelphia.	
  



	
  

EVALUATION	
  OF	
  SEMI-­‐AUTOMATIC	
  METADATA	
  GENERATION	
  TOOLS|	
  PARK	
  AND	
  BRENZA	
  	
  
doi:	
  10.6017/ital.v34i3.5889	
  

23	
  

concern	
  the	
  use	
  of	
  software	
  to	
  create	
  metadata	
  records	
  with	
  varying	
  degrees	
  of	
  supervision	
  from	
  a	
  
human	
  specialist.1	
  In	
  its	
  ideal	
  form,	
  semi-­‐automatic	
  metadata	
  generation	
  tools	
  are	
  capable	
  of	
  
extracting	
  information	
  from	
  structured	
  and	
  unstructured	
  information	
  resources	
  of	
  all	
  types	
  and	
  
creating	
  quality	
  metadata	
  that	
  not	
  only	
  facilitate	
  bibliographic	
  record	
  creation	
  but	
  also	
  semantic	
  
interoperability,	
  a	
  critical	
  factor	
  for	
  resource	
  sharing	
  and	
  discovery	
  in	
  the	
  networked	
  environment.	
  
Through	
  the	
  use	
  of	
  semi-­‐automatic	
  metadata	
  generation	
  tools,	
  the	
  library	
  community	
  has	
  the	
  
potential	
  to	
  address	
  many	
  issues	
  related	
  to	
  the	
  increase	
  of	
  information	
  resources,	
  the	
  strain	
  on	
  
library	
  budget,	
  the	
  need	
  to	
  create	
  high-­‐quality,	
  interoperable	
  metadata	
  records,	
  and,	
  ultimately,	
  
the	
  effective	
  provision	
  of	
  information	
  resources	
  to	
  users.	
  

There	
  are	
  many	
  potential	
  benefits	
  to	
  semi-­‐automatic	
  metadata	
  generation.	
  The	
  first	
  is	
  scalability.	
  
Because	
  of	
  the	
  quantity	
  of	
  information	
  resources	
  and	
  the	
  costly	
  and	
  time-­‐consuming	
  nature	
  of	
  
manual	
  metadata	
  generation,2	
  it	
  is	
  increasingly	
  apparent	
  that	
  there	
  simply	
  are	
  not	
  enough	
  
information	
  professionals	
  available	
  for	
  satisfying	
  the	
  metadata-­‐generation	
  needs	
  of	
  the	
  library	
  
community.	
  Semi-­‐automatic	
  metadata	
  generation,	
  on	
  the	
  other	
  hand,	
  offers	
  the	
  promise	
  of	
  using	
  
high	
  levels	
  of	
  computing	
  power	
  to	
  manage	
  large	
  amounts	
  of	
  information	
  resources.	
  In	
  addition	
  to	
  
scalability,	
  semi-­‐automatic	
  metadata	
  generation	
  also	
  offers	
  potential	
  cost	
  savings	
  through	
  a	
  
decrease	
  in	
  the	
  time	
  required	
  to	
  create	
  effective	
  records.	
  Furthermore,	
  the	
  time	
  savings	
  would	
  
allow	
  information	
  professionals	
  to	
  focus	
  on	
  tasks	
  that	
  are	
  more	
  conceptually	
  demanding	
  and	
  thus	
  
not	
  suitable	
  for	
  automatic	
  generation.	
  Finally,	
  because	
  computers	
  can	
  perform	
  repetitive	
  tasks	
  
with	
  relative	
  consistency	
  when	
  compared	
  to	
  their	
  human	
  counterparts,	
  automatic	
  metadata	
  
generation	
  promises	
  the	
  ability	
  to	
  create	
  more	
  consistent	
  records.	
  A	
  potential	
  increase	
  in	
  
consistency	
  of	
  quality	
  metadata	
  records	
  would,	
  in	
  turn,	
  increase	
  the	
  potential	
  for	
  interoperability	
  
and	
  thereby	
  the	
  accessibility	
  of	
  information	
  resources	
  in	
  general.	
  Thus	
  semi-­‐automatic	
  metadata	
  
generation	
  offers	
  the	
  potential	
  to	
  not	
  only	
  ease	
  resource	
  description	
  demands	
  on	
  the	
  library	
  
community	
  but	
  also	
  to	
  improve	
  resource	
  discovery	
  for	
  its	
  users.	
  	
  

GOALS	
  OF	
  THE	
  STUDY	
  

Assessment	
  of	
  the	
  current	
  landscape	
  of	
  semi-­‐automatic	
  metadata	
  generation	
  tools	
  is	
  particularly	
  
important	
  considering	
  the	
  fast	
  development	
  of	
  digital	
  repositories	
  and	
  the	
  recent	
  explosion	
  of	
  
data	
  and	
  information.	
  Utilization	
  of	
  semi-­‐automatic	
  metadata	
  generation	
  is	
  critical	
  to	
  address	
  such	
  
environmental	
  changes	
  and	
  may	
  be	
  unavoidable	
  in	
  the	
  future	
  considering	
  the	
  costly	
  and	
  complex	
  
operation	
  of	
  manual	
  metadata	
  creation.	
  Even	
  though	
  there	
  are	
  promising	
  experimental	
  studies	
  
that	
  exploit	
  various	
  methods	
  and	
  sources	
  for	
  semi-­‐automatic	
  metadata	
  generation,3	
  a	
  lack	
  of	
  
studies	
  assessing	
  and	
  evaluating	
  the	
  range	
  of	
  tools	
  have	
  been	
  developed,	
  implemented,	
  or	
  
improved.	
  To	
  address	
  such	
  needs,	
  this	
  study	
  aims	
  to	
  examine	
  the	
  current	
  landscape	
  of	
  semi-­‐
automatic	
  metadata	
  generation	
  tools	
  while	
  providing	
  an	
  evaluative	
  analysis	
  of	
  their	
  techniques,	
  
features,	
  and	
  functions.	
  The	
  study	
  primarily	
  focuses	
  on	
  open-­‐source	
  tools	
  that	
  can	
  be	
  readily	
  
utilized	
  in	
  libraries	
  and	
  other	
  memory	
  institutions.	
  The	
  study	
  also	
  highlights	
  some	
  of	
  the	
  
challenges	
  still	
  facing	
  the	
  continued	
  development	
  of	
  semi-­‐automatic	
  tools	
  and	
  the	
  current	
  barriers	
  



	
  

INFORMATION	
  TECHNOLOGY	
  AND	
  LIBRARIES	
  |	
  SEPTEMBER	
  2015	
  	
  

	
  

24	
  

to	
  their	
  incorporation	
  into	
  the	
  daily	
  workflows	
  for	
  information	
  organization	
  and	
  management.	
  
Future	
  directions	
  for	
  the	
  further	
  development	
  of	
  tools	
  are	
  also	
  discussed.	
  	
  

Toward	
  this	
  end,	
  a	
  critical	
  review	
  of	
  the	
  literature	
  in	
  relation	
  to	
  semi-­‐automatic	
  metadata	
  
generation	
  tools	
  published	
  from	
  2004	
  to	
  2014	
  was	
  conducted.	
  Databases	
  such	
  as	
  Library	
  and	
  
Information	
  Sciences	
  Abstracts	
  and	
  Library,	
  Information	
  Science	
  and	
  Technology	
  Abstracts	
  were	
  
searched	
  and	
  germane	
  articles	
  identified	
  through	
  review	
  of	
  titles	
  and	
  abstracts.	
  Because	
  the	
  
problem	
  of	
  creating	
  viable	
  tools	
  for	
  the	
  reliable	
  automatic	
  generation	
  of	
  metadata	
  is	
  a	
  not	
  a	
  
problem	
  limited	
  to	
  the	
  library	
  and	
  information	
  science	
  professions,4	
  database	
  searches	
  were	
  
expanded	
  to	
  include	
  those	
  databases	
  pertinent	
  to	
  the	
  computing	
  science,	
  including	
  Proquest	
  
Computing,	
  Academic	
  Search	
  Premier,	
  and	
  Applied	
  Science	
  and	
  Technology.	
  Keywords,	
  such	
  as	
  
“automatic	
  metadata	
  generation,”	
  “metadata	
  extraction,”	
  “metadata	
  tools,”	
  and	
  “text	
  mining,”	
  
including	
  their	
  stems,	
  were	
  used	
  to	
  explore	
  the	
  databases.	
  In	
  addition	
  to	
  keyword	
  searching,	
  
relevant	
  articles	
  were	
  also	
  identified	
  within	
  the	
  reference	
  sections	
  of	
  articles	
  already	
  deemed	
  
pertinent	
  to	
  the	
  focus	
  of	
  the	
  survey	
  as	
  well	
  as	
  through	
  the	
  expansion	
  of	
  results	
  lists	
  through	
  the	
  
application	
  of	
  relevant	
  subject	
  terms	
  applied	
  to	
  pertinent	
  articles.	
  To	
  ensure	
  that	
  the	
  latest,	
  most	
  
reliable	
  developments	
  in	
  automatic	
  metadata	
  were	
  reviewed,	
  various	
  filters,	
  such	
  as	
  date	
  range	
  
and	
  peer-­‐review,	
  were	
  employed.	
  Once	
  tools	
  were	
  identified,	
  their	
  capabilities	
  were	
  tested	
  (when	
  
possible),	
  their	
  features	
  were	
  noted,	
  and	
  overarching	
  developments	
  were	
  determined.	
  	
  

The	
  remainder	
  of	
  the	
  article	
  provides	
  an	
  overview	
  of	
  the	
  primary	
  techniques	
  developed	
  for	
  the	
  
semi-­‐automatic	
  generation	
  of	
  metadata	
  and	
  a	
  review	
  of	
  the	
  open-­‐source	
  metadata	
  generation	
  
tools	
  that	
  employ	
  them.	
  The	
  challenges	
  and	
  current	
  barriers	
  to	
  semi-­‐automatic	
  metadata	
  tool	
  
implementation	
  are	
  described	
  as	
  well	
  as	
  suggestions	
  for	
  future	
  developments	
  that	
  may	
  assist	
  
information	
  professionals	
  with	
  integration	
  of	
  semi-­‐automatic	
  tools	
  within	
  the	
  daily	
  workflow	
  of	
  
technical	
  services	
  departments.	
  	
  

Current	
  Techniques	
  for	
  the	
  Automatic	
  Generation	
  of	
  Metadata	
  

As	
  opposed	
  to	
  manual	
  metadata	
  generation,	
  semi-­‐automatic	
  metadata	
  generation	
  relies	
  on	
  
machine	
  methods	
  to	
  assist	
  with	
  or	
  to	
  complete	
  the	
  metadata-­‐creation	
  process.	
  Greenberg	
  
distinguished	
  between	
  two	
  methods	
  of	
  automatic	
  metadata	
  generation:	
  metadata	
  extraction	
  and	
  
metadata	
  harvesting.5	
  Metadata	
  extraction	
  in	
  general	
  employs	
  automatic	
  indexing	
  and	
  
information	
  retrieval	
  techniques	
  to	
  generate	
  structured	
  metadata	
  using	
  the	
  original	
  content	
  of	
  
resources.	
  On	
  the	
  other	
  hand,	
  metadata	
  harvesting	
  concerns	
  a	
  technique	
  to	
  automatically	
  gather	
  
metadata	
  from	
  individual	
  repositories	
  in	
  which	
  metadata	
  has	
  been	
  produced	
  by	
  semi-­‐automatic	
  or	
  
manual	
  approaches.	
  The	
  harvested	
  metadata	
  can	
  be	
  stored	
  in	
  a	
  central	
  repository	
  for	
  future	
  
resource	
  retrieval.	
  

Within	
  this	
  dichotomy	
  of	
  extraction	
  methods,	
  there	
  are	
  several	
  other	
  more	
  specific	
  techniques	
  
that	
  researchers	
  have	
  developed	
  for	
  the	
  semi-­‐automatic	
  generation	
  of	
  metadata.	
  Polfreman	
  et	
  al.	
  
identified	
  an	
  additional	
  six	
  techniques	
  that	
  have	
  been	
  developed	
  over	
  the	
  years:	
  meta-­‐tag	
  
harvesting,	
  content	
  extraction,	
  automatic	
  indexing,	
  text	
  and	
  data	
  mining,	
  extrinsic	
  data	
  auto	
  



	
  

EVALUATION	
  OF	
  SEMI-­‐AUTOMATIC	
  METADATA	
  GENERATION	
  TOOLS|	
  PARK	
  AND	
  BRENZA	
  	
  
doi:	
  10.6017/ital.v34i3.5889	
  

25	
  

generation,	
  and	
  social	
  tagging.6	
  Although	
  the	
  last	
  technique	
  is	
  not	
  properly	
  a	
  semi-­‐automatic	
  
metadata	
  generation	
  technique	
  because	
  it	
  is	
  used	
  to	
  generate	
  metadata	
  with	
  a	
  minimum	
  of	
  
intervention	
  required	
  by	
  metadata	
  professionals,	
  it	
  can	
  be	
  viewed	
  as	
  a	
  possible	
  mode	
  to	
  
streamline	
  the	
  metadata	
  creation	
  process.	
  	
  

Both	
  Greenberg	
  and	
  Polfreman	
  provide	
  comprehensive,	
  high-­‐level	
  characterizations	
  of	
  the	
  
techniques	
  employed	
  in	
  current	
  semi-­‐automatic	
  metadata	
  generation	
  tools.	
  However,	
  an	
  
evaluation	
  of	
  these	
  techniques	
  within	
  the	
  context	
  of	
  a	
  broad	
  survey	
  of	
  the	
  tools	
  themselves	
  and	
  a	
  
comprehensive	
  enumeration	
  of	
  currently	
  available	
  tools	
  are	
  not	
  addressed.	
  Thus,	
  although	
  these	
  
techniques	
  will	
  be	
  examined	
  for	
  the	
  remainder	
  of	
  this	
  section,	
  they	
  serve	
  simply	
  as	
  a	
  framework	
  
through	
  which	
  this	
  study	
  provides	
  a	
  current	
  and	
  comprehensive	
  analysis	
  of	
  the	
  tools	
  available	
  for	
  
use	
  today.	
  Each	
  section	
  provides	
  an	
  overview	
  of	
  the	
  relevant	
  technique,	
  a	
  discussion	
  of	
  the	
  most	
  
current	
  research	
  related	
  to	
  it,	
  and	
  the	
  tools	
  that	
  employ	
  that	
  technique.	
  

The	
  tables	
  included	
  in	
  each	
  section	
  provide	
  lists	
  of	
  the	
  semi-­‐automatic	
  metadata	
  generation	
  tools	
  
(N	
  =	
  39)	
  evaluated	
  in	
  the	
  course	
  of	
  this	
  survey.	
  The	
  information	
  presented	
  in	
  the	
  tables	
  is	
  
designed	
  to	
  provide	
  a	
  characterization	
  of	
  each	
  tool:	
  its	
  name,	
  its	
  online	
  location,	
  the	
  technique(s)	
  
used	
  to	
  generate	
  metadata,	
  and	
  a	
  brief	
  description	
  of	
  the	
  tool’s	
  functions	
  and	
  features.	
  Only	
  those	
  
tools	
  that	
  are	
  currently	
  available	
  for	
  download	
  or	
  for	
  use	
  as	
  web	
  services	
  at	
  the	
  time	
  of	
  this	
  
writing	
  are	
  included.	
  Furthermore,	
  the	
  listed	
  tools	
  have	
  not	
  been	
  strictly	
  limited	
  to	
  metadata-­‐
generation	
  applications	
  but	
  also	
  include	
  some	
  content	
  management	
  system	
  software	
  (CMSS)	
  as	
  
these	
  generally	
  provide	
  some	
  form	
  of	
  semi-­‐automatic	
  metadata	
  extraction.	
  Typically,	
  CMSS	
  are	
  
capable	
  of	
  extracting	
  technical	
  metadata	
  as	
  well	
  as	
  data	
  that	
  can	
  found	
  in	
  the	
  meta-­‐tags	
  of	
  
information	
  resources,	
  such	
  as	
  the	
  file	
  name,	
  and	
  using	
  that	
  information	
  as	
  the	
  title	
  of	
  a	
  record.	
  	
  

Meta-­‐Tag	
  Extraction	
  

Meta-­‐tag	
  extraction	
  is	
  a	
  computing	
  process	
  whereby	
  values	
  for	
  metadata	
  fields	
  are	
  identified	
  and	
  
populated	
  through	
  an	
  examination	
  of	
  metadata	
  tags	
  within	
  or	
  attached	
  to	
  a	
  document.	
  In	
  other	
  
words,	
  it	
  is	
  a	
  form	
  of	
  metadata	
  harvesting	
  and,	
  possibly,	
  conversion	
  of	
  that	
  metadata	
  into	
  other	
  
formats.	
  MarcEdit,	
  the	
  most	
  widely	
  used	
  semi-­‐automatic	
  tool	
  for	
  the	
  generation	
  of	
  metadata	
  in	
  US	
  
libraries,7	
  is	
  an	
  example	
  of	
  this	
  technique.	
  MarcEdit	
  essentially	
  harvests	
  metadata	
  from	
  Open	
  
Archives	
  Initiative	
  Protocol	
  for	
  Metadata	
  Harvesting	
  (OAI-­‐PMH)	
  compliant	
  records	
  and	
  offers	
  the	
  
user	
  the	
  opportunity	
  to	
  convert	
  those	
  records	
  to	
  a	
  variety	
  of	
  formats,	
  including	
  MAchine-­‐Readable	
  
Cataloging	
  (MARC),	
  MAchine-­‐Readable	
  Cataloguing	
  in	
  XML	
  (MARC	
  XML),	
  Metadata	
  Object	
  
Description	
  Schema	
  (MODS),	
  and	
  Encoded	
  Archival	
  Description	
  (EAD).	
  It	
  also	
  offers	
  the	
  
capabilities	
  of	
  converting	
  records	
  from	
  any	
  of	
  the	
  supported	
  formats	
  to	
  any	
  of	
  the	
  other	
  supported	
  
formats.	
  

Other	
  examples	
  of	
  this	
  technique	
  are	
  the	
  web	
  services	
  Editor-­‐Converter	
  Dublin	
  Core	
  Metadata	
  and	
  
Firefox	
  Dublin	
  Core	
  Viewer	
  Extension.	
  Both	
  of	
  these	
  programs	
  search	
  HTML	
  files	
  on	
  the	
  web	
  and	
  
convert	
  information	
  found	
  in	
  HTML	
  meta-­‐tags	
  to	
  Dublin	
  Core	
  elements.	
  In	
  the	
  cases	
  of	
  MarcEdit	
  



	
  

INFORMATION	
  TECHNOLOGY	
  AND	
  LIBRARIES	
  |	
  SEPTEMBER	
  2015	
  	
  

	
  

26	
  

and	
  Editor-­‐Converter	
  Dublin	
  Core,	
  users	
  are	
  presented	
  with	
  the	
  converted	
  information	
  in	
  an	
  
interface	
  that	
  allows	
  the	
  user	
  to	
  edit	
  or	
  refine	
  the	
  data.	
  	
  

Figure	
  1	
  provides	
  an	
  illustration	
  of	
  the	
  extracted	
  metadata	
  of	
  the	
  New	
  York	
  Times	
  homepage	
  using	
  
Editor-­‐Converter	
  Dublin	
  Core,	
  while	
  figure	
  2	
  offers	
  an	
  illustration	
  of	
  the	
  editor	
  that	
  this	
  web	
  
service	
  provides.	
  	
  

	
  	
  

Figure	
  1.	
  Screenshot	
  of	
  Extracted	
  Dublin	
  Core	
  Metadata	
  Using	
  Editor-­‐Converter	
  Dublin	
  Core.	
  

	
  

Figure	
  2.	
  Screenshot	
  of	
  Editor-­‐Converter	
  Dublin	
  Core	
  Editing	
  Tool	
  (only	
  eight	
  of	
  the	
  sixteen	
  fields	
  
are	
  visible	
  in	
  this	
  screenshot).	
  



	
  

EVALUATION	
  OF	
  SEMI-­‐AUTOMATIC	
  METADATA	
  GENERATION	
  TOOLS|	
  PARK	
  AND	
  BRENZA	
  	
  
doi:	
  10.6017/ital.v34i3.5889	
  

27	
  

Perhaps	
  the	
  biggest	
  weakness	
  to	
  this	
  type	
  of	
  tool	
  is	
  that	
  it	
  entirely	
  depends	
  on	
  the	
  quality	
  of	
  the	
  
metadata	
  from	
  which	
  the	
  programs	
  harvest.	
  This	
  can	
  be	
  most	
  readily	
  seen	
  in	
  the	
  above	
  figure	
  by	
  
the	
  lack	
  of	
  values	
  for	
  a	
  number	
  of	
  the	
  Dublin	
  Core	
  fields	
  for	
  the	
  The	
  New	
  York	
  Times	
  website.	
  
Programs	
  that	
  solely	
  employ	
  the	
  technique	
  of	
  meta-­‐tag	
  harvesting	
  are	
  unable	
  to	
  infer	
  values	
  for	
  
metadata	
  elements	
  that	
  are	
  not	
  already	
  populated	
  in	
  the	
  source.	
  	
  

Table	
  1	
  lists	
  the	
  tools	
  that	
  support	
  meta-­‐tag	
  harvesting	
  either	
  as	
  the	
  sole	
  technique	
  or	
  as	
  one	
  of	
  a	
  
suite	
  of	
  techniques	
  used	
  to	
  generate	
  metadata	
  from	
  resources.	
  Of	
  the	
  thirty-­‐nine	
  tools	
  evaluated	
  
for	
  this	
  study,	
  nineteen	
  support	
  meta-­‐tag	
  harvesting.	
  

Tool	
  Name	
   Location	
   Techniques	
   Functions/Features	
  

ANVL/ERC	
  
Kernel	
  Metadata	
  
Conversion	
  
Toolkit	
  

http://search.cpan.org/~jak/File-­‐
ANVL/anvl	
  	
  

meta-­‐tag	
  harvester	
  

A	
  utility	
  that	
  can	
  automatically	
  
convert	
  records	
  in	
  the	
  ANVL	
  
format	
  into	
  other	
  formats	
  such	
  as	
  
XML,	
  JSON	
  (JavaScript	
  Object	
  
Notation),	
  Turtle	
  or	
  Plain,	
  among	
  
others.	
  

Apache	
  POI	
  –	
  
Text	
  Extractor	
  

http://poi.apache.org/download.html	
  	
  

content	
  extractor;	
  
meta-­‐tag	
  harvester;	
  
extrinsic	
  auto-­‐
generator	
  

Apache	
  POI	
  provides	
  basic	
  text	
  
extraction	
  for	
  all	
  project	
  
supported	
  file	
  formats.	
  In	
  
addition	
  to	
  the	
  (plain)	
  text,	
  
Apache	
  POI	
  can	
  access	
  the	
  
metadata	
  associated	
  with	
  a	
  given	
  
file,	
  such	
  as	
  title	
  and	
  author.	
  	
  

Apache	
  Tika	
   http://tika.apache.org/	
  	
  

content	
  extractor;	
  
meta-­‐tag	
  harvester;	
  
extrinsic	
  auto-­‐
generator	
  

Built	
  on	
  Apache	
  POI,	
  the	
  Apache	
  
Tika	
  toolkit	
  detects	
  and	
  extracts	
  
metadata	
  and	
  text	
  content	
  from	
  
various	
  documents.	
  

Ariadne	
  
Harvester	
  

http://sourceforge.net/projects/ariadn
ekps/files/?source=navbar	
  	
  

meta-­‐tag	
  harvester	
  

A	
  harvester	
  of	
  OAI-­‐PMH	
  
compliant	
  records	
  which	
  can	
  be	
  
converted	
  to	
  various	
  other	
  
schema	
  such	
  as	
  Learning	
  Object	
  
Metadata	
  (LOM).	
  	
  	
  

BIBFRAME	
  Tools	
  
http://www.loc.gov/bibframe/implem
entation/	
  	
  

meta-­‐tag	
  harvester	
  

BIBFRAME	
  offers	
  a	
  number	
  of	
  
tools	
  for	
  the	
  conversion	
  of	
  
MARCXML	
  documents	
  to	
  
BIBFRAME	
  documents.	
  	
  Web	
  
service	
  and	
  downloadable	
  
software	
  are	
  both	
  available.	
  

Data	
  Fountains	
   http://datafountains.ucr.edu/	
  	
  

content	
  extractor;	
  
automatic	
  indexer;	
  
meta-­‐tag	
  harvester;	
  
extrinsic	
  auto-­‐
generator	
  

Scans	
  HTML	
  documents	
  and	
  first	
  
extracts	
  information	
  contained	
  in	
  
meta-­‐tags.	
  	
  If	
  information	
  is	
  
unavailable	
  in	
  meta-­‐tags,	
  the	
  
program	
  will	
  use	
  other	
  
techniques	
  to	
  assign	
  values.	
  	
  
Includes	
  a	
  focused	
  web	
  crawler	
  
that	
  can	
  target	
  websites	
  
concerning	
  a	
  specific	
  subject.	
  	
  	
  	
  



	
  

INFORMATION	
  TECHNOLOGY	
  AND	
  LIBRARIES	
  |	
  SEPTEMBER	
  2015	
  	
  

	
  

28	
  

Dublin	
  Core	
  Meta	
  
Toolkit	
  

http://sourceforge.net/projects/dcmet
atoolkit/files/?source=navbar	
  	
  

meta-­‐tag	
  harvester	
  
Transforms	
  data	
  collected	
  via	
  
different	
  methods	
  into	
  Dublin	
  
Core	
  (DC)	
  compatible	
  metadata.	
  

Dspace	
   http://www.dspace.org/	
  	
  

meta-­‐tag	
  harvester;	
  
extrinsic	
  auto-­‐
generator;	
  social	
  
tagging	
  

Automatically	
  extracts	
  technical	
  
information	
  regarding	
  file	
  format	
  
and	
  size.	
  	
  Can	
  also	
  extract	
  some	
  
information	
  from	
  meta-­‐tags.	
  

Editor-­‐Converter	
  
Dublin	
  Core	
  
Metadata	
  

http://www.library.kr.ua/dc/dcedituni
e.html	
  

meta-­‐tag	
  harvester;	
  
extrinsic	
  auto-­‐
generator	
  

Scans	
  HTML	
  documents,	
  
harvesting	
  metadata	
  from	
  tags	
  
and	
  converting	
  them	
  to	
  DC.	
  

Embedded	
  
Metadata	
  
Extraction	
  Tool	
  
(EMET)	
  

http://www.artstor.org/global/g-­‐
html/download-­‐emet-­‐public.html	
  	
  

content	
  extractor;	
  
EMET	
  is	
  a	
  tool	
  designed	
  to	
  
extract	
  metadata	
  embedded	
  in	
  
JPEG	
  and	
  TIFF	
  files.	
  

meta-­‐tag	
  harvester;	
  
extrinsic	
  auto-­‐
generator	
  

Firefox	
  Dublin	
  
Core	
  Viewer	
  
Extension	
  

http://www.splintered.co.uk/experime
nts/73/	
  	
  

meta-­‐tag	
  harvester;	
  
extrinsic	
  auto-­‐
generator	
  

Scans	
  HTML	
  documents,	
  
harvesting	
  metadata	
  from	
  tags	
  
and	
  displaying	
  them	
  in	
  Dublin	
  
Core.	
  

MarcEdit	
   http://marcedit.reeset.net/	
   meta-­‐tag	
  harvester	
  
Harvests	
  OAI-­‐PMH	
  compliant	
  
data	
  and	
  converts	
  it	
  to	
  various	
  
formats	
  including	
  DC	
  and	
  MARC.	
  

Metatag	
  
Extractor	
  
Software	
  

http://meta-­‐tag-­‐
extractor.software.informer.com/	
  	
  

meta-­‐tag	
  harvester	
  

Permits	
  customizable	
  extraction	
  
features,	
  harvesting	
  meta-­‐tags	
  as	
  
well	
  as	
  contact	
  information	
  from	
  
websites.	
  

My	
  Meta	
  Maker	
  
http://old.isn-­‐
oldenburg.de/services/mmm/	
  	
  

meta-­‐tag	
  harvester	
   Can	
  convert	
  manually	
  entered	
  
data	
  into	
  DC.	
  

Photo	
  RDF-­‐Gen	
  
http://www.webposible.com/utilidade
s/photo_rdf_generator_en.html	
  

meta-­‐tag	
  harvester	
  

Generates	
  Dublin	
  Core	
  and	
  
Resource	
  Description	
  Framework	
  
(RDF)	
  output	
  from	
  manually	
  
entered	
  input.	
  

PyMarc	
   https://github.com/edsu/pymarc	
  	
   meta-­‐tag	
  harvester	
  
Scripting	
  tool	
  in	
  Python	
  language	
  
for	
  the	
  batch	
  processing	
  of	
  MARC	
  
records,	
  similar	
  to	
  MarcEdit.	
  	
  	
  

RepoMMan	
  
http://www.hull.ac.uk/esig/repomman
/index.html	
  

meta-­‐tag	
  harvester;	
  
content	
  extractor;	
  
extrinsic	
  auto-­‐
generator	
  

Automatically	
  extracts	
  various	
  
elements	
  for	
  documents	
  
uploaded	
  to	
  Fedora	
  such	
  as	
  
author,	
  title,	
  description,	
  and	
  key	
  
words,	
  among	
  others.	
  	
  Results	
  are	
  
presented	
  to	
  user	
  for	
  review.	
  

SHERPA/RoMEO	
  
http://www.sherpa.ac.uk/romeo/api.h
tml	
  	
  

meta-­‐tag	
  harvester	
  

A	
  machine-­‐to-­‐machine	
  
Application	
  Program	
  Interface	
  
(API)	
  that	
  permits	
  the	
  automatic	
  
look-­‐up	
  and	
  importation	
  of	
  
publishers	
  and	
  journals.	
  

URL	
  and	
  Metatag	
  
Extractor	
  

http://www.metatagextractor.com/	
  	
   meta-­‐tag	
  harvester	
  
Permits	
  the	
  targeted	
  searching	
  of	
  
websites	
  and	
  extracts	
  URLs	
  and	
  
meta-­‐tags	
  from	
  those	
  sites.	
  

Table	
  1.	
  Semi-­‐Automatic	
  Tools	
  that	
  Support	
  Meta-­‐Tag	
  Harvesting.	
  



	
  

EVALUATION	
  OF	
  SEMI-­‐AUTOMATIC	
  METADATA	
  GENERATION	
  TOOLS|	
  PARK	
  AND	
  BRENZA	
  	
  
doi:	
  10.6017/ital.v34i3.5889	
  

29	
  

Content	
  Extraction	
  	
  

Content	
  extraction	
  is	
  a	
  form	
  of	
  metadata	
  extraction	
  whereby	
  various	
  computing	
  techniques	
  are	
  
used	
  to	
  extract	
  information	
  from	
  the	
  information	
  resource	
  itself.	
  In	
  other	
  words,	
  these	
  techniques	
  
do	
  not	
  rely	
  on	
  the	
  identification	
  of	
  relevant	
  meta-­‐tags	
  for	
  the	
  population	
  of	
  metadata	
  values.	
  An	
  
example	
  of	
  this	
  technique	
  is	
  the	
  Kea	
  application,	
  a	
  program	
  developed	
  at	
  the	
  New	
  Zealand	
  Digital	
  
Library	
  that	
  uses	
  machine	
  learning,	
  term	
  frequency-­‐inverse	
  document	
  frequency	
  (TF.IDF)	
  and	
  
first-­‐occurrence	
  techniques	
  to	
  identify	
  and	
  assign	
  key	
  phrases	
  from	
  the	
  full	
  text	
  of	
  documents.8	
  
The	
  major	
  advantage	
  of	
  this	
  type	
  of	
  technique	
  is	
  that	
  the	
  extraction	
  of	
  metadata	
  can	
  be	
  done	
  
independently	
  of	
  the	
  quality	
  of	
  metadata	
  associated	
  with	
  any	
  given	
  information	
  resource.	
  Another	
  
example	
  of	
  a	
  tool	
  utilizing	
  this	
  technique	
  is	
  the	
  Open	
  Text	
  Summarizer,	
  an	
  open-­‐source	
  program	
  
that	
  offers	
  the	
  capability	
  of	
  reading	
  a	
  text	
  and	
  extracting	
  important	
  sentences	
  to	
  create	
  a	
  summary	
  
as	
  well	
  as	
  to	
  assign	
  keywords.	
  Figure	
  3	
  provides	
  a	
  screenshot	
  of	
  what	
  a	
  summarized	
  text	
  might	
  
look	
  like	
  using	
  the	
  Open	
  Text	
  Summarizer.	
  

	
  

	
  	
   	
   Figure	
  3.	
  Open	
  Text	
  Summarizer:	
  Sample	
  Summary	
  of	
  Text.	
  

Another	
  form	
  of	
  this	
  technique	
  often	
  relies	
  on	
  the	
  predictable	
  structure	
  of	
  certain	
  types	
  of	
  
documents	
  to	
  identify	
  candidate	
  values	
  for	
  metadata	
  elements.	
  For	
  instance,	
  because	
  of	
  the	
  
reliable	
  format	
  of	
  scholarly	
  research	
  papers—which	
  generally	
  include	
  a	
  title,	
  author,	
  abstract,	
  
introduction,	
  conclusion,	
  and	
  reference	
  sections	
  in	
  predictable	
  ways—this	
  format	
  can	
  be	
  exploited	
  
by	
  machines	
  to	
  extract	
  metadata	
  values	
  from	
  them.	
  Several	
  projects	
  have	
  been	
  able	
  to	
  exploit	
  this	
  
technique	
  in	
  combination	
  with	
  machine	
  learning	
  algorithms	
  to	
  extract	
  various	
  forms	
  of	
  metadata.	
  	
  

For	
  instance,	
  in	
  the	
  Randkte	
  project,	
  optical	
  character	
  recognition	
  software	
  was	
  used	
  to	
  scan	
  a	
  
large	
  quantity	
  of	
  legal	
  documents	
  from	
  which,	
  because	
  of	
  the	
  regularity	
  of	
  the	
  documents’	
  



	
  

INFORMATION	
  TECHNOLOGY	
  AND	
  LIBRARIES	
  |	
  SEPTEMBER	
  2015	
  	
  

	
  

30	
  

structure,	
  structural	
  metadata	
  such	
  as	
  chapter,	
  section,	
  and	
  page	
  number	
  could	
  be	
  extracted.9	
  In	
  
contrast,	
  the	
  Kovacevic’s	
  project	
  used	
  the	
  predictable	
  structure	
  of	
  scholarly	
  articles,	
  converting	
  
documents	
  from	
  PDF	
  to	
  HTML	
  files	
  while	
  preserving	
  the	
  formatting	
  details	
  and	
  used	
  classification	
  
algorithms	
  to	
  extract	
  metadata	
  regarding	
  title,	
  author,	
  abstract,	
  and	
  keywords,	
  among	
  other	
  
elements.10	
  

Table	
  2	
  lists	
  the	
  tools	
  that	
  support	
  content	
  extraction	
  either	
  as	
  the	
  sole	
  technique	
  or	
  as	
  one	
  of	
  a	
  
suite	
  of	
  techniques	
  used	
  to	
  generate	
  metadata	
  from	
  resources.	
  Of	
  the	
  thirty-­‐nine	
  tools	
  evaluated	
  
for	
  this	
  study,	
  twenty	
  tools	
  support	
  some	
  form	
  of	
  content	
  extraction.	
  

Tool	
  Name	
   Location	
   Techniques	
   Functions/Features	
  

Apache	
  POI—
Text	
  Extractor	
  

http://poi.apache.org/download.html	
  	
  

content	
  extractor;	
  
meta-­‐tag	
  harvester;	
  
extrinsic	
  auto-­‐
generator	
  

Apache	
  POI	
  provides	
  basic	
  text	
  
extraction	
  for	
  all	
  project	
  
supported	
  file	
  formats.	
  In	
  
addition	
  to	
  the	
  (plain)	
  text,	
  
Apache	
  POI	
  can	
  access	
  the	
  
metadata	
  associated	
  with	
  a	
  given	
  
file,	
  such	
  as	
  title	
  and	
  author.	
  	
  

Apache	
  
Standol	
  

https://stanbol.apache.org/	
  	
  
content	
  extractor;	
  
automatic	
  indexer	
  

Extracts	
  semantic	
  metadata	
  from	
  
PDF	
  and	
  text	
  files.	
  Can	
  apply	
  
extracted	
  terms	
  to	
  ontologies.	
  

Apache	
  Tika	
   http://tika.apache.org/	
  	
  

content	
  extractor;	
  
meta-­‐tag	
  harvester;	
  
extrinsic	
  auto-­‐
generator	
  

Built	
  on	
  Apache	
  POI,	
  the	
  Apache	
  
Tika	
  toolkit	
  detects	
  and	
  extracts	
  
metadata	
  and	
  text	
  content	
  from	
  
various	
  documents.	
  

Biblio	
  Citation	
  
Parser	
  

http://search.cpan.org/~mjewell/	
  
Biblio-­‐Citation-­‐Parser-­‐1.10/	
  	
  

content	
  extractor	
  
A	
  set	
  of	
  modules	
  for	
  citation	
  
parsing.	
  

CatMDEdit	
   http://catmdedit.sourceforge.net/	
  	
   content	
  extractor	
  

CatMDEdit	
  allows	
  the	
  automatic	
  
creation	
  of	
  metadata	
  for	
  
collections	
  of	
  related	
  resources,	
  
in	
  particular	
  spatial	
  series	
  that	
  
arise	
  as	
  a	
  result	
  of	
  the	
  
fragmentation	
  of	
  geometric	
  
resources	
  into	
  datasets	
  of	
  
manageable	
  size	
  and	
  similar	
  
scale.	
  

CrossRef	
  
http://www.crossref.org/	
  
SimpleTextQuery/	
  	
  

content	
  extractor	
  
This	
  web	
  service	
  returns	
  Digital	
  
Object	
  Identifiers	
  for	
  inputted	
  
references.	
  	
  

Data	
  
Fountains	
  

http://datafountains.ucr.edu/	
  	
  

content	
  extractor;	
  
automatic	
  indexer;	
  
meta-­‐tag	
  harvester;	
  
extrinsic	
  auto-­‐
generator	
  

Scans	
  HTML	
  documents	
  and	
  first	
  
extracts	
  information	
  contained	
  in	
  
meta-­‐tags.	
  If	
  information	
  is	
  
unavailable	
  in	
  meta-­‐tags,	
  the	
  
program	
  will	
  use	
  other	
  
techniques	
  to	
  assign	
  values.	
  
Includes	
  a	
  focused	
  web	
  crawler	
  
that	
  can	
  target	
  websites	
  
concerning	
  a	
  specific	
  subject.	
  	
  



	
  

EVALUATION	
  OF	
  SEMI-­‐AUTOMATIC	
  METADATA	
  GENERATION	
  TOOLS|	
  PARK	
  AND	
  BRENZA	
  	
  
doi:	
  10.6017/ital.v34i3.5889	
  

31	
  

Embedded	
  
Metadata	
  
Extraction	
  
Tool	
  (EMET)	
  

http://www.artstor.org/global/g	
  
-­‐html/download-­‐emet-­‐public.html	
  	
  

content	
  extractor;	
  
meta-­‐tag	
  harvester;	
  
extrinsic	
  auto-­‐
generator	
  

EMET	
  is	
  a	
  tool	
  designed	
  to	
  extract	
  
metadata	
  embedded	
  in	
  JPEG	
  and	
  
TIFF	
  files.	
  

FreeCite	
   http://freecite.library.brown.edu/	
  	
   content	
  extractor	
  

Free	
  parsing	
  tool	
  for	
  the	
  
extraction	
  of	
  reference	
  
information.	
  Can	
  be	
  downloaded	
  
or	
  used	
  as	
  a	
  web	
  service.	
  	
  

General	
  
Architecture	
  
for	
  Text	
  
Engineering	
  
(GATE)	
  

http://gate.ac.uk/overview.html	
  	
  
content	
  extractor;	
  
automatic	
  indexer;	
  

Natural	
  language	
  processor	
  and	
  
information	
  extractor.	
  

Kea	
  	
  
http://www.nzdl.org/Kea/index_old	
  
.html#download	
  

content	
  extractor;	
  
automatic	
  indexer	
  

Analyzes	
  the	
  full	
  texts	
  of	
  
resources	
  and	
  extracts	
  
keyphrases.	
  Keyphrases	
  can	
  also	
  
be	
  mapped	
  to	
  customized	
  
ontologies	
  or	
  controlled	
  
vocabularies	
  for	
  subject	
  term	
  
assignment.	
  

MetaGen	
  
http://www.codeproject.com/Articles
/41910/MetaGen-­‐A-­‐project	
  
-­‐metadata-­‐generator-­‐for-­‐Visual-­‐St	
  	
  

content	
  extractor;	
  
automatic	
  indexer	
  

Used	
  to	
  build	
  a	
  metadata	
  
generator	
  for	
  Silverlight	
  and	
  
Desktop	
  CLR	
  projects,	
  MetaGen	
  
can	
  be	
  used	
  as	
  a	
  replacement	
  for	
  
static	
  reflection	
  (expression	
  
trees),	
  reflection	
  (walking	
  the	
  
stack),	
  and	
  various	
  other	
  means	
  
for	
  deriving	
  the	
  name	
  of	
  a	
  
property,	
  method,	
  or	
  field.	
  	
  

MetaGenerator	
  
http://extensions.joomla.org/	
  
extensions/site-­‐management/seo-­‐a	
  
-­‐metadata/meta-­‐data/11038	
  

content	
  extractor	
  

A	
  plugin	
  that	
  automatically	
  
generates	
  description	
  and	
  
keyword	
  meta-­‐tags	
  by	
  pulling	
  
text	
  from	
  joomla	
  content.	
  With	
  
this	
  plugin	
  you	
  can	
  also	
  control	
  
some	
  title	
  options	
  and	
  add	
  URL	
  
meta-­‐tags.	
  	
  

Ont-­‐O-­‐Mat	
  
http://projects.semwebcentral.org/	
  
projects/ontomat/	
  	
  

content	
  extractor	
  

Assists	
  user	
  with	
  annotation	
  of	
  
websites	
  that	
  are	
  Semantic	
  Web-­‐
compliant.	
  May	
  now	
  include	
  a	
  
feature	
  that	
  automatically	
  
suggests	
  portions	
  of	
  the	
  website	
  
to	
  annotate.	
  

Open	
  Text	
  
Summarizer	
  

http://libots.sourceforge.net/	
   content	
  extractor	
  
Extracts	
  pertinent	
  sentences	
  from	
  
a	
  resource	
  to	
  build	
  a	
  free	
  text	
  
description.	
  



	
  

INFORMATION	
  TECHNOLOGY	
  AND	
  LIBRARIES	
  |	
  SEPTEMBER	
  2015	
  	
  

	
  

32	
  

ParsCit	
  
http://wing.comp.nus.edu.sg/parsCit/
#ws	
  	
  

content	
  extractor	
  

Open-­‐source	
  string-­‐parsing	
  
package	
  for	
  the	
  extraction	
  of	
  
reference	
  information	
  from	
  
scholarly	
  articles.	
  

RepoMMan	
  
http://www.hull.ac.uk/esig/	
  
repomman/index.html	
  

meta-­‐tag	
  harvester;	
  
content	
  extractor;	
  
extrinsic	
  auto-­‐
generator	
  

Automatically	
  extracts	
  various	
  
elements	
  for	
  documents	
  
uploaded	
  to	
  Fedora	
  such	
  as	
  
author,	
  title,	
  description,	
  and	
  key	
  
words,	
  among	
  others.	
  Results	
  are	
  
presented	
  to	
  user	
  for	
  review.	
  

Simple	
  
Automatic	
  
Metadata	
  
Generation	
  
Interface	
  
(SamgI)	
  

http://hmdb.cs.kuleuven.be/amg/	
  
Download.php	
  

content	
  extractor;	
  
extrinsic	
  auto-­‐
generator	
  

A	
  suite	
  of	
  tools	
  that	
  is	
  able	
  to	
  
automatically	
  extract	
  metadata	
  
elements	
  such	
  as	
  key	
  phrase	
  and	
  
language	
  from	
  documents	
  as	
  well	
  
as	
  from	
  the	
  context	
  in	
  which	
  a	
  
document	
  exists.	
  	
  

Termine	
  
http://www.nactem.ac.uk/software/	
  
termine/	
  	
  

content	
  extractor	
  

Extracts	
  keywords	
  from	
  texts	
  
through	
  C-­‐value	
  analysis	
  and	
  
Acromine,	
  an	
  acronym	
  identifier	
  
and	
  dictionary.	
  Available	
  as	
  free	
  
web	
  service	
  for	
  academic	
  use.	
  

Yahoo	
  Content	
  
Analysis	
  API	
  

https://developer.yahoo.com/	
  
contentanalysis/	
  	
  

content	
  extractor;	
  
automatic	
  indexer	
  

The	
  Content	
  Analysis	
  Web	
  
Service	
  detects	
  entities/concepts,	
  
categories,	
  and	
  relationships	
  
within	
  unstructured	
  content.	
  It	
  
ranks	
  those	
  detected	
  
entities/concepts	
  by	
  their	
  overall	
  
relevance,	
  resolves	
  those	
  if	
  
possible	
  into	
  Wikipedia	
  pages,	
  
and	
  annotates	
  tags	
  with	
  relevant	
  
metadata.	
  

Table	
  2.	
  Semi-­‐automatic	
  Tools	
  that	
  Support	
  Content	
  Extraction	
  

Automatic	
  Indexing	
  

In	
  the	
  same	
  way	
  as	
  content	
  extraction,	
  automatic	
  indexing	
  involves	
  the	
  use	
  of	
  machine	
  learning	
  
and	
  rule-­‐based	
  algorithms	
  to	
  extract	
  metadata	
  values	
  from	
  within	
  information	
  resources	
  
themselves,	
  rather	
  than	
  relying	
  on	
  the	
  content	
  of	
  meta-­‐tags	
  applied	
  to	
  resources.	
  However,	
  this	
  
technique	
  also	
  involves	
  the	
  mapping	
  of	
  extracted	
  metadata	
  terms	
  to	
  controlled	
  vocabularies	
  such	
  
as	
  the	
  Library	
  of	
  Congress	
  Subject	
  Headings	
  (LCSH),	
  the	
  Getty	
  Thesaurus	
  of	
  Geographic	
  Names	
  
(TGN),	
  or	
  the	
  Library	
  of	
  Congress	
  Name	
  Authority	
  File	
  (LCNAF),	
  or	
  to	
  domain-­‐specific	
  or	
  locally	
  
developed	
  ontologies.	
  Thus,	
  in	
  this	
  technique,	
  researchers	
  use	
  classifying	
  and	
  clustering	
  
algorithms	
  to	
  extract	
  relevant	
  metadata	
  from	
  texts.	
  Term-­‐frequency	
  statistics	
  or	
  IF.IDF,	
  which	
  
determines	
  likelihood	
  of	
  keyword	
  applicability	
  through	
  its	
  relative	
  frequency	
  within	
  a	
  given	
  



	
  

EVALUATION	
  OF	
  SEMI-­‐AUTOMATIC	
  METADATA	
  GENERATION	
  TOOLS|	
  PARK	
  AND	
  BRENZA	
  	
  
doi:	
  10.6017/ital.v34i3.5889	
  

33	
  

document	
  as	
  opposed	
  to	
  its	
  relative	
  infrequency	
  in	
  related	
  documents,	
  are	
  commonly	
  used	
  in	
  this	
  
technique.	
  	
  

Projects	
  such	
  as	
  John	
  Hopkins	
  University’s	
  Automatic	
  Name	
  Authority	
  Control	
  (ANAC)	
  tool	
  utilizes	
  
this	
  technique	
  to	
  extract	
  the	
  names	
  of	
  composers	
  within	
  its	
  sheet	
  music	
  collections	
  and	
  to	
  assign	
  
the	
  authorized	
  form	
  of	
  those	
  names	
  based	
  on	
  comparisons	
  with	
  LCNAF.11	
  Erbs	
  et	
  al.	
  also	
  use	
  this	
  
technique	
  to	
  extract	
  key	
  phrases	
  from	
  German	
  educational	
  documents	
  which	
  are	
  then	
  used	
  to	
  
assign	
  index	
  terms,	
  thereby	
  increasing	
  the	
  degree	
  to	
  which	
  related	
  documents	
  are	
  collocated	
  
within	
  the	
  repository	
  and	
  the	
  consistency	
  of	
  subject	
  term	
  application.12	
  

Table	
  3	
  lists	
  the	
  tools	
  that	
  support	
  automatic	
  indexing	
  either	
  as	
  the	
  sole	
  technique	
  or	
  as	
  one	
  of	
  a	
  
suite	
  of	
  techniques	
  used	
  to	
  generate	
  metadata	
  from	
  resources.	
  Of	
  the	
  thirty-­‐nine	
  tools	
  evaluated	
  
for	
  this	
  study,	
  seven	
  tools	
  support	
  some	
  form	
  of	
  automatic	
  indexing.	
  

Tool	
  Name	
   Location	
   Techniques	
   Functions/Features	
  

Apache	
  POI—
Text	
  Extractor	
  

http://poi.apache.org/download.html	
  	
   content	
  extractor;	
  
meta-­‐tag	
  harvester;	
  
extrinsic	
  auto-­‐
generator	
  

Apache	
  POI	
  provides	
  basic	
  text	
  
extraction	
  for	
  all	
  project	
  
supported	
  file	
  formats.	
  In	
  addition	
  
to	
  the	
  (plain)	
  text,	
  Apache	
  POI	
  can	
  
access	
  the	
  metadata	
  associated	
  
with	
  a	
  given	
  file,	
  such	
  as	
  title	
  and	
  
author.	
  	
  

Apache	
  Tika	
   http://tika.apache.org/	
  	
   content	
  extractor;	
  
meta-­‐tag	
  harvester;	
  
extrinsic	
  auto-­‐
generator	
  

Built	
  on	
  Apache	
  POI,	
  the	
  Apache	
  
Tika	
  toolkit	
  detects	
  and	
  extracts	
  
metadata	
  and	
  text	
  content	
  from	
  
various	
  documents.	
  

Data	
  
Fountains	
  

http://datafountains.ucr.edu/	
  	
   content	
  extractor;	
  
automatic	
  indexer;	
  
meta-­‐tag	
  harvester;	
  
extrinsic	
  auto-­‐
generator	
  

Scans	
  HTML	
  documents	
  and	
  first	
  
extracts	
  information	
  contained	
  in	
  
meta-­‐tags.	
  If	
  information	
  is	
  
unavailable	
  in	
  meta-­‐tags,	
  the	
  
program	
  will	
  use	
  other	
  techniques	
  
to	
  assign	
  values.	
  Includes	
  a	
  
focused	
  web	
  crawler	
  that	
  can	
  
target	
  websites	
  concerning	
  a	
  
specific	
  subject.	
  	
  

Digital	
  Record	
  
Object	
  
Identification	
  
(DROID)	
  

http://www.nationalarchives.gov.uk/	
  
information-­‐management/manage	
  
-­‐information/preserving-­‐digital	
  
-­‐records/droid/	
  	
  

extrinsic	
  auto-­‐
generator	
  

DROID	
  is	
  a	
  software	
  tool	
  
developed	
  by	
  the	
  National	
  
Archives	
  to	
  perform	
  automated	
  
batch	
  identification	
  of	
  file	
  formats.	
  

Dspace	
   http://www.dspace.org/	
  	
   meta-­‐tag	
  harvester;	
  
extrinsic	
  auto-­‐
generator	
  

Automatically	
  extracts	
  technical	
  
information	
  regarding	
  file	
  format	
  
and	
  size.	
  Can	
  also	
  extract	
  some	
  
information	
  from	
  meta-­‐tags.	
  

Editor-­‐
Converter	
  
Dublin	
  Core	
  
Metadata	
  

http://www.library.kr.ua/dc/	
  
dceditunie.html	
  

meta-­‐tag	
  harvester;	
  
extrinsic	
  auto-­‐
generator	
  

Scans	
  HTML	
  documents,	
  
harvesting	
  metadata	
  from	
  tags	
  
and	
  converting	
  them	
  to	
  Dublin	
  
Core.	
  



	
  

INFORMATION	
  TECHNOLOGY	
  AND	
  LIBRARIES	
  |	
  SEPTEMBER	
  2015	
  	
  

	
  

34	
  

Embedded	
  
Metadata	
  
Extraction	
  
Tool	
  (EMET)	
  

http://www.artstor.org/global/g	
  
-­‐html/download-­‐emet-­‐public.html	
  	
  

content	
  extractor;	
  
meta-­‐tag	
  harvester;	
  
extrinsic	
  auto-­‐
generator	
  

EMET	
  is	
  a	
  tool	
  designed	
  to	
  extract	
  
metadata	
  embedded	
  in	
  JPEG	
  and	
  
TIFF	
  files.	
  

Firefox	
  Dublin	
  
Core	
  Viewer	
  
Extension	
  

http://www.splintered.co.uk/	
  
experiments/73/	
  	
  

meta-­‐tag	
  harvester;	
  
extrinsic	
  auto-­‐
generator	
  

Scans	
  HTML	
  documents,	
  
harvesting	
  metadata	
  from	
  tags	
  
and	
  displaying	
  them	
  to	
  Dublin	
  
Core.	
  

JHove	
   http://jhove.sourceforge.net/	
  
#implementation	
  	
  

extrinsic	
  auto-­‐
generator	
  

Extracts	
  metadata	
  regarding	
  file	
  
format	
  and	
  size	
  as	
  well	
  as	
  
validating	
  the	
  structure	
  of	
  the	
  
identified	
  file	
  format.	
  

National	
  
Library	
  of	
  
New	
  
Zealand—
Metadata	
  
Extraction	
  
Tool	
  

http://meta-­‐extractor	
  
.sourceforge.net/	
  	
  

extrinsic	
  auto-­‐
generator	
  

Developed	
  by	
  the	
  National	
  Library	
  
of	
  New	
  Zealand	
  to	
  
programmatically	
  extract	
  
preservation	
  metadata	
  from	
  a	
  
range	
  of	
  file	
  formats	
  like	
  PDF	
  
documents,	
  image	
  files,	
  sound	
  
files,	
  Microsoft	
  Office	
  documents,	
  
and	
  others.	
  

Omeka	
   http://omeka.org/	
  	
   extrinsic	
  auto-­‐
generator;	
  social	
  
tagging	
  

Automatically	
  extracts	
  technical	
  
information	
  regarding	
  file	
  format	
  
and	
  size.	
  	
  

RepoMMan	
   http://www.hull.ac.uk/esig/	
  
repomman/index.html	
  

meta-­‐tag	
  harvester;	
  
content	
  extractor;	
  
extrinsic	
  auto-­‐
generator	
  

Automatically	
  extracts	
  various	
  
elements	
  for	
  documents	
  uploaded	
  
to	
  Fedora	
  such	
  as	
  author,	
  title,	
  
description,	
  and	
  key	
  words,	
  
among	
  others.	
  Results	
  are	
  
presented	
  to	
  user	
  for	
  review.	
  

Simple	
  
Automatic	
  
Metadata	
  
Generation	
  
Interface	
  
(SamgI)	
  

http://hmdb.cs.kuleuven.be/amg/	
  
Download.php	
  

content	
  extractor;	
  
extrinsic	
  auto-­‐
generator	
  

A	
  suite	
  of	
  tools	
  that	
  is	
  able	
  to	
  
automatically	
  extract	
  metadata	
  
elements	
  such	
  as	
  keyphrase	
  and	
  
language	
  from	
  documents	
  as	
  well	
  
as	
  from	
  the	
  context	
  in	
  which	
  a	
  
document	
  exists.	
  	
  

Table	
  3.	
  Semi-­‐automatic	
  Tools	
  that	
  Support	
  Automatic	
  Indexing	
  

Text	
  and	
  Data	
  Mining	
  

The	
  two	
  methods	
  discussed	
  above,	
  content	
  extraction	
  and	
  automatic	
  indexing,	
  rely	
  on	
  text-­‐	
  and	
  
data-­‐mining	
  techniques	
  for	
  the	
  automatic	
  extraction	
  of	
  metadata.	
  In	
  other	
  words,	
  the	
  above	
  
methods	
  utilize	
  machine-­‐learning	
  algorithms,	
  statistical	
  analysis	
  of	
  term	
  frequencies,	
  clustering	
  
techniques,	
  or	
  techniques	
  that	
  examine	
  the	
  frequency	
  of	
  term	
  utilization	
  between	
  documents	
  as	
  
opposed	
  to	
  the	
  use	
  of	
  controlled	
  vocabularies,	
  and	
  classifying	
  techniques,	
  or	
  techniques	
  that	
  
exploit	
  the	
  conventional	
  structure	
  of	
  documents,	
  for	
  the	
  semi-­‐automatic	
  generation	
  of	
  metadata.	
  
Because	
  of	
  the	
  complexity	
  of	
  these	
  techniques,	
  few	
  tools	
  have	
  been	
  fully	
  developed	
  for	
  application	
  
within	
  real-­‐world	
  library	
  settings.	
  Rather,	
  most	
  uses	
  of	
  these	
  techniques	
  have	
  been	
  developed	
  to	
  
solve	
  the	
  problems	
  of	
  automatic	
  metadata	
  generation	
  within	
  the	
  context	
  of	
  specific	
  research	
  
projects.	
  	
  



	
  

EVALUATION	
  OF	
  SEMI-­‐AUTOMATIC	
  METADATA	
  GENERATION	
  TOOLS|	
  PARK	
  AND	
  BRENZA	
  	
  
doi:	
  10.6017/ital.v34i3.5889	
  

35	
  

There	
  are	
  two	
  reasons	
  for	
  this.	
  One	
  is	
  that,	
  as	
  many	
  researchers	
  have	
  noted,	
  the	
  effectiveness	
  of	
  
machine	
  learning	
  techniques	
  depends	
  on	
  the	
  quality	
  and	
  quantity	
  of	
  training	
  data	
  used	
  to	
  teach	
  
the	
  system.13,	
  14,	
  15	
  Because	
  of	
  the	
  number	
  and	
  diversity	
  of	
  subject	
  domains	
  as	
  well	
  as	
  the	
  shear	
  
variety	
  of	
  document	
  formats,	
  many	
  applications	
  are	
  designed	
  to	
  address	
  the	
  metadata	
  needs	
  of	
  
very	
  specific	
  subject	
  domains	
  and	
  very	
  specific	
  types	
  of	
  documents.	
  This	
  is	
  a	
  point	
  that	
  Kovacevic	
  
et	
  al.	
  make	
  in	
  stating	
  that	
  machine	
  learning	
  techniques	
  generally	
  work	
  best	
  for	
  documents	
  of	
  a	
  
similar	
  type,	
  like	
  research	
  papers.16	
  Another	
  issue,	
  especially	
  as	
  it	
  applies	
  to	
  automatic	
  indexing,	
  is	
  
the	
  fact	
  that,	
  as	
  Gardner	
  notes,	
  controlled	
  vocabularies	
  such	
  as	
  the	
  LCSH	
  are	
  too	
  complicated	
  and	
  
diverse	
  in	
  structure	
  to	
  be	
  applied	
  through	
  semi-­‐automatic	
  means.17	
  Although	
  some	
  open-­‐source	
  
tools	
  such	
  as	
  Data	
  Fountains	
  have	
  made	
  efforts	
  to	
  overcome	
  this	
  complexity,	
  projects	
  like	
  it	
  are	
  the	
  
exception	
  rather	
  than	
  the	
  rule.	
  These	
  issues	
  signify	
  the	
  difficulty	
  with	
  developing	
  sophisticated	
  
semi-­‐automatic	
  metadata	
  generation	
  tools	
  that	
  have	
  general	
  applicability	
  across	
  a	
  wide	
  range	
  of	
  
subject	
  domains	
  and	
  format	
  types.	
  Nevertheless,	
  for	
  semi-­‐automatic	
  metadata	
  generation	
  tools	
  to	
  
become	
  a	
  reality	
  for	
  the	
  library	
  community,	
  such	
  complexity	
  will	
  have	
  to	
  be	
  overcome.	
  

There	
  are,	
  however,	
  some	
  tools	
  that	
  have	
  broader	
  applicability	
  or	
  can	
  be	
  customized	
  to	
  meet	
  local	
  
needs.	
  For	
  instance,	
  the	
  Kea	
  keyphrase	
  extractor	
  offers	
  the	
  option	
  of	
  building	
  local	
  or	
  applying	
  
available	
  ontologies	
  that	
  can	
  be	
  used	
  to	
  refine	
  the	
  extraction	
  process.	
  Perhaps	
  the	
  most	
  promising	
  
of	
  all	
  is	
  the	
  above	
  mentioned	
  Data	
  Fountains	
  suite	
  of	
  tools	
  developed	
  by	
  the	
  University	
  of	
  
California.	
  The	
  Data	
  Fountains	
  suite	
  incorporates	
  almost	
  every	
  one	
  of	
  the	
  semi-­‐automatic	
  
metadata	
  techniques	
  described	
  in	
  this	
  study,	
  including	
  sophisticated	
  content	
  extraction	
  and	
  
automatic	
  indexing	
  features.	
  It	
  also	
  provides	
  several	
  ways	
  to	
  customize	
  the	
  suite	
  in	
  order	
  to	
  meet	
  
local	
  needs.	
  	
  

Extrinsic	
  Data	
  Auto-­‐Generation	
  

Extrinsic	
  data	
  auto-­‐generation	
  is	
  the	
  process	
  of	
  extracting	
  metadata	
  about	
  an	
  information	
  
resource	
  that	
  is	
  not	
  contained	
  within	
  the	
  resource	
  itself.	
  Extrinsic	
  data	
  auto-­‐generation	
  can	
  
involve	
  the	
  extraction	
  of	
  technical	
  metadata	
  such	
  as	
  file	
  format	
  and	
  size	
  but	
  can	
  also	
  include	
  the	
  
extraction	
  of	
  more	
  complicated	
  features	
  such	
  as	
  the	
  grade	
  level	
  of	
  an	
  educational	
  resource	
  or	
  the	
  
intended	
  audience	
  for	
  a	
  document.	
  The	
  process	
  of	
  extracting	
  technical	
  metadata	
  is	
  perhaps	
  one	
  
area	
  of	
  semi-­‐automatic	
  metadata	
  generation	
  that	
  is	
  in	
  a	
  high	
  state	
  of	
  development,	
  included	
  in	
  
most	
  CMSS	
  such	
  as	
  Dspace,18	
  as	
  well	
  as	
  other	
  more	
  sophisticated	
  tools	
  such	
  as	
  Harvard’s	
  JHove,	
  
which	
  can	
  recognize	
  at	
  least	
  7twelve	
  different	
  kinds	
  of	
  textual,	
  audio,	
  and	
  visual	
  file	
  formats.19	
  On	
  
the	
  other	
  hand,	
  the	
  problem	
  of	
  semi-­‐automatically	
  generating	
  other	
  types	
  of	
  extrinsic	
  metadata,	
  
like	
  grade	
  level,	
  are	
  of	
  the	
  most	
  difficult	
  to	
  solve.	
  	
  

As	
  Leibbrandt	
  et	
  al.	
  note	
  in	
  their	
  analysis	
  of	
  the	
  use	
  of	
  artificial	
  intelligence	
  mechanisms	
  to	
  
generate	
  subject	
  metadata	
  for	
  a	
  repository	
  of	
  educational	
  materials	
  at	
  the	
  Education	
  Services	
  
Australia,	
  the	
  extraction	
  of	
  extrinsic	
  metadata	
  such	
  as	
  grade	
  level	
  was	
  much	
  more	
  difficult	
  than	
  
the	
  extraction	
  of	
  keywords	
  because	
  of	
  the	
  lack	
  of	
  information	
  surrounding	
  a	
  resource’s	
  context	
  
within	
  the	
  resource	
  itself.20	
  This	
  difficulty	
  can	
  also	
  be	
  seen	
  in	
  the	
  absence	
  of	
  tools	
  that	
  support	
  the	
  



	
  

INFORMATION	
  TECHNOLOGY	
  AND	
  LIBRARIES	
  |	
  SEPTEMBER	
  2015	
  	
  

	
  

36	
  

extraction	
  of	
  extrinsic	
  data	
  beyond	
  those	
  that	
  are	
  harvesting	
  metadata	
  that	
  has	
  been	
  created	
  
manually	
  or	
  extracting	
  technical	
  metadata.	
  	
  

Table	
  4	
  lists	
  the	
  tools	
  that	
  support	
  extrinsic	
  data	
  auto-­‐generation	
  either	
  as	
  the	
  sole	
  technique	
  or	
  as	
  
one	
  of	
  a	
  suite	
  of	
  techniques	
  used	
  to	
  generate	
  metadata	
  from	
  resources.	
  Of	
  the	
  thirty-­‐nine	
  tools	
  
evaluated	
  for	
  this	
  study,	
  thirteen	
  tools	
  support	
  some	
  form	
  of	
  extrinsic	
  data	
  auto-­‐generation.	
  

Tool	
  Name	
   Location	
   Techniques	
   Functions/Features	
  

Apache	
  POI—
Text	
  Extractor	
  

http://poi.apache.org/download.html	
  	
   content	
  extractor;	
  
meta-­‐tag	
  harvester;	
  
extrinsic	
  auto-­‐
generator	
  

Apache	
  POI	
  provides	
  basic	
  text	
  
extraction	
  for	
  all	
  project	
  
supported	
  file	
  formats.	
  In	
  addition	
  
to	
  the	
  (plain)	
  text,	
  Apache	
  POI	
  can	
  
access	
  the	
  metadata	
  associated	
  
with	
  a	
  given	
  file,	
  such	
  as	
  title	
  and	
  
author.	
  	
  

Apache	
  Tika	
   http://tika.apache.org/	
  	
   content	
  extractor;	
  
meta-­‐tag	
  harvester;	
  
extrinsic	
  auto-­‐
generator	
  

Built	
  on	
  Apache	
  POI,	
  the	
  Apache	
  
Tika	
  toolkit	
  detects	
  and	
  extracts	
  
metadata	
  and	
  text	
  content	
  from	
  
various	
  documents.	
  

Data	
  
Fountains	
  

http://datafountains.ucr.edu/	
  	
   content	
  extractor;	
  
automatic	
  indexer;	
  
meta-­‐tag	
  harvester;	
  
extrinsic	
  auto-­‐
generator	
  

Scans	
  HTML	
  documents	
  and	
  first	
  
extracts	
  information	
  contained	
  in	
  
meta-­‐tags.	
  If	
  information	
  is	
  
unavailable	
  in	
  meta-­‐tags,	
  the	
  
program	
  will	
  use	
  other	
  techniques	
  
to	
  assign	
  values.	
  Includes	
  a	
  
focused	
  web	
  crawler	
  that	
  can	
  
target	
  websites	
  concerning	
  a	
  
specific	
  subject.	
  	
  

Digital	
  Record	
  
Object	
  
Identification	
  
(DROID)	
  

http://www.nationalarchives.gov.uk/	
  
information-­‐management/manage	
  
-­‐information/preserving-­‐digital	
  
-­‐records/droid/	
  	
  

extrinsic	
  auto-­‐
generator	
  

DROID	
  is	
  a	
  software	
  tool	
  
developed	
  by	
  the	
  National	
  
Archives	
  to	
  perform	
  automated	
  
batch	
  identification	
  of	
  file	
  formats.	
  

Dspace	
   http://www.dspace.org/	
  	
   meta-­‐tag	
  harvester;	
  
extrinsic	
  auto-­‐
generator	
  

Automatically	
  extracts	
  technical	
  
information	
  regarding	
  file	
  format	
  
and	
  size.	
  Can	
  also	
  extract	
  some	
  
information	
  from	
  meta-­‐tags.	
  

Editor-­‐
Converter	
  
Dublin	
  Core	
  
Metadata	
  

http://www.library.kr.ua/dc/	
  
dceditunie.html	
  

meta-­‐tag	
  harvester;	
  
extrinsic	
  auto-­‐
generator	
  

Scans	
  HTML	
  documents,	
  
harvesting	
  metadata	
  from	
  tags	
  
and	
  converting	
  them	
  to	
  Dublin	
  
Core.	
  

Embedded	
  
Metadata	
  
Extraction	
  
Tool	
  (EMET)	
  

http://www.artstor.org/global/g	
  
-­‐html/download-­‐emet-­‐public.html	
  	
  

content	
  extractor;	
  
meta-­‐tag	
  harvester;	
  
extrinsic	
  auto-­‐
generator	
  

EMET	
  is	
  a	
  tool	
  designed	
  to	
  extract	
  
metadata	
  embedded	
  in	
  JPEG	
  and	
  
TIFF	
  files.	
  

Firefox	
  Dublin	
  
Core	
  Viewer	
  
Extension	
  

http://www.splintered.co.uk/	
  
experiments/73/	
  	
  

meta-­‐tag	
  harvester;	
  
extrinsic	
  auto-­‐
generator	
  

Scans	
  HTML	
  documents,	
  
harvesting	
  metadata	
  from	
  tags	
  
and	
  displaying	
  them	
  to	
  Dublin	
  
Core.	
  

JHove	
   http://jhove.sourceforge.net/	
   extrinsic	
  auto-­‐ Extracts	
  metadata	
  regarding	
  file	
  



	
  

EVALUATION	
  OF	
  SEMI-­‐AUTOMATIC	
  METADATA	
  GENERATION	
  TOOLS|	
  PARK	
  AND	
  BRENZA	
  	
  
doi:	
  10.6017/ital.v34i3.5889	
  

37	
  

#implementation	
  	
   generator	
   format	
  and	
  size	
  as	
  well	
  as	
  
validating	
  the	
  structure	
  of	
  the	
  
identified	
  file	
  format.	
  

National	
  
Library	
  of	
  
New	
  
Zealand—
Metadata	
  
Extraction	
  
Tool	
  

http://meta-­‐extractor	
  
.sourceforge.net/	
  	
  

extrinsic	
  auto-­‐
generator	
  

Developed	
  by	
  the	
  National	
  Library	
  
of	
  New	
  Zealand	
  to	
  
programmatically	
  extract	
  
preservation	
  metadata	
  from	
  a	
  
range	
  of	
  file	
  formats	
  like	
  PDF	
  
documents,	
  image	
  files,	
  sound	
  
files,	
  Microsoft	
  Office	
  documents,	
  
and	
  others.	
  

Omeka	
   http://omeka.org/	
  	
   extrinsic	
  auto-­‐
generator;	
  social	
  
tagging	
  

Automatically	
  extracts	
  technical	
  
information	
  regarding	
  file	
  format	
  
and	
  size.	
  	
  

RepoMMan	
   http://www.hull.ac.uk/esig/	
  
repomman/index.html	
  

meta-­‐tag	
  harvester;	
  
content	
  extractor;	
  
extrinsic	
  auto-­‐
generator	
  

Automatically	
  extracts	
  various	
  
elements	
  for	
  documents	
  uploaded	
  
to	
  Fedora	
  such	
  as	
  author,	
  title,	
  
description,	
  and	
  key	
  words,	
  
among	
  others.	
  Results	
  are	
  
presented	
  to	
  user	
  for	
  review.	
  

Simple	
  
Automatic	
  
Metadata	
  
Generation	
  
Interface	
  
(SamgI)	
  

http://hmdb.cs.kuleuven.be/amg/	
  
Download.php	
  

content	
  extractor;	
  
extrinsic	
  auto-­‐
generator	
  

A	
  suite	
  of	
  tools	
  that	
  is	
  able	
  to	
  
automatically	
  extract	
  metadata	
  
elements	
  such	
  as	
  keyphrase	
  and	
  
language	
  from	
  documents	
  as	
  well	
  
as	
  from	
  the	
  context	
  in	
  which	
  a	
  
document	
  exists.	
  	
  

	
  
Table	
  4.	
  Semi-­‐Automatic	
  Tools	
  that	
  Support	
  Extrinsic	
  Data	
  Auto-­‐Generation.	
  

Social	
  Tagging	
  	
  

Social	
  tagging	
  is	
  now	
  a	
  familiar	
  form	
  of	
  subject	
  metadata	
  generation	
  although,	
  as	
  mentioned	
  
previously,	
  it	
  is	
  not	
  properly	
  a	
  form	
  of	
  automatic	
  metadata	
  generation.	
  Nevertheless,	
  because	
  of	
  
the	
  relatively	
  low	
  cost	
  in	
  generating	
  and	
  maintaining	
  metadata	
  through	
  social	
  tagging	
  and	
  its	
  
current	
  widespread	
  popularity,	
  a	
  few	
  projects	
  have	
  attempted	
  to	
  utilize	
  such	
  data	
  to	
  enhance	
  
repositories.	
  For	
  instance,	
  Linstaedt	
  et	
  al.	
  use	
  sophisticated	
  computer	
  programs	
  to	
  analyze	
  still	
  
images	
  found	
  within	
  Flickr	
  and	
  then	
  use	
  this	
  analysis	
  to	
  process	
  new	
  images	
  and	
  to	
  propagate	
  
relevant	
  user	
  tags	
  to	
  those	
  images.21	
  	
  

In	
  a	
  slightly	
  more	
  complicated	
  example,	
  Liu	
  and	
  Qin	
  employ	
  machine-­‐learning	
  techniques	
  to	
  
initially	
  process	
  and	
  assign	
  metadata,	
  including	
  subject	
  terms,	
  to	
  a	
  repository	
  of	
  documents	
  
related	
  to	
  the	
  computer	
  science	
  profession.22	
  However,	
  this	
  proof	
  of	
  concept	
  project	
  also	
  permits	
  
users	
  to	
  edit	
  the	
  fields	
  of	
  the	
  metadata	
  once	
  established.	
  The	
  user-­‐edited	
  tags	
  are	
  then	
  
reprocessed	
  by	
  the	
  system	
  with	
  the	
  hope	
  of	
  improving	
  the	
  machine-­‐learning	
  mechanisms	
  of	
  the	
  
database,	
  creating	
  a	
  kind	
  of	
  feedback	
  loop	
  for	
  the	
  system.	
  Specifically,	
  the	
  improved	
  tags	
  are	
  used	
  
by	
  the	
  system	
  to	
  suggest	
  and	
  assign	
  subject	
  terms	
  for	
  new	
  documents	
  as	
  well	
  as	
  to	
  improve	
  
subject	
  description	
  of	
  existing	
  documents	
  within	
  the	
  repository.	
  Although	
  these	
  two	
  examples	
  
provide	
  instances	
  of	
  sophisticated	
  reprocessing	
  of	
  social	
  tag	
  metadata,	
  these	
  capabilities	
  do	
  not	
  
seem	
  to	
  be	
  present	
  in	
  open-­‐source	
  tools	
  at	
  this	
  time.	
  Nevertheless,	
  social	
  tagging	
  capabilities	
  are	
  
offered	
  by	
  many	
  CMSS	
  such	
  as	
  Omeka.	
  These	
  social	
  tagging	
  capabilities	
  may	
  offer	
  a	
  means	
  to	
  
enhance	
  subject	
  access	
  to	
  holdings.	
  	
  



	
  

INFORMATION	
  TECHNOLOGY	
  AND	
  LIBRARIES	
  |	
  SEPTEMBER	
  2015	
  	
  

	
  

38	
  

Table	
  5	
  below	
  lists	
  the	
  tools	
  that	
  support	
  social	
  tagging	
  either	
  as	
  the	
  sole	
  technique	
  or	
  as	
  one	
  of	
  a	
  
suite	
  of	
  techniques	
  used	
  to	
  generate	
  metadata	
  from	
  resources.	
  Of	
  the	
  thirty-­‐nine	
  tools	
  evaluated	
  
for	
  this	
  study,	
  two	
  tools	
  support	
  some	
  form	
  of	
  social	
  tagging.	
  

Tool	
  Name	
   Location	
   Techniques	
   Functions/Features	
  

Dspace	
   http://www.dspace.org/	
  	
  

meta-­‐tag	
  harvester;	
  
extrinsic	
  auto-­‐
generator;	
  social	
  
tagging	
  

Automatically	
  extracts	
  
technical	
  information	
  
regarding	
  file	
  format	
  and	
  size.	
  
Can	
  also	
  extract	
  some	
  
information	
  from	
  meta-­‐tags.	
  

Omeka	
   http://omeka.org/	
  	
  
extrinsic	
  auto-­‐
generator;	
  social	
  
tagging	
  

Automatically	
  extracts	
  
technical	
  information	
  
regarding	
  file	
  format	
  and	
  size.	
  	
  

Table	
  5.	
  Semi-­‐automatic	
  Tools	
  that	
  Support	
  Social	
  Tagging.	
  

Challenges	
  to	
  Implementation	
  

Although	
  semi-­‐automatic	
  metadata	
  generation	
  tools	
  offer	
  many	
  benefits,	
  especially	
  in	
  regards	
  to	
  
streamlining	
  the	
  metadata-­‐creation	
  process,	
  there	
  are	
  significant	
  barriers	
  to	
  the	
  widespread	
  
adoption	
  and	
  implementation	
  of	
  these	
  tools.	
  One	
  problem	
  with	
  semi-­‐automatic	
  metadata	
  
generation	
  tools	
  is	
  that	
  many	
  are	
  developed	
  locally	
  to	
  address	
  the	
  specific	
  needs	
  of	
  a	
  given	
  project	
  
or	
  as	
  part	
  of	
  academic	
  research.	
  This	
  local,	
  highly	
  focused	
  milieu	
  for	
  development	
  means	
  that	
  
general	
  applicability	
  of	
  the	
  tools	
  is	
  potentially	
  diminished.	
  The	
  local	
  context	
  may	
  also	
  hinder	
  
widespread	
  adoption	
  of	
  applications	
  that	
  would	
  result	
  in	
  strong	
  communities	
  of	
  application	
  users	
  
and	
  provide	
  further	
  support	
  for	
  the	
  development	
  of	
  applications	
  in	
  an	
  open-­‐source	
  context.	
  
Because	
  of	
  the	
  highly	
  specific	
  nature	
  of	
  many	
  current	
  tools,	
  their	
  relevance	
  to	
  real-­‐world	
  
processes	
  of	
  metadata	
  creation	
  within	
  the	
  broader	
  context	
  of	
  libraries’	
  diverse	
  information	
  
management	
  needs	
  are	
  not	
  accounted	
  for.	
  

Additionally,	
  many	
  tools	
  are	
  focused	
  on	
  solving	
  one	
  or,	
  at	
  most,	
  a	
  few	
  metadata	
  generation	
  
problems.	
  For	
  instance,	
  the	
  Kea	
  application	
  is	
  designed	
  to	
  use	
  machine-­‐learning	
  techniques	
  for	
  the	
  
sole	
  purpose	
  of	
  extracting	
  keywords,	
  the	
  Open	
  Text	
  Summarizer	
  is	
  limited	
  to	
  automatic	
  
extractions	
  of	
  summary	
  descriptions	
  and	
  keywords,	
  and	
  Editor	
  Converter	
  Dublin	
  Core	
  is	
  designed	
  
to	
  extract	
  information	
  in	
  HTML	
  meta-­‐tags	
  and	
  map	
  them	
  to	
  Dublin	
  Core	
  elements.	
  Because	
  of	
  the	
  
piecemeal	
  development	
  of	
  semi-­‐automatic	
  generation	
  tools,	
  any	
  comprehensive	
  package	
  of	
  tools	
  
will	
  require	
  the	
  significant	
  efforts	
  of	
  the	
  implementer	
  to	
  coordinate	
  the	
  selected	
  applications	
  and	
  
to	
  produce	
  results	
  in	
  a	
  single	
  output.	
  This	
  is,	
  to	
  say	
  the	
  least,	
  a	
  daunting	
  task.	
  	
  

Furthermore,	
  a	
  high	
  degree	
  of	
  technical	
  skill	
  is	
  required	
  to	
  implement	
  these	
  complex	
  tools.	
  Many	
  
of	
  the	
  more	
  sophisticated	
  tools	
  used	
  to	
  semi-­‐automatically	
  generate	
  metadata,	
  such	
  as	
  Data	
  
Fountains,	
  Kea,	
  and	
  Apache	
  Stanbol,	
  require	
  competence	
  in	
  a	
  variety	
  of	
  programming	
  languages.	
  



	
  

EVALUATION	
  OF	
  SEMI-­‐AUTOMATIC	
  METADATA	
  GENERATION	
  TOOLS|	
  PARK	
  AND	
  BRENZA	
  	
  
doi:	
  10.6017/ital.v34i3.5889	
  

39	
  

Significant	
  knowledge	
  of	
  C++,	
  Python,	
  and	
  Java,	
  are	
  required	
  to	
  implement	
  these	
  systems	
  properly.	
  
The	
  high	
  degree	
  of	
  technical	
  knowledge	
  needed	
  to	
  implement	
  these	
  tools	
  means	
  that	
  many	
  
libraries	
  and	
  other	
  institutions	
  may	
  not	
  have	
  resources	
  to	
  begin	
  implementing	
  them,	
  let	
  alone	
  
incorporating	
  them	
  into	
  the	
  daily	
  workflows	
  of	
  the	
  metadata	
  creation	
  process.	
  Further,	
  this	
  high	
  
degree	
  of	
  technical	
  expertise	
  may	
  require	
  libraries	
  to	
  seek	
  assistance	
  outside	
  of	
  the	
  library.	
  In	
  
other	
  words,	
  librarians	
  may	
  need	
  to	
  build	
  strong	
  collaborative	
  relationships	
  with	
  those	
  who	
  have	
  
the	
  technical	
  skills,	
  expertise	
  and	
  credentials	
  to	
  implement	
  and	
  maintain	
  these	
  complicated	
  tools.	
  
As	
  Vellucci	
  et	
  al.	
  note	
  in	
  regards	
  to	
  their	
  development	
  of	
  the	
  Metadata	
  Education	
  and	
  Research	
  
Information	
  Commons	
  (MERIC),	
  a	
  metadata-­‐driven	
  clearinghouse	
  of	
  education	
  materials	
  related	
  
to	
  metadata,	
  elaborate	
  and	
  multidisciplinary	
  partnerships	
  need	
  to	
  be	
  firmly	
  established	
  for	
  the	
  
ultimate	
  success	
  of	
  such	
  projects,	
  including	
  the	
  sustained	
  support	
  of	
  the	
  highest	
  levels	
  of	
  
administration.23	
  These	
  types	
  of	
  partnerships	
  may	
  be	
  difficult	
  to	
  establish	
  and	
  maintain	
  for	
  the	
  
sustained	
  implementation	
  of	
  complicated	
  tools.	
  	
  

Additionally,	
  sustainable	
  development	
  of	
  tools,	
  especially	
  in	
  regards	
  to	
  the	
  funding	
  needed	
  for	
  
continued	
  development	
  of	
  open-­‐source	
  applications,	
  appears	
  to	
  be	
  a	
  significant	
  barrier	
  to	
  
implementation.	
  For	
  instance,	
  at	
  the	
  time	
  of	
  this	
  writing,	
  many	
  of	
  the	
  tools	
  that	
  were	
  touted	
  in	
  the	
  
literature	
  as	
  being	
  most	
  promising,	
  such	
  as	
  DC	
  Dot,	
  Reggie,	
  and	
  DescribeThis,	
  are	
  no	
  longer	
  
available	
  for	
  implementation.	
  Beyond	
  the	
  fact	
  that	
  discontinuation	
  hurts	
  the	
  potential	
  adoption	
  
and	
  continued	
  development	
  of	
  semi-­‐automatic	
  tools	
  within	
  real	
  world	
  library	
  and	
  other	
  
information	
  settings,	
  there	
  is	
  also	
  the	
  problem	
  that	
  those	
  settings	
  that	
  have	
  in	
  fact	
  adopted	
  tools	
  
may	
  lose	
  the	
  technical	
  support	
  of	
  a	
  central	
  developer	
  and	
  community	
  of	
  users.	
  Thus	
  
discontinuation	
  may	
  result	
  in	
  higher	
  rates	
  of	
  tool	
  obsolescence	
  and	
  increase	
  the	
  potential	
  
expenses	
  of	
  libraries	
  who	
  have	
  implemented	
  and	
  then	
  must	
  change	
  applications.	
  

Finally,	
  the	
  application	
  of	
  semi-­‐automatic	
  metadata	
  tools	
  remains	
  relatively	
  untested	
  in	
  real-­‐world	
  
scenarios.	
  As	
  Polfreman	
  et	
  al.	
  note,	
  most	
  tests	
  of	
  automatic	
  metadata	
  generation	
  tools	
  have	
  several	
  
of	
  problems,	
  including	
  small	
  sample	
  sizes,	
  narrow	
  scope	
  of	
  project	
  domains,	
  and	
  experiments	
  that	
  
lack	
  true	
  objectivity	
  because	
  systems	
  are	
  generally	
  tested	
  by	
  their	
  creators.24	
  For	
  these	
  reasons,	
  
libraries	
  and	
  other	
  institutions	
  may	
  be	
  reluctant	
  to	
  expand	
  the	
  resources	
  needed	
  to	
  implement	
  
and	
  fully	
  integrate	
  a	
  complicated,	
  promising,	
  but	
  ultimately	
  untested,	
  tool	
  within	
  the	
  already	
  
strained	
  workflows	
  of	
  its	
  processes.	
  	
  

CONCLUSION	
  

Semi-­‐automatic	
  metadata	
  generation	
  tools	
  hold	
  the	
  promise	
  of	
  assisting	
  information	
  professionals	
  
with	
  the	
  management	
  of	
  ever-­‐increasing	
  quantities	
  and	
  types	
  of	
  information	
  resources.	
  Using	
  
software	
  that	
  can	
  create	
  metadata	
  records	
  consistently	
  and	
  efficiently,	
  semi-­‐automatic	
  metadata	
  
generation	
  tools	
  potentially	
  offer	
  significant	
  cost	
  and	
  time	
  savings.	
  However,	
  the	
  full	
  integration	
  of	
  
these	
  tools	
  into	
  the	
  daily	
  workflows	
  of	
  libraries	
  and	
  other	
  information	
  settings	
  remains	
  elusive.	
  
For	
  instance,	
  although	
  many	
  tools	
  have	
  been	
  developed	
  that	
  have	
  addressed	
  many	
  of	
  the	
  more	
  
complicated	
  aspects	
  of	
  semi-­‐automatic	
  metadata	
  generation,	
  including	
  the	
  extraction	
  of	
  



	
  

INFORMATION	
  TECHNOLOGY	
  AND	
  LIBRARIES	
  |	
  SEPTEMBER	
  2015	
  	
  

	
  

40	
  

information	
  related	
  to	
  conceptually	
  difficult	
  areas	
  of	
  bibliographic	
  description	
  such	
  as	
  subject	
  
terms,	
  open-­‐ended	
  resource	
  descriptions,	
  and	
  keyword	
  assignment,	
  many	
  of	
  these	
  tools	
  are	
  
relevant	
  only	
  at	
  the	
  project	
  level	
  and	
  are	
  not	
  applicable	
  to	
  the	
  broader	
  contexts	
  needed	
  by	
  
libraries.	
  In	
  other	
  words,	
  the	
  current	
  array	
  of	
  tools	
  exists	
  to	
  solve	
  experimental	
  problems	
  but	
  has	
  
not	
  been	
  developed	
  to	
  the	
  point	
  that	
  the	
  library	
  community	
  can	
  implement	
  it	
  in	
  a	
  meaningful	
  way.	
  	
  

Perhaps	
  the	
  greatest	
  area	
  of	
  difficulty	
  lies	
  in	
  the	
  fact	
  that	
  most	
  tools	
  only	
  address	
  part	
  of	
  the	
  
problem	
  of	
  semi-­‐automatic	
  metadata	
  generation,	
  providing	
  solutions	
  to	
  the	
  semi-­‐automatic	
  
generation	
  of	
  one	
  or	
  a	
  few	
  bibliographic	
  elements	
  but	
  not	
  the	
  full	
  range	
  elements.	
  This	
  means	
  that	
  
for	
  libraries	
  to	
  truly	
  have	
  a	
  comprehensive	
  tool	
  set	
  for	
  the	
  semi-­‐automatic	
  generation	
  of	
  metadata	
  
records,	
  significant	
  local	
  efforts	
  will	
  be	
  required	
  to	
  integrate	
  the	
  various	
  tools	
  into	
  a	
  working	
  
whole.	
  Couple	
  this	
  issue	
  with	
  the	
  instability	
  of	
  tool	
  development	
  and	
  maintenance	
  and	
  it	
  appears	
  
that	
  the	
  library	
  community	
  may	
  lack	
  incentive	
  to	
  invest	
  already	
  strained	
  and	
  limited	
  resources	
  in	
  
the	
  adoption	
  of	
  these	
  tools.	
  

Thus	
  it	
  appears	
  that	
  a	
  number	
  of	
  steps	
  will	
  need	
  to	
  be	
  taken	
  before	
  the	
  library	
  community	
  can	
  
seriously	
  consider	
  the	
  incorporation	
  of	
  semi-­‐automatic	
  metadata	
  generation	
  tools	
  within	
  its	
  daily	
  
workflows.	
  First,	
  it	
  seems	
  that	
  the	
  integration	
  of	
  these	
  various	
  tools	
  into	
  a	
  coherent	
  set	
  of	
  
applications	
  is	
  likely	
  the	
  next	
  step	
  in	
  the	
  development	
  of	
  viable	
  semi-­‐automatic	
  metadata	
  
generation.	
  Since	
  most	
  small	
  libraries	
  likely	
  do	
  not	
  have	
  the	
  resources	
  required	
  to	
  integrate	
  these	
  
disparate	
  tools	
  together,	
  let	
  alone	
  incorporate	
  them	
  within	
  existing	
  library	
  systems,	
  a	
  single	
  
package	
  of	
  tools	
  will	
  be	
  needed	
  simply	
  from	
  a	
  resource	
  perspective.	
  Secondly,	
  considering	
  the	
  high	
  
level	
  of	
  technical	
  expertise	
  needed	
  to	
  implement	
  the	
  current	
  array	
  of	
  tools,	
  the	
  integrated	
  set	
  of	
  
tools	
  must	
  be	
  accomplished	
  in	
  such	
  a	
  way	
  as	
  to	
  foster	
  implementation,	
  utilization,	
  and	
  
maintenance	
  with	
  a	
  minimum	
  of	
  technical	
  expertise.	
  For	
  instance,	
  if	
  an	
  integrated	
  set	
  of	
  tools	
  that	
  
functioned	
  across	
  a	
  wide	
  range	
  of	
  subject	
  domains	
  and	
  format	
  types	
  could	
  be	
  developed,	
  the	
  suite	
  
might	
  be	
  akin	
  to	
  the	
  CMSS	
  currently	
  employed	
  by	
  many	
  libraries.	
  Furthermore,	
  with	
  a	
  suite	
  of	
  
tools	
  that	
  are	
  relatively	
  easy	
  to	
  use,	
  adaption	
  would	
  likely	
  increase.	
  This	
  might	
  result	
  in	
  a	
  stable	
  
community	
  of	
  users	
  that	
  would	
  foster	
  the	
  further	
  development	
  of	
  the	
  tools	
  in	
  a	
  sustainable	
  
manner.	
  A	
  comprehensive,	
  relatively	
  easy	
  to	
  implement	
  set	
  of	
  tools	
  might	
  foster	
  independent	
  
testing	
  of	
  those	
  tools.	
  The	
  independent	
  testing	
  of	
  the	
  semi-­‐automatic	
  tools	
  is	
  needed	
  to	
  provide	
  an	
  
objective	
  basis	
  for	
  tool	
  evaluation	
  and	
  further	
  development.	
  

Finally,	
  designing	
  automated	
  workflows	
  tailored	
  to	
  the	
  subject	
  domain	
  and	
  types	
  of	
  resources	
  
seems	
  to	
  be	
  an	
  essential	
  step	
  for	
  integrating	
  semi-­‐automatic	
  metadata	
  generation	
  tools	
  into	
  
metadata	
  creation.	
  Such	
  workflows	
  may	
  delineate	
  data	
  elements	
  that	
  can	
  be	
  generated	
  by	
  
automated	
  meta-­‐tag	
  extractor	
  from	
  data	
  elements	
  that	
  need	
  to	
  be	
  refined	
  and	
  manually	
  created	
  by	
  
cataloging	
  and	
  metadata	
  professionals.	
  To	
  develop,	
  maximize,	
  and	
  sustain	
  semi-­‐automatic	
  
metadata	
  generation	
  workflows,	
  administrative	
  support	
  for	
  finance,	
  human	
  resources,	
  and	
  
training	
  is	
  critical.	
  	
  



	
  

EVALUATION	
  OF	
  SEMI-­‐AUTOMATIC	
  METADATA	
  GENERATION	
  TOOLS|	
  PARK	
  AND	
  BRENZA	
  	
  
doi:	
  10.6017/ital.v34i3.5889	
  

41	
  

Thus,	
  although	
  many	
  of	
  the	
  technical	
  aspects	
  of	
  semi-­‐automatic	
  metadata	
  generation	
  are	
  well	
  on	
  
their	
  way	
  to	
  being	
  solved,	
  many	
  other	
  barriers	
  exist	
  that	
  might	
  limit	
  adoption.	
  Further,	
  these	
  
barriers	
  may	
  have	
  a	
  negative	
  influence	
  on	
  the	
  continued,	
  sustainable	
  development	
  of	
  semi-­‐
automatic	
  metadata	
  generation	
  tools.	
  Nevertheless,	
  there	
  is	
  a	
  critical	
  need	
  that	
  the	
  library	
  
community	
  finds	
  ways	
  to	
  manage	
  the	
  recent	
  explosion	
  of	
  data	
  and	
  information	
  in	
  cost-­‐effective	
  
and	
  efficient	
  ways.	
  Semi-­‐automatic	
  metadata	
  generation	
  holds	
  the	
  promise	
  to	
  do	
  just	
  that.	
  	
  

ACKNOWLEDGEMENT	
  

This	
  study	
  was	
  supported	
  by	
  the	
  Institute	
  of	
  Museum	
  and	
  Library	
  Services.	
  

REFERENCES	
  
	
  
1.	
  	
   Jane	
  Greenberg,	
  Kristina	
  Spurgin,	
  and	
  Abe	
  Crystal,	
  “Final	
  Report	
  for	
  the	
  AMeGA	
  (AutoZmatic	
  

2.	
  	
   Sue	
  Ann	
  Gardner,	
  “Cresting	
  Toward	
  the	
  Sea	
  Change,”	
  Library	
  Resources	
  &	
  Technical	
  Services	
  
56,	
  no.	
  2	
  (2012):	
  64–79,	
  http://dx.doi.org/10.5860/lrts.56n2.64.	
  

3.	
  	
   For	
  details,	
  see	
  Jung-­‐ran	
  Park	
  and	
  Caimei	
  Lu,	
  “Application	
  of	
  Semi-­‐Automatic	
  Metadata	
  
Generation	
  in	
  Libraries:	
  Types,	
  Tools,	
  and	
  Techniques,”	
  Library	
  &	
  Information	
  Science	
  
Research	
  31,	
  no.	
  4	
  (2009):	
  225–31,	
  http://dx.doi.org/10.1016/j.lisr.2009.05.002.	
  

4.	
  	
   Erik	
  Mitchell,	
  “Trending	
  Tech	
  Services:	
  Programmatic	
  Tools	
  and	
  the	
  Implications	
  of	
  
Automation	
  in	
  the	
  Next	
  Generation	
  of	
  Metadata,”	
  Technical	
  Services	
  Quarterly	
  30,	
  no.	
  3	
  (2013):	
  
296–10,	
  http://dx.doi.org/10.1080/07317131.2013.785802.	
  	
  

5.	
  	
   Jane	
  Greenberg,	
  “Metadata	
  Extraction	
  and	
  Harvesting:	
  A	
  Comparison	
  of	
  Two	
  Automatic	
  
Metadata	
  Generation	
  Applications,”	
  Journal	
  of	
  Internet	
  Cataloging	
  6,	
  no.	
  4	
  (2004):	
  59–82,	
  
http://dx.doi.org/10.1300/J141v06n04_05.	
  	
  

6.	
  	
   Malcolm	
  Polfreman,	
  Vanda	
  Broughton,	
  and	
  Andrew	
  Wilson,	
  “Metadata	
  Generation	
  for	
  
Resource	
  Discovery,”	
  JISC,	
  2008,	
  
http://www.jisc.ac.uk/whatwedo/programmes/resourcediscovery/autometgen.aspx.	
  

7	
  	
   Park	
  and	
  Lu,	
  “Application	
  of	
  Semi-­‐Automatic	
  Metadata	
  Generation	
  in	
  Libraries.”	
  

8.	
   Kea	
  Automatic	
  Keyphrase	
  Extraction	
  homepage,	
  http://www.nzdl.org/Kea/index_old.html.	
  

9.	
  	
   Wilhelmina	
  Randtke,	
  “Automated	
  Metadata	
  Creation:	
  Possibilities	
  and	
  Pitfalls,”	
  Serials	
  
Librarian	
  64,	
  no.	
  1–4	
  (2013):	
  267–84,	
  http://dx.doi.org/10.1080/0361526X.2013.760286.	
  	
  

	
  

10.	
  	
  Aleksandar	
  Kovačević	
  et	
  al.,“Automatic	
  Extraction	
  of	
  Metadata	
  from	
  Scientific	
  Publications	
  for	
  
CRIS	
  Systems.”	
  Electronic	
  Library	
  and	
  Information	
  Systems	
  45,	
  no.	
  4	
  (2011):	
  376–96,	
  
http://dx.doi.org/10.1108/00330331111182094.	
  



	
  

INFORMATION	
  TECHNOLOGY	
  AND	
  LIBRARIES	
  |	
  SEPTEMBER	
  2015	
  	
  

	
  

42	
  

	
  
11.	
  	
  Mark	
  Patton	
  et	
  al.,	
  “Toward	
  a	
  Metadata	
  Generation	
  Framework:	
  A	
  Case	
  Study	
  at	
  Johns	
  Hopkins	
  

University,”	
  D-­‐Lib	
  Magazine	
  10,	
  no.	
  11	
  (2004),	
  
http://www.dlib.org/dlib/november04/choudhury/11choudhury.html.	
  	
  

12.	
  	
  Nicolai	
  Erbs,	
  Iryna	
  Gurevych,	
  and	
  Marc	
  Rittberger,	
  “Bringing	
  Order	
  to	
  Digital	
  Libraries:	
  From	
  
Keyphrase	
  Extraction	
  to	
  Index	
  Term	
  Assignment.”	
  D-­‐Lib	
  Magazine	
  19,	
  no.	
  9/10	
  (2013),	
  
http://www.dlib.org/dlib/september13/erbs/09erbs.html.	
  

13.	
  	
  Polfreman,	
  Broughton,	
  and	
  Wilson,	
  “Metadata	
  Generation	
  for	
  Resource	
  Discovery.”	
  

14.	
  	
  Randtke,	
  “Automated	
  Metadata	
  Creation.”	
  

15.	
  	
  Xiaozhong	
  Liu	
  and	
  Jian	
  Qin,	
  “An	
  Interactive	
  Metadata	
  Model	
  for	
  Structural,	
  Descriptive,	
  and	
  
Referential	
  Representation	
  of	
  Scholarly	
  Output,”	
  Journal	
  of	
  the	
  Association	
  for	
  Information	
  
Science	
  &	
  Technology	
  65,	
  no.	
  5	
  (2014):	
  964–83,	
  http://dx.doi.org/10.1002/asi.23007.	
  

16.	
  	
  Kovačević	
  et	
  al.,	
  “Automatic	
  Extraction	
  of	
  Metadata	
  from	
  Scientific	
  Publications	
  for	
  CRIS	
  
Systems.”	
  

17.	
  	
  Gardner,	
  “Cresting	
  Toward	
  the	
  Sea	
  Change.”	
  

18.	
  	
  Mary	
  Kurtz,	
  “Dublin	
  Core,	
  Dspace,	
  and	
  a	
  Brief	
  Analysis	
  of	
  Three	
  University	
  Repositories,”	
  
Information	
  Technology	
  &	
  Libraries	
  29,	
  no.	
  1	
  (2010):	
  40–46,	
  
http://dx.doi.org/10.6017/ital.v29i1.3157.	
  	
  

19.	
  	
  “JHOVE	
  -­‐	
  JSTOR/Harvard	
  Object	
  Validation	
  Environment,”	
  JSTOR,	
  	
  
http://jhove.sourceforge.net.	
  

20.	
  	
  Richard	
  Leibbrandt	
  et	
  al.,	
  “Smart	
  Collections:	
  Can	
  Artificial	
  Intelligence	
  Tools	
  and	
  Techniques	
  
Assist	
  with	
  Discovering,	
  Evaluating	
  and	
  Tagging	
  Digital	
  Learning	
  Resources?”	
  International	
  
Association	
  of	
  School	
  Librarianship:	
  Selected	
  Papers	
  from	
  the	
  Annual	
  Conference	
  (2010).	
  

21.	
  	
  Stefanie	
  Lindstaedt	
  et	
  al.,	
  “Automatic	
  Image	
  Annotation	
  Using	
  Visual	
  Content	
  and	
  
Folksonomies,”	
  Multimedia	
  Tools	
  &	
  Applications	
  42,	
  no.	
  1	
  (2009):	
  97–113,	
  
http://dx.doi.org/10.1007/s11042-­‐008-­‐0247-­‐7.	
  

22.	
  	
  Liu	
  and	
  Qin,	
  “An	
  Interactive	
  Metadata	
  Model.”	
  

23.	
  	
  Sherry	
  Vellucci,	
  Ingrid	
  Hsieh-­‐Yee,	
  and	
  William	
  Moen,	
  “The	
  Metadata	
  Education	
  and	
  Research	
  
Information	
  Commons	
  (MERIC):	
  A	
  Collaborative	
  Teaching	
  and	
  Research	
  Initiative,”	
  Education	
  
for	
  Information	
  25,	
  no.	
  3/4	
  (2007):	
  169–78.	
  

24.	
  	
  Polfreman,	
  Broughton,	
  and	
  Wilson,	
  “Metadata	
  Generation	
  for	
  Resource	
  Discovery.”