Ontology-based Annotation

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Ontology-based Annotation

• Sergey Sosnovsky_at_PAWS_at_SIS_at_PITT

2
Outline

• O-based Annotation
• Conclusion
• Questions

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Why Do We Need Annotation

• Annotation-based Services
• Integration of Disperse Information
  (knowledge-based linking)
• Better Indexing and Retrieval (based on the
  document semantics)
• Content-based Adaptation (modeling document
  content in terms of domain model)
• Knowledge Management
• Organizations Repositories as mini Webs (Boeing,
  Rolls Royce, Fiat, GlaxoSmithKline, Merck, NPSA,
  )
• Collaboration Support
• Knowledge sharing and communication

What is Added by O-based Annotation

• Ontology-driven processing (effective formal
  reasoning)
• Connecting other O-based Services (O-mapping,
  O-visualization)
• Unified vocabulary
• Connecting to the rest of SW knowledge

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Definition

• O-based Annotation is a process ofcreating a
  mark-up of Web-documents using a pre-existing
  ontologyand/orpopulating knowledge bases by
  marked up documents

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5
List of Tools

• AeroDAML / AeroSWARM
• Annotea / Annozilla
• Armadillo
• AktiveDoc
• COHSE
• GOA
• KIM Semantic Annotation Platform
• MagPie
• Melita
• MnM
• OntoAnnotate
• Ontobroker
• OntoGloss
• ONTO-H
• Ont-O-Mat / S-CREAM / CREAM
• Ontoseek
• Pankow
• SHOE Knowledge Annotator
• Seeker

• Information Extraction Tools
• Alembic
• Amilcare / T-REX
• Annie
• Fastus
• Lasie
• Poteus
• SIFT

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Important Characteristics

• Automation of Annotation(manual / semiautomatic
  / automatic / editable)
• Ontology-related issues
• pluggable ontology (yes/no)
• ontology language (RDFS / DAMLOIL / OWL / )
• local / anywhere access
• ontology elements available for annotation
  (concept / instances / relations / triples)
• where annotations are stored (in the annotated
  document / on the dedicated server / where
  specified)
• annotation format (XML / RDF / OWL / ).
• Annotated Documents
• document kinds (text / multimedia)
• document formats (plain text / html / pdf / )
• documents access (local / web)
• Architecture / Interface / Interoperability
• Standalone tool / web interface / web component /
  API /
• Annotation Scale (large the WWW size / small -
  a hundred)
• Existing Documentation / Tutorial
• Availability

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SMORE

• Manual Annotation
• OWL-based Markup
• Simultaneous O modification (if necessary)
• ScreenScraper mines metadata from annotated pages
  and suggests as candidates for the mark-up
• Post-annotation O-based Inference

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Problems of Manual Annotation

• Expensive / Time-consuming
• Difficult / Error prone
• Subjective (two people annotating the same
  documents have in 1530 annotate them
  differently)
• Never ending
• new documents
• new versions of ontologies
• Annotation storage problem
• where?
• Trust owners annotation
• incompetence
• Spam (Google does not use ltMETAgt info)

Solution Dedicated Automatic Annotation Services
(Search Engine- like)
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Automatic O-based Annotation

• Supervised
• MnM
• S-Cream
• Melita AktiveDoc
• Unsupervised
• SemTag - Seeker
• Armadillo
• AeroSWARM

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MnM

• Ontology-based Annotation Interface
• Ontology browser (rich navigation capabilities)
• Document browser (usually Web-browser)
• The annotation is mainly based on
  select-drag-N-drop association of text fragments
  with ontology elements
• Built-in or External ML component classifies the
  main corpus of documents
• Activity Flow
• Markup (A human user manually annotate training
  set of documents by ontology elements)
• Learn (A learning algorithm is run over the
  marked up corpus to learn the extraction rules)
• Extract (An IE mechanism is selected and run over
  a set of documents)
• Review (A human user observes the results and
  correct them if necessary)

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Amilcare and T-REX

• Amilcare
• Automatic IE component
• Is used in at least five O-based A tools (Melita,
  MnM, Ontoannotate, Ontomat, SemantiK)
• Released to about 50 Industrial and Academic
  sites
• Java API
• Recently succeeded by T-REX

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Pankow

• Input A web page.
• Step 1 Web page is scanned for phrases that
  might be categorized as instances of the ontology
  (partof-speech tagger to find candidate proper
  nouns)
• Result 1 set of candidate proper nouns
• Step 2 The system iterates through all candidate
  proper nouns and all candidate ontology concepts
  to derive hypothesis phrases using preset
  linguistic patterns.
• Result 2 Set of hypothesis phrases.
• Step 3 Google is queried for the hypothesis
  phrases through
• Result 3 the number of hits for each hypothesis
  phrase.
• Step 4 The system sums up the query results to a
  total for each instance-concept pair. Then the
  system categorizes the candidate proper nouns
  into their highest ranked concepts
• Result 4 an ontologically annotated web page.

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SemTag - Seeker

• IBM-developed
• 264 million web pages
• 72 thousand of concepts (TAP taxonomy)
• 434 million automatically disambiguated semantic
  tags
• Spotting pass
• Documents are retrieved from the Seeker store,
  and tokenized
• Tokens are matched against the TAP concepts.
• Each resulting label is saved with ten words to
  either side as a window'' of context around the
  particular candidate object.
• Learning pass
• A representative sample of the data is scanned to
  determine the corpus-wide distribution of terms
  at each internal node of the taxonomy. TBD
  (taxonomy-based disambiguation) algorithm is
  used.
• Tagging pass
• Windows are scanned once more to disambiguate
  each reference determine an TAP object
• A record is entered into a database of final
  results containing the URL, the reference, and
  any other associated metadata.

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Conclusions

• Web-document A is a necessary thing
• O-based A benefits (O-based post-processing,
  unified vocabularies, etc.)
• Manual A is a bad thing
• Automatic A is a good thing
• Supervised O-based A
• Useful O-based interface for annotating training
  set
• Traditional IE tools for textual classification
• Unsupervised O-based A
• COHSE matches concept names from the ontology
  and a thesaurus against tokens from the text
• Pankow uses ontology to build candidate
  queries, then uses community wisdom to choose the
  best candidate
• SemTag uses concept names to match tokens and
  hierarchical relations in the ontology to
  disambiguate between candidate concepts for a
  text fragment

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Questions
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