Ontology-Driven Information Retrieval

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Ontology-DrivenInformation Retrieval
ICDL 2004, New Dehli, India

• Nicola Guarino
• Laboratory for Applied Ontology
• Institute for Cognitive Sciences and Technology
  (ISTC-CNR)
• Trento-Roma, Italy

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Summary

• The role of ontologies in (generalized) IR
• Semantic Matching
• Increasing precision
• Increasing recall
• Problems with WordNet
• The role of Foundational Ontologies

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The key problem

• Semantic matching

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Simple queries need more knowledge about what
the user wants

• Search for Washington (the person)
• Google 26,000,000 hits
• 45th entry is the first relevant
• Noise places
• Search for George Washington
• Google 2,200,00 hits
• 3rd entry is relevant
• Noise institutions, other people, places

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Solution ontologysemantic markup

• Ontology
• Person
• George Washington
• George Washington Carver
• Place
• Washington, D.C.
• Artifact
• George Washington Bridge
• Organization
• George Washington University
• Semantic disambiguation/markup of questions and
  corpora
• What Washington are you talking about?

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The role of taxonomy and lexical knowledge

• Search for Artificial Intelligence Research
• Misses subfields of the general field
• Misses references to AI and Machine
  Intelligence (synonyms)
• Noise non-research pages, other fields

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Solution

• Extra knowledge
• Ontology Sub-fields (of AI)
• Knowledge Representation
• Machine Vision etc.
• Neural networks
• Lexicon Synonyms (for AI)
• Artificial Intelligence
• Machine Intelligence
• Techniques
• Query Expansion
• Add disjuncted sub-fields to search
• Add disjuncted synonyms to search
• Semantic Markup of question and corpora
• Add general terms (categories)
• Add synonyms

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Ontology-driven search engines

• Idealized view
• Ontology-driven search engines act as virtual
  librarians
• Determine what you really mean
• Discover relevant sources
• Find what you really want
• Requires common knowledge on all ends
• Semantic linkage between questioning agent,
  answering agent and knowledge sources
• Hence the Semantic Web?

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Two main roles of ontologies in IT

• Semantic Interoperability
• Generalized database integration
• Virtual Enterprises, Concurrent Engineering
• e-commerce
• Web services
• Information Retrieval
• Documents
• Facts (query answering)
• Products
• Services
• Not mentioning IS analysis and design

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The role of linguistic ontologiescoupled with
structured representation formalisms

• Why not just simple thesauri based on fixed
  keyword hierarchies?
• Datas intrinsic dynamics needs to keep track of
  new terms
• Need of understanding a rigid set of terms
• Heterogeneous descriptions need a broad-coverage
  vocabulary
• Linguistic ontologies with structured
  representation formalisms
• Decouple user vocabulary from data vocabulary
• Increase recall (synonyms, generalizations)
• Increase precision (disambiguation, ontology
  navigation)
• Further increase precision (by capturing the
  structure of queries and data)

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Using WordNet as an ontology

• Unclear semantic interpretation of hyperonimy
• Instantiation vs. subsumption
• Object-level vs. meta-level
• Hyperonymy used to account for polysemy
• (law both a document and a rule)
• Unclear taxonomic structure
• Glosses not consistent with taxonomic structure
• Heterogeneous leves of generality
• Formal constraints violations (especially
  concerning roles)
• Polysemous use of antonymy (child/parent vs.
  daughter/son)
• Poor ontology of adjectives and qualities
• Shallow taxonomy of verbs

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When subtle distinctions are important

• Trying to engage with too many partners too fast
  is one of the main reasons that so many online
  market makers have foundered. The transactions
  they had viewed as simple and routine actually
  involved many subtle distinctions in terminology
  and meaning
• Harvard Business Review, October 2001

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Ontologies and intended meaning
Ontology
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Levels of Ontological Precision
game(x) ? activity(x) athletic game(x) ?
game(x) court game(x) ? athletic game(x) ? ?y.
played_in(x,y) ? court(y) tennis(x) ? court
game(x) double fault(x) ? fault(x) ? ?y.
part_of(x,y) ? tennis(y)
game athletic game court game tennis
outdoor game field game football
tennis football game field game court
game athletic game outdoor game
Axiomatized theory
Taxonomy
game NT athletic game NT court game RT
court NT tennis RT double fault
Glossary
DB/OO scheme
Catalog
Thesaurus
Ontological precision

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Ontology Quality Precision and Coverage
Low precision, max coverage
BAD
Max precision, low coverage
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Why precision is important
MD(L)
False agreement!
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DOLCEa Descriptive Ontology for Linguistic and
Cognitive Engineering

• Strong cognitive bias descriptive (as opposite
  to prescriptive) attitude
• Emphasis on cognitive invariants
• Categories as conceptual containers no deep
  metaphysical implications wrt true reality
• Clear branching points to allow easy comparison
  with different ontological options
• Rich axiomatization

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DOLCEs basic taxonomy
Quality Physical Spatial location Temporal
Temporal location Abstract Abstract Quali
ty region Time region Space region Color
region
Endurant Physical Amount of matter Physical
object Feature Non-Physical Mental
object Social object Perdurant Static Stat
e Process Dynamic Achievement Accomplishmen
t
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Research priorities at LOA-CNR

• Foundational ontologies and ontological analysis
• Domain ontologies
• Physical objects
• Information and information processing
• Social interaction
• Ontology of legal and financial entities
• Ontology, language, cognition
• Ontology-driven information systems
• Ontology-driven conceptual modeling
• Ontology-driven information access
• Ontology-driven information integration

www.loa-cnr.it