Bootstrapping an Ontology-based Information Extraction System

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Bootstrapping an Ontology-based Information
Extraction System

• Alexander Maedche, Günter Neumann, Steffen Staab
• (presented by D. Lonsdale)
• CS 652 June 7/04

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Overview

• Traditional IE machine learning
• Extensive use of NLP (SMES German, English,
  Japanese)
• Ontologies and related tools (OntoEdit,
  OntoBroker)
• abstract ontology lexicon
• concrete ontology
• Conclusions/reflections

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The mantra

• Lexical knowledge
• As usual, concepts are grounded in lexical items
• Extraction rules
• OntoBroker deductive, OODB, F-Logic
• Ontology
• Abstract ontology lexicon ? concrete ontology

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Lexical knowledge

• Low-level lexicons, dynamically updated
• Basic low-level NLP
• tokenization (50 classes)
• morphological processing
• POS tagging
• named entity extraction
• chunk parsing
• thematic role assignment (grammatical function)
• Cascading finite-state transducers

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The NLP component
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NLP terms

• Dependency syntax
• Chunk parsing
• Subcategorization
• Case
• Topolological fields
• PP attachment

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Dependency syntax
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Extraction

• Concept definitions
• Inference rules/axioms
• Bridging (forward inferencing)
• Syntactic dependency relations
• ...implementations of idiosyncratic syntactic
  cues for particular ontological structures...
• Logical relations (e.g. transitivity, LocatedIn)
• OntoBroker engine

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OntoEdit display (tourism)
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An abstract ontology
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A(n ontology) lexicon
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Ontology learning

• So how does ontology learning happen?
• Ontology engineer specifies, refines knowledge
  structures
• Select and process a text corpus with the model
• Use a set of different learning approaches
• ...generalized association rule learning
  algorithm...
• Extend the extracted model (all three parts...)
• Human reviews learning decisions
• The ontology is concrete, the methodology
  description less so...

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The overall approach/system
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GETESS visualization
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Conclusions/reflections

• Heavy use of NLP (good/bad)
• Fairly typical mapping of lexical items,
  concepts, relations
• Toolkit approach lingware, inferencing, GUIs
• Machine learning description is vague
• A picture is only worth a thousand words...