Towards the Semantic Web 6 Generating Ontologies for the Semantic Web: OntoBuilder R.H.P. Engles and T.Ch.Lech

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Towards the Semantic Web 6 Generating
Ontologies for the Semantic Web
OntoBuilderR.H.P. Engles and T.Ch.Lech

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• 2005. 2. 17

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1. The overall

• OntoBuilder
• Extraction of information from texts for building
  knowledge bases.
• Consist of the two modules OntoExtract and
  OntoWrapper.

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1.1 The overall architecture
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1.2 OntoExtract and OntoWrapper(1/2)

• OntoExtract
• Semi-automatic Ontology construction from
  unstructured information (natural language
  sources).
• OntoWrapper
• Semi-automatic Ontology construction from
  semi-structured and structured information
  sources.
• extract information from places on specific sites
  (e.g. names, email addresses, telephone numbers).

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1.2 OntoExtract and OntoWrapper(2/2)

• CORPORUM is dependent on a linguistic analysis of
  a given text, comprising normalization,
  tokenization and part-of-speech tagging.
• Relations between concepts are defined
  (e.g. subClassOf relations, or InstanceOf
  relations).
• Through semantic analysis of a domain, the tool
  can automatically generate relation between words
  within a domain.
• Visualization of such semantic structures can
  than be used for navigation and browsing through
  document sets.

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2. OntoExtract(1/3)

• OntoExtract supports analysis of natural language
  texts and generates lightweight, domain specific
  ontologies of these texts (utilizing already
  existing knowledge from a central data
  repository).
• OntoExtract is able to
• analysis of natural language,
• provide initial ontologies,
• refine existing ontologies,
• find relations between key terms in documents,
• find instances of concepts within document,
• finds classes, sub-class relationships.

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2. OntoExtract(2/3)

• How does OntoExtract currently work
• parses, tokenizes and analyses text,
• generates nodes and relations between them,
• enhances specific aspects of the discovered
  knowledge item using a background
  repository (containing general knowledge of the
  world, represented in Sesame),
• and the final analysis results are submitted to
  the RDFS server Sesame.

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Sesame domain knowledge
Sesame background knowledge
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3. OntoWrapper

• OntoWrapper
• deal with the analysis of structured pages
• allow the user to define XML/RDF templates,
  variables and rule sets to perform a structured
  analysis of a specific domain
• generate the merged output and sending it to the
  Sesame repository as data statements about
  specific pages.

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4.1 Generating Semantic Structures(1/2)

• Generation of semantic knowledge in information
  extraction is based upon the result of parsing
  steps that can be of varying analysis depth.
• Level of Linguistic Analysis
• Tokenization
• Lexical/Morphological Analysis
• POS tagging
• Syntactic Analysis
• Semantic/Pragmatic Analysis
• Discourse Analysis
• CORPORUMs lexical analysis includes
• text normalization, tokenization, POS tagging

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4.1 Generating Semantic Structures(2/2)

• In OntoExtract the initial analysed and annotated
  text is transformed into an internal
  representation that makes use of a variety of
  linguistic analysis steps to come to an initial
  interpretation of what is written.
• Representation contains the original text, its
  annotations, but also the resolutions performed
  on it.
• The semantic structures undergo a translation
  such a more formal representation.

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4.2 Generating Ontologies from Textual Resources

• How the translation from linguistics into
  formalisms can be done properly
• problem of representation level what knowledge
  should be represented at the ontology level/ fact
  level (what represents an instance/ concept)
• problem of dealing with the inheritance problem
• consistency between extracted ontologies and
  their truth within specific domains
• Ontologies are extracted from single documents
  taken from the web( concepts are extracted,
  created). These are set into relation with each
  other, augmented with properties and found
  instances are hooked up to them.

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4.3 Visualization and Navigation

• The exported semantic network structures and be
  run through a graph layout algorithm in order to
  generate visualizations (with CCA viewer).
• Intercluster relationships are used to navigate
  from one cluster to another by relevant concepts.

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5. Issues in Using Automated Text Extraction for
Ontology Building using IE on Web Resources

• Internet has an additional challenge
  multi-cultural background of the authors
• Generated ontologies can be used as seed
  ontologies , automatically generated from a
  variety of user defined documents.

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