UserFriendly Ontology Authoring Using a Controlled Language

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User-Friendly Ontology Authoring Using a
Controlled Language

• Valentin Tablan, Tamara Polajnar
• Hamish Cunningham, Kalina Bontcheva
• NLP Research Group
• University of Sheffield
• Regent Court, 211 Portobello Street,
• Sheffield, S1 4DP, UK
• http//nlp.shef.ac.uk, http//gate.ac.uk

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Motivation

• Ontologies starting to be used in many NLP
  applications for
• encoding system knowledge
• storing results.
• Current standards (RDF-S, OWL) are complex
• Large number of features supported
• Steep learning curve
• Training required
• Authoring tools (e.g. Protégé) complicated and
  difficult to use by non-specialists.

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Motivation (continued)

• Ontological requirements for NLP applications
  usually simple
• Taxonomy of classes
• Hierarchy of properties
• Instances.
• Graphical tools difficult to embed in a
  text-based pipelines (e.g. wikis, existing NLP
  apps, other web set-ups).

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Controlled Languages

• Good compromise between structured data and
  natural language
• Feels almost natural to humans
• Can be understood by machines.
• People find it easy to put into words
  ontological information (which they may find
  difficult to do with a specialised tool).
• Used before for automating translation (e.g.
  Caterpillar and Boeing).

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Round-Trip Authoring

• Very little or no training necessary (learning by
  example).
• Can be used to extend existing ontologies or
  create new ones.
• Limited number of syntactical constructs.
• Open vocabulary.

CLIE
CL Text
Generation
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Controlled Language
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An Example

• There are pets and owners. Cat is a type of pet.
• Tabatha is a cat. John is an owner.
• Owners have pets. Pets can have textual nickname.
• John has Tabatha. Tabatha has nickname with value
  "Tabby".

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From Text to Ontologies
CL Text
Tokeniser
POS Tagger
Morph
Quote Finder
Key-phrase
NP Chunker
CLIE Parser
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Closing the Loop

• Generating CL text from ontologies
• Generate triples.
• Match triples to generation templates.
• Group similar triples.
• Generate sentences for each group of triples.

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• There are
• number"plural"/.
• There are
• number"plural"/.

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Conclusions

• Simple way of editing ontologies.
• Standards compliant (through GATEs ontology
  support I/O).
• No training required.
• Embeddable in text-only applications.
• Language could be extended to
• Better cover OWL features
• Better cover natural ways of expression.

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Thank you!

• More information
• http//gate.ac.uk
• http//nlp.shef.ac.uk