User-Friendly Ontology Authoring Using a Controlled Language

Valentin Tablan, Tamara PolajnarHamish Cunningham, Kalina Bontcheva

NLP Research GroupUniversity of Sheffield

Regent Court, 211 Portobello Street,Sheffield, S1 4DP, UK

http://nlp.shef.ac.uk, http://gate.ac.uk

2LREC 2006, Genoa, Italy http://gate.ac.uk

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

CL Text

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

Generation

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Controlled LanguageNew Class There are … There are pets and owners.

Subclass_of … is a type of … Cat is a type of pet.

Cats and dogs are types of pet.

New instance … is a … Tabatha is a pet.

New object property

… [can] have … Owners have pets.

New datatype property

… [can] have textual …

Pets can have textual nickname.

Property value … has … John has Tabatha.

… has <property> … with value …

Tabatha has nickname with value “Tabby”.

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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

CLText

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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<Pet, rdf:type, owl:Class> <in> <triple id="t1"> <property ns="rdf" name="type"/> <object ns="owl" name="Class"/> </triple> </in><out> <singular> <phrase>There are <ref ref="t1.subject" number="plural"/>.

</phrase> </singular> <plural> <phrase>There are <ref ref="t1.subject" number="plural"/>. </phrase> </plural></out>

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Conclusions

Simple way of editing ontologies. Standards compliant (through GATE’s

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.

12LREC 2006, Genoa, Italy http://gate.ac.uk

Thank you!

More information:

http://gate.ac.uk

http://nlp.shef.ac.uk