University of Sheffield, NLP

Case study: GATE in the NeOn project

Diana Maynard

University of Sheffield

University of Sheffield, NLP

Aims of this talk

• Demonstrates using GATE for automating SW-specific tasks such as semantic annotation and ontology learning from texts

• SARDINE: pattern-based relation extraction in the fisheries domain

• Adding new concepts and instances to the ontology

• Finding relations between existing concepts in the ontology

• SPRAT: generic version of SARDINE

University of Sheffield, NLP

Recap: IE for the Semantic Web

• Traditional IE is based on a flat structure, e.g. recognising Person, Location, Organisation, Date, Time etc.

• For the Semantic Web, we need information in a hierarchical structure

• Idea is that we attach annotations to the documents, pointing to concepts in an ontology

• Information can be exported as an ontology annotated with instances

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Linking the Text to the Ontology

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The NeOn project

• NeOn (Networking Ontologies) is a 4-year 14.7 million Euro EU project involving 14 European partners.

• Focus on using ontologies for large-scale semantic applications in distributed organizations

• Handles multiple networked ontologies that exist in a particular context, are created collaboratively, and might be highly dynamic and constantly evolving.

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ODd SOFAS

• The Food and Agricultural Organisation of the UN have odd sofas…..

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Wall climbing sofa

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Sofa made from bicycle seats

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FAO Case Study• Actually, it’s nothing to do with sofas, or any kind of

seating.

• They do, however, have an Ontology-driven stock over-fishing alert system

• Focuses on agricultural sector and information management for hunger prevention

• Case study aims at management of alerts to avoid over-fishing in already stretched global waters

• Role of GATE is to analyse textual resources to find new information such as new fish names, and relations between ontology elements, e.g. “Atlantic cod are fished in the Gulf of Maine”

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SARDINE

Species Annotation, Recognition and Indexing of Named Entities

• SARDINE identify mentions of fish species from text

• It identifies

– existing fish names listed in the ontology and their morphological variants

– potential new fish names not listed in the ontology

– potential relations between fish names

• For the new fish, it attempts to classify them in the ontology, based on linguistic information such as synonyms and hyponyms of existing fish

• It may generate properties also for existing fish in the ontology

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Using patterns to find new fish

Synonyms:– mummichogs (fundulus heteroclitus)

Names appearing in lists:– “plankton, herring and clams....”

– “clams, herring and other types of fish”

More specific fish names: – Japanese flounder

– Red salmon

– Suberites sponges

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Example of JAPE rule (1)

Example: “Suberites sponges” (where “sponge” is a known class)

Rule: AdjClass

(

({Token.category == JJ})

({Class}):super

):sub

-->

:sub.SardineSubclass = {rule=AdjClass},

:super.SardineSuperclass = {rule=AdjClass},

…

……

University of Sheffield, NLP

Example of JAPE rule (2)

Example: “Frogs are a kind of amphibian.”

Rule:Subclass1

(

({NP}):sub

( {Lookup.minorType == be}

{Token.category == DT}

{Lookup.majorType == kind}

)

({NP}):super

) -->

……

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Annotated text in GATE

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Augmenting the OntologyThe new classes found are linked to existing classes in the ontology

For existing fish, and new fish which we identified as a

synonym or hyponym of an existing fish, the link is to an

existing ontology instance

When we don't identify a link to any existing fish, we create

a new concept

The changes to the ontology are stored and can be

verified later by human experts

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Generated “animal” ontology

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Recognising components from the ontology

• In addition to the standard IE components, we use some special ontology components.

• The OntoRootGazetteer enables us to match words or phrases in the text with classes, instances or properties in an ontology, as any morphological variant

• Morphological analysis is performed on both text and ontology, then matching is done between the two at the root level.

• Text is annotated with features containing the root and original string(s)

• When new elements are added to the ontology, these features can be used to regenerate alternative forms

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Modifying the ontology• We developed a special GATE plugin called NEBOnE

(Named Entity Based ONtology Editor)

• This reuses technology taken from CLOnE (Controlled Language ONtology Editor)

• CLOnE is designed to create new classes, instances etc from raw (controlled) text generated by the user

• NEBOnE enables changes to be made to the ontology based on information extraction from input texts (e.g. web pages) in natural language

• Morphological analysis enables both root forms and variants to be added to the ontology (as properties), along with other variants (e.g. capitalisation)

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Finding relations between known elements• In this case study, we use existing information from the

ontology to find relations between them. e.g. fish species -- gear type

• We have already annotated all fish species, gear types, fishing areas and so on in the text, based on ontology lookup

• JAPE grammar first finds the subject of the document (a gear type) and adds the information as a document feature

• When a species name is found, we create a new annotation for the relation “gear_used”, with a property denoting the species, and another property denoting the ID number of the gear.

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Viewing relations

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Using ANNIC to view results

• By running our application on a Lucene datastore, we can then use ANNIC to view the results

• Search for the pattern consisting of the name of the relation annotation (in this case “gear_used”)

• Show the relevant features (species, gear ID, gear type)

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Using ANNIC to view results

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SPRAT

• Semantic Pattern Recognition and Annotation Tool

• This is a generic version of SARDINE that runs on all kinds of texts, not just fisheries

• Does not require a seed ontology

• Useful for building a domain ontology from scratch

• Tested on wikipedia pages

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How well can we do it?

• Traditional NE recognition on news texts: ~90% precision/recall

• Ontology-based information extraction on news texts: ~80% precision/recall

• Pattern-based relation extraction on Wikipedia texts: high accuracy but low recall (or vice versa depending on setup)

• Relation finding between known entities: ~90% precision/recall

University of Sheffield, NLP

More information

Neon Project: http://www.neon-project.org

Neon Toolkit is freely available: http://www.neon-toolkit.org

SARDINE application can be downloaded from the GATE website

http://gate.ac.uk/projects/neon/sardine