[PPT] - Case study: GATE in the NeOn project Diana Maynard University of PowerPoint Presentation

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University of Sheffield, NLP

Case study: GATE in the NeOn project

Diana Maynard University of Sheffield

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University of Sheffield, NLP

Aims of this talk

Demonstrates using GATE for automating SW-

specific tasks such as semantic annotation and

ntology learning from texts
SARDINE: pattern-based relation extraction in

the fisheries domain

Adding new concepts and instances to the
ntology
Finding relations between existing concepts in

the ontology

SPRAT: generic version of SARDINE

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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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University of Sheffield, NLP

Linking the Text to the Ontology

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University of Sheffield, NLP

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
dd sofas…..

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University of Sheffield, NLP

Wall climbing sofa

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University of Sheffield, NLP

Sofa made from bicycle seats

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University of Sheffield, NLP

FAO Case Study

Actually, it’s nothing to do with sofas, or any kind of

seating.

They do, however, have an Ontology-driven stock
ver-fishing alert system
Focuses on agricultural sector and information

management for hunger prevention

Case study aims at management of alerts to avoid
ver-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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University of Sheffield, NLP

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}, …

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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 Ontology

The new classes found are linked to existing classes in the
ntology
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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University of Sheffield, NLP

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
ntology, then matching is done between the two at the

root level.

Text is annotated with features containing the root and
riginal string(s)
When new elements are added to the ontology, these

features can be used to regenerate alternative forms

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University of Sheffield, NLP

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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University of Sheffield, NLP

Finding relations between known elements

In this case study, we use existing information from the
ntology 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
f 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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SARDINE demo

Running SARDINE from NeOn toolkit and

visualising results

Showing relation finding in GATE
Viewing results with ANNIC

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University of Sheffield, NLP

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

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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