Linguistic enrichment of ontologies: a glance to the role of previously existing linguistic resources Maria Teresa Pazienza, Armando Stellato {pazienza,stellato}@info.uniroma2.it

Linguistic enrichment of ontologies: a glance to Linguistic enrichment of ontologies: a glance to the role of previously existing linguistic the role of previously existing linguistic

resourcesresources

Maria Teresa Pazienza, Armando Stellato{pazienza,stellato}@info.uniroma2.it

ART group, Dept. of Computer Science, Systems and Production

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MotivationMotivation

Ontologies provide vocabularies through which agents in the Semantic Web will be able to communicate

– Every specific ontology bears its semantics, which is specified

by:

• the interpretation given by people using the ontology inside a given

framework

• the consistent use that applications make of ontological knowledge

How can we recognize if and when these constraints are considered? Or, at least…

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Role of Natural LanguageRole of Natural Language

What information can both humans and machines rely on? …natural language

• Natural Language is the last exploitable resource– …to convey data semantics

• It helps humans in understanding how formal objects relate to their world knowledge

• It may help machines in harmonizing different conceptualizations

– Pros and cons:• Pros: it offers a rich and universally accepted mean for express meaning

• Cons: it is ambiguous; phenomena like synonymy and homonymy must be taken in consideration

• Possible exploitations for a linguistically motivated approach to ontology development:

– Provides useful linguistic anchors for improving knowledge sharing efforts– Strengthens relationships between ontology and raw textual information (for tasks

like information extraction, ontology population etc…)– Enhances knowledge understanding and reuse even for humans

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Enriching ontologies withEnriching ontologies withlexical informationlexical information

• Possible scenarios for linguistic enrichment:

– Explicit Linguistic Enrichment

Ontology

Linguistic

Resource

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– Producing Multilingual Ontologies

Ontology

Bilingual Linguistic

Resource

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– LexicoSemantic Enrichment of Ontologies

Craftsman

Employee

event Academic

...

Technician Administrative...

ProfessorResearcher ...

…

Ontology Linguistic Resource with aSemantic structure (e.g WordNet)

Worker

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Exploiting Linguistic ResourcesExploiting Linguistic Resources

• Different Linguistic Resources (LRs) are available on the Web

• These resources differentiate upon:– Trustworthiness: from free initiatives to coordinated research

projects

– Complexity: quantity and quality of detailed information, adopted

model, morphology…

– Representation: no standard for representation of linguistic

resources

– Implementation: available as databases, huge xml repositories,

proprietary text formats etc..

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Tools for Linguistic Enrichment: Tools for Linguistic Enrichment: RequirementsRequirements

• (possibly) embedded in ontology editing applications

• Browsing different linguistic resources

• Providing functionalities for:

– Querying LRs with terms from ontology

– Enriching ontology concepts with linguistic information

– Synonyms

– Rich textual descriptions

– Translations in different languages

– Semantic Indexes from LR

– Supporting ontology development by reusing semantic

information from linguistic resources (when available)

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InfrastuctureInfrastucture

The Linguistic

Watermark– Offers a

classification of

different LRs

– Provides API

for accessing

their content

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The Linguistic


classification of

different LRs

– Provides API

for accessing

their content

WordNetWordNethttp://wordnet.princeton.edu/

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The Linguistic


classification of

different LRs

– Provides API

for accessing

their content

FreelangFreelanghttp://www.freelang.net/

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The Linguistic


classification of

different LRs

– Provides API

for accessing

their content

DictDicthttp://www.dict.org/bin/Dict

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OntoLing: a OntoLing: a tool for semi-automatic tool for semi-automatic linguistic enrichment of ontologieslinguistic enrichment of ontologies

• Deployed as a plug-in for the popular ontology editing tool Protégé ( http://protege.stanford.edu/ then go plugins -> OntoLing )

• Exploits the Linguistic Watermark API for accessing LRs• Support linguistic enrichment of ontologies and ontology development

Linguistic

Browser

Ontology

Browser

GUI

Facade

Linguistic Interface

<<interface>>

Protégé API

Ontoling Core

Ontoling

Architecture

Different resources may be plugged and recognized at run time, by inspection of their Linguistic Watermark

Wordnet 1.7

WordnetInterface

<<Implementation>>

FreeDictInterface

<<Implementation>>

…Interface

<<Implementation>>

Wordnet 2.1

Wordnet 2.0

Italian Hungarian

EnglishItalian

EnglishDanish ... ……

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…synonyms……semantic pointers to the LR…Linguistic Metadata for…Concepts documentation

Search linguistic expressions inside the LR

Explore semantic relationships which characterize the LR

…and linguistic relationships

Integration between ontology and linguistic resource: search ontology terms inside the linguistic resource

Assist ontology creation by extracting portions of knowledge from the LR

Linguistic Enrichment of the Ontology

Ontology concepts bear a greater linguistic expressivity: this helps in identifying similarities with other conceptualizations.

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Adaptive behaviour andAdaptive behaviour andGraphic User InterfaceGraphic User Interface

• Linguistic Resources may be loaded into OntoLing at run time

• Upon initialization they declare themselves and their specific Linguistic

Watermark

• OntoLing understands their capabilities and rearranges its Linguistic

Browser according to properties and characteristics exhibited by the LR

• Different functionalities for enriching ontologies with content from the loaded

LR are also activated depending on its watermark

• Support to semiautomatic enrichment also takes into consideration which ki

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Dynamic FunctionalitiesDynamic Functionalities

• The Linguistic Watermark provides a generic interface which embraces typical LR configurations and structures

• Three methods act as service providers, in that they allow the definition of functionalities dedicated to the exploration of particular aspects of a given LR

– exploitSearchMethod

– exploreSemanticRelation

– exploreLexicalRelation

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Representing Linguistic Information Representing Linguistic Information inside Ontologiesinside Ontologies

• Standard Protégé Model

– Use of meta-classes

• Linguistic-class

• Linguistic-slot

– A terminology slot (one for

each language) for

indicating synonyms

– Frame Documentation Slot

• Protégé-OWL

– Use of standard rdfs

properties:

• rdfs:label to indicate

synonyms (also specifying

the language)

• rdfs:comment to provide

documentation about

ontology objects

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Summarizing Summarizing

• attention paid to formal conceptual representation in the Semantic Web is not being matched by an equivalent interest on how this information will be made easily accessible by humans, and by machines not sharing any form of semantic commitment.

• A wider and deeply aware adoption of Natural Language in representing knowledge could fill this gap

• We developed infrastructures and a tool for:– General framework for describing different kind of LRs

– provide functionalities for accessing their content

– enriching ontologies with information from LR

– Support a “linguistically aware ontology development”

• Future Work:– Integrate as many lexical resources as possible!

– Include interfaces for accessing and exploiting other kind of linguistic resources (e.g. Framenet)

– Establish more complex connections between lexical resources and ontologies

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Automatic Lexico-Semantic Automatic Lexico-Semantic Enrichment (LSE) of OntologiesEnrichment (LSE) of Ontologies

• Objective:

– identify pointers (lexico-semantic anchors) from ontological objects to

semantic entities (e.g. synsets, for WordNet) of a linguistic resource

• Through:

– Observed linguistic/semantic similarities between the ontology and the

Linguistic Resource (LR) exploited for enrichment

• Exploitable Linguistic Watermarks:

– ConceptualizedLR

– At least one from:

• TaxonomicalLR

• LRWithGlosses

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Automatic Lexico-Semantic Automatic Lexico-Semantic Enrichment (LSE) of OntologiesEnrichment (LSE) of Ontologies

Intuition behind the strategy:

If a semantic pointer links a frame-synset pair <F,S>

Then other frame-synset pairs (where the frame is more specific/more

generic than F and the synset is narrower/broader than S) have a good

probability of being linked through a semantic pointer

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Automatic LSE of Ontologies:Automatic LSE of Ontologies:the Frameworkthe Framework

• O: space of ontological objects, called Frames (classes, properties,

individuals)

• L: space of semantic indexes (semex) in the LR

• Plausibility Matrix MP (defined over a O×L space)

– MP(i,j) represents the plausibility that the ontological object i be matched with

the semantic index j

• Evidence Matrix ME (defined over a O×L space)

– contains in each element ME(i,j) the set of evidences which contribute to the

computation of element MP(i,j) in the Plausibility Matrix.

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• Discovery Phase– Objective: reduce the dimension of the L space

– Process: find candidate (lexical) anchors between elements in O

and elements in L, through:

• Search filtered by String similarity measures

• Exploitation of Translation and/or Synonyms vocabularies (possibly the LR itself)

– Output:

• LA L (all synsets bound by candidate anchors)

– Notes:• Maximize recall

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Semantic Enrichment function:

Implemented through:

– Extraction of semantic/linguistic similarity evidences ME

– Computation of MP

Due to mutual dependencies between evidences for different candidate anchors:

and:

: 0..1se Af O L

( )se sef f t

( ) , ( 1), (0)P E P PM t f M M t M

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Legenda:

– candidate pair : < f, s > (< frame, semex >)

with: f O ; s LA

where: p(f,s,0) ≠ 0.

– Smarter notation for plausibility:

( , , ) ( , ) with ( )def

P P Pp f s t M f s M M t

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Implementing Implementing ffsese

• Guidelines

1. prizing candidate pairs characterized by positive

evidences.

2. punishing candidate pairs characterized by negative

evidences

3. evaluate quantitative factors associated to different

kind of evidences (representing the strength, or

presence, of the evidence)

4. take into account inherent ambiguity (polysemy) of

every label associated to ontology concepts

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0 01

1 0

1 1 , 1

( )1

1 1 1 , 1

n

ii

m

ii

p t p

p t

tp


Plausibility

at time = 0

Plausibility threshold

for an anchorto be confirmed

Plausibility threshold

for an anchorto be discarded

Ambiguity (polysemy) of term bounding synset to frame

Plausibilityat time t

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0 01

1 0

1 1 , 1

( )1

1 1 1 , 1

n

ii

m

ii

p t p

p t

tp


Plausibility

at time = 0


Weight related to single evidence at

time t

Positive EvidencesContribution

Negative EvidencesContribution

Plausibility

at time = 0


Normalizationfactor

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Extracting evidencesExtracting evidences (1)(1)

Establishing proper context for each type of frame and for each type of evidence

computeConceptualSphere(Frame frm, int DepthRange) SET OF Frameinput

frm: the class, property or individual which has been selected for linguistic enrichmentDepthRange: the number of allowed hops along the IS-A relation for retrieving super concepts of frm

outputConceptualSphere: the conceptual sphere surrounding frm

beginFrameType type getOntoType(frm)SET OF Frame ConceptualSphere {}if (type = class or type = property)

ConceptualSphere ConceptualSphere getSuperConcepts(frm, DepthRange)else //frm is an instance

Classes getClasses(frm)for each class Classes do

ConceptualSphere ConceptualSphere {class} getSuperConcepts(class, DepthRange)

end forend ifif (type = class)

for each property p, class c | frm.hasRestriction(p,c) or c.harRestriction(p,frm) doConceptualSphere ConceptualSphere { c } { p }

if (type = instance)for each property p ( frm.getOwnRelationalProperties() ) do

ConceptualSphere ConceptualSphere { p } frm.getOwnPropertyValues(p)end ifif (type = property)

for each class c ( domain(frm) range(frm) ) doConceptualSphere ConceptualSphere {class}

end ifreturn ConceptualSphere

end

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Examined evidences

– Analysis of Taxonomical alignment

• ConceptualSphere (context) := the transitive closure of the IS-A

relationship in the ontology (and hyponymy relation for LRs)

• Requirements: TaxonomicalLR compliant Linguistic Resource

– Analysis of glosses from the LR

• ConceptualSphere := depends on frame type (see example in

previous slide)

• Requirements: LRWithGlosses compliant Linguistic Resource

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Evidences based on Taxonomical Alignment

Reflect alignment between the respective structures of the ontology and the

linguistic resource exploited for enrichment

Captured taxonomy patterns may have positive as well as negative influence

over the plausibility of a given < frame, semex > pair

Positive Evidence Negative Evidence

FH SH

FL SL

IS-A

semantic pointer

pair candidate for asemantic pointer

ONT LR

FL SL

IS-A

ONT LR

candidate pair candidate pairSHFH

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, sgn , , 1i TAt p frame semex t

Weighting coefficient for

Taxonomy Alignment

sign

Plausibility at step t-1 of frame/semex

pair closing the alignment square

Evidences based on Taxonomical Alignment

Reflect alignment between the respective structures of the ontology and the

linguistic resource exploited for enrichment

Captured taxonomy patterns may have positive as well as negative influence

over the plausibility of a given < frame, semex > pair

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Evidences extracted through Analysis of Glosses

Glosses bear a lot of semantic information; it is not formally explicited, but,

once unveiled, can provide useful hints on how to properly match ontology

concepts and linguistic expressions

Gloss Analysis generates three kind of evidences, provided by:

• glosses which contain linguistic reference to concepts expressed in the ontology and which are semantically related to the concept being enriched

• glosses which contain linguistic reference to concepts which at least exist in the ontology

• linguistic overlap between glosses of synsets which are candidate to enrich related concepts

Next slides: examples for enrichment of baseball ontology from:

http://www.daml.org/2001/08/baseball/baseball-ont

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Ontology Linguistic Resource

Division

League

Noun.7741947

Gloss:A league ranked by quality; ”he played baseball in class D…

rdf triple: League division Division

GlossRelateds,League,prop(class,domain),1

,i GRv t MatchingLevel

Glosses containing linguistic reference to Glosses containing linguistic reference to semantically related conceptssemantically related concepts

for each Frame rc ConceptualSphere doMtchLvl match(rc, gloss), if MtchLvl 0 Evidences Evidences evd(GR, rc, MtchLvl) end ifend for

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Noun.179011

Gloss: A score in baseball made by a runnertouching all four bases safely;

"the Yankees scored 3 runs in the bottom of the 9th";"their first tally came in the 3rd inning"

Glosses containing linguistic reference to Glosses containing linguistic reference to concepts which exist in the ontologyconcepts which exist in the ontology

for each term t gloss do Frame rc find(Ontology, t, MtchLvl), if rc null Evidences Evidences evd(GG, rc, MtchLvl) end ifend for


Run

Inning

Inning O

GlossGeneral,Inning,1

,i GGv t MatchingLevel

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Noun. 7009602

series that constitutes theplayoff for the baseball championship

Overlap between glosses of synsets which Overlap between glosses of synsets which are candidate to enrich related conceptsare candidate to enrich related concepts

for each Frame rfi ConceptualSphere do for each synset sij candidateSynsets(rfi) do let rfgloss[i,j] sj.getGloss() end for for each term t, t gloss and t rfgloss[i,j] let freq = LR.getGlossFrequency(t) if !filter(freq) Evidences Evidences evd(GO, rfi, si, freq) end if end forend for


WorldSeries

home

rdf triple: WorldSeries home Team

Noun. 3399133

(baseball) base consisting of a rubber slab where the batter stands

GlossOverlap,baseball, home-noun.3399133,1

, , , 1i GOv t MatchingLevel object synset t

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Testing our frameworkTesting our framework

Experimental setup:

Fine tuning of evidence-typed σ-parameters has been performed over a

collection of several small ontologies and/or portions of them

Two ontologies used for testing, WordNet used for enrichment in both cases:

1. BASEBALL ontology ( http://www.daml.org/2001/08/baseball/baseball-ont )

– Original version in DAML+OIL and converted to OWL

– 78 classes, 26 properties and 13 individuals

– 75,3% of ambiguous concepts, average ambiguity ~9,16

– Inter-annotator agreement = 98.76% (one contrasting decision out of the whole oracle)

2. MOSES Ontology about university ( http://www.mondeca.com/owl/moses/ita.owl )

– developed in the context of the EU funded project MOSES (IST-2001-37244)

– built, in OWL language, over a pre-existing DAML ontology, and finalized for representing the

Italian university domain

– 192 classes, 122 properties

– 73,1% of ambiguous concepts, average ambiguity ~5,23

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Experimental resultsExperimental results

Detailed analysis of the test data on the first experiment revealed that,

though only 40% of the original corpus (ontology) has been correctly

enriched, another 50% contains the right choice as first (but still under

acceptance threshold), second or third in order of plausibility

Ontology Precision Recall

Baseball Ont 80% 39,5%

Moses Italian 81,48% 42,72%

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ConclusionsConclusions

• attention paid to formal conceptual representation in the Semantic Web is not being matched by an equivalent interest on how this information will be made easily accessible by humans, and by machines not sharing any form of semantic commitment.

• A wider and deeply aware adoption of Natural Language in representing knowledge – or, at least, support knowledge representation – could fill this gap

• We defined a first framework for:– describing LRs (under an “operational point of view”) and for enriching ontologies with their

content

– (Semi)Automatically enrich the content of ontologies with information from linguistic resources

• Future work:– Large scale (ontologies) testing!

– Improving glosses processing (pos tagging, shallow parsing…)

– Development of new techniques for multilingual ontology enrichment (possibly exploiting more than one LR at a time)

– Embedding all these techniques inside existing frameworks for ontology editing

ReferencesReferences

Maria Teresa Pazienza, Armando Stellato An Environment for Semi-automatic Annotation of Ontological Knowledge with Linguistic Content 3rd European Semantic Web Conference (ESWC 2006) Budva, Montenegro, June 11-14, 2006

Maria Teresa Pazienza, Armando Stellato Exploiting Linguistic Resources for building linguistically motivated ontologies in the Semantic Web Second Workshop on Interfacing Ontologies and Lexical Resources for Semantic Web Technologies (OntoLex2006), held jointly with LREC2006 ,Magazzini del Cotone Conference Center, Genoa, Italy, 24-26 May 2006

Maria Teresa Pazienza, Armando Stellato Linguistic Enrichment of Ontologies: a methodological framework Second Workshop on Interfacing Ontologies and Lexical Resources for Semantic Web Technologies (OntoLex2006), held jointly with LREC2006 ,Magazzini del Cotone Conference Center, Genoa, Italy, 24-26 May 2006

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References References

Maria Teresa Pazienza, Armando Stellato Linguistically motivated Ontology Mapping for the Semantic Web SWAP 2005, the 2nd Italian Semantic Web Workshop Trento, Italy, December 14-16, 2005

Maria Teresa Pazienza, Armando Stellato The Protégé Ontoling Plugin - Linguistic Enrichment of Ontologies in the Semantic Web 4th International Semantic Web Conference (ISWC-2005) Galway, Ireland, November, 2005

Armando Stellato, Michele Vindigni, Fabio Massimo Zanzotto XeOML: An XML-based extensible Ontology Mapping Language Workshop on Meaning Coordination and Negotiation, held in conjunction with 3rd International Semantic Web Conference (ISWC-2004) Hiroshima, Japan, November 8, 2004

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Thanks for your attention….

see you in Roma for Aiia07 congress

http://aiia.info.uniroma2.it

Documents

Linguistic enrichment of ontologies: a glance to the role of previously existing linguistic resources Maria Teresa Pazienza, Armando Stellato {pazienza,stellato}@info.uniroma2.it