Lecture 18 Ontologies and Wordnet

Lecture 18 Ontologies and Wordnet

Topics Topics Ontologies Wordnet Overview of Meaning

Readings:Readings: Text 13.5

NLTK book Chapter 2

March 25, 2013

CSCE 771 Natural Language Processing

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OverviewOverviewLast Time (Programming)Last Time (Programming)

Chunking Chunking with NLTK HW 5 Project Ideas

TodayToday app.ChunkParser under NLTK

Readings: Readings: Chapter 7 http://www.nltk.org/howto http://nltk.googlecode.com/svn/trunk/doc/howto/wordnet.html

Next Time:Next Time:

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Ontologies – the old meaningOntologies – the old meaning

http://www.merriam-webster.com/dictionary/ontology

1.1.: a branch of metaphysics concerned with the nature : a branch of metaphysics concerned with the nature and relations of being and relations of being

2.2.: a particular theory about the nature of being or the : a particular theory about the nature of being or the kinds of things that have existence kinds of things that have existence

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Ontologies – the new (CS) meaningOntologies – the new (CS) meaning

http://en.wikipedia.org/wiki/Ontology_(information_science)

““In In computer science and and information science, an ontology , an ontology formally represents knowledge as a set of concepts formally represents knowledge as a set of concepts within a within a domain, and the relationships between pairs of , and the relationships between pairs of concepts.”concepts.”

"Toward Principles for the Design of Ontologies Used for "Toward Principles for the Design of Ontologies Used for Knowledge Sharing" by Knowledge Sharing" by Tom Gruber 1993 1993

• ““An ontology is a formal, explicit specification of a An ontology is a formal, explicit specification of a shared conceptualization.”shared conceptualization.”

http://en.wikipedia.org/wiki/Ontology_(information_science)

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Gruber elaboratingGruber elaborating

"An ontology is a description (like a formal "An ontology is a description (like a formal specification of a program) of the concepts and specification of a program) of the concepts and relationships that can formally exist for an agent or a relationships that can formally exist for an agent or a community of agents. This definition is consistent community of agents. This definition is consistent with the usage of ontology as set of concept with the usage of ontology as set of concept definitions, but more general. And it is a different definitions, but more general. And it is a different sense of the word than its use in philosophy."sense of the word than its use in philosophy."[8] Gruber 2001 “2001 “

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Focus Levels of OntologiesFocus Levels of Ontologies

GenericGeneric

CoreCore

DomainDomain

TaskTask

ApplicationApplication

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Examples of in-use OntologiesExamples of in-use Ontologies

MedicalMedical

• UMLS

• SNOMED-RT, ,

• GALEN, GALEN,

• MEDLINE

LinguisticsLinguistics

• Wordnet Miller Princeton 1990sWordnet Miller Princeton 1990s

• Gold Gold http://linguistics-ontology.org/

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Early OWL versionsEarly OWL versions

OWL provides three increasingly expressive OWL provides three increasingly expressive sublanguagessublanguages

1.1. OWL LiteOWL Lite supports those users primarily needing a supports those users primarily needing a classification hierarchy and simple constraintsclassification hierarchy and simple constraints

2.2. OWL DLOWL DL supports those users who want the supports those users who want the maximum expressiveness while retainingmaximum expressiveness while retaining computational completeness (all conclusions are

guaranteed to be computable) and decidability (all computations will finish in finite time).

3.3. OWL FullOWL Full is meant for users who want maximum is meant for users who want maximum expressiveness and the syntactic freedom of RDF expressiveness and the syntactic freedom of RDF with no computational guaranteeswith no computational guarantees

http://www.w3.org/TR/2004/REC-owl-features-20040210/#s1.3

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Owl 2.0Owl 2.0

The OWL 2 Web Ontology Language, informally OWL 2, The OWL 2 Web Ontology Language, informally OWL 2, is an ontology language for the Semantic Web with is an ontology language for the Semantic Web with formally defined meaning. formally defined meaning.

OWL 2 ontologies provide classes, properties, OWL 2 ontologies provide classes, properties, individuals, and data values and are stored as individuals, and data values and are stored as Semantic Web documents. Semantic Web documents.

OWL 2 ontologies can be used along with information OWL 2 ontologies can be used along with information written in RDF, and OWL 2 ontologies themselves written in RDF, and OWL 2 ontologies themselves are primarily exchanged as RDF documents.are primarily exchanged as RDF documents.

http://www.w3.org/TR/owl2-overview/

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Owl 2 relationships to other languagesOwl 2 relationships to other languages

http://www.w3.org/TR/owl2-overview/#Semantics

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ontology tools - Editorsontology tools - Editors

Editors – protégé Editors – protégé http://protege.stanford.edu/

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Semantic WebSemantic Web

Web – static web pages + Web – static web pages +

Web 2.0 - Web 2.0 - http://en.wikipedia.org/wiki/Web_2.0 ~1999 ~1999

Semantic WebSemantic Web

"The Semantic Web is not a separate Web but an extension of the "The Semantic Web is not a separate Web but an extension of the current one, in which information is given well-defined current one, in which information is given well-defined meaning, better enabling computers and people to work in meaning, better enabling computers and people to work in cooperation." It is a source to retrieve information from the web cooperation." It is a source to retrieve information from the web (using the web spiders from RDF files) and access the data (using the web spiders from RDF files) and access the data through Semantic Web Agents or Semantic Web Services. through Semantic Web Agents or Semantic Web Services. Source: "The Semantic Web" by Source: "The Semantic Web" by Tim Berners-Lee, , James Hendler, and , and Ora Lassila, , Scientific American, 2001

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Basic NLTK Corpus FunctionalityBasic NLTK Corpus FunctionalityExample Descriptionfileids() the files of the corpusfileids([categories]) the files of the corpus corresponding to these categoriescategories() the categories of the corpuscategories([fileids]) the categories of the corpus corresponding to these filesraw() the raw content of the corpusraw(fileids=[f1,f2,f3]) the raw content of the specified filesraw(categories=[c1,c2]) the raw content of the specified categorieswords() the words of the whole corpuswords(fileids=[f1,f2,f3]) the words of the specified fileidswords(categories=[c1,c2]) the words of the specified categoriessents() the sentences of the whole corpussents(fileids=[f1,f2,f3]) the sentences of the specified fileidssents(categories=[c1,c2]) the sentences of the specified categoriesabspath(fileid) the location of the given file on diskencoding(fileid) the encoding of the file (if known)open(fileid) open a stream for reading the given corpus fileroot() the path to the root of locally installed corpusreadme() the contents of the README file of the corpus

Reference: NLTK Book Chapter 2

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More from Chapter 2 of NLTK BookMore from Chapter 2 of NLTK Book

2.2   Conditional Frequency Distributions2.2   Conditional Frequency Distributions• Conditions and Events• Counting Words by Genre• Plotting and Tabulating Distributions• Generating Random Text with Bigrams

2.3   More Python: Reusing Code2.3   More Python: Reusing Code• Functions• Modules

2.4   Lexical Resources2.4   Lexical Resources• Wordlist Corpora• A Pronouncing Dictionary• Comparative Wordlists• Shoebox and Toolbox Lexicons

2.5   WordNet2.5   WordNetReference: NLTK Book Chapter 2

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WordnetWordnet

George Miller Princeton UniversityGeorge Miller Princeton University

NLTK includes the English WordNet, with 155,287 words NLTK includes the English WordNet, with 155,287 words and 117,659 synonym setsand 117,659 synonym sets

Links:Links:

• http://en.wikipedia.org/wiki/WordNet

• http://wordnet.princeton.edu/

• http://nltk.googlecode.com/svn/trunk/doc/howto/wordnet.html

Reference: NLTK Book Chapter 2

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WordNetWordNet

WordNet distinguishes between WordNet distinguishes between nouns, , verbs, , adjectives and and adverbs—it does not include —it does not include prepositions, determiners etc. prepositions, determiners etc.

Every synset contains a group of synonymous words Every synset contains a group of synonymous words or collocations or collocations

Different senses of a word are in different synsets. Different senses of a word are in different synsets.

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Nouns in WordnetNouns in Wordnet

hypernymshypernyms: : YY is a hypernym of is a hypernym of XX if every if every XX is a (kind is a (kind of) of) YY ( (caninecanine is a hypernym of is a hypernym of dogdog, because every , because every dog is a member of the larger category of canines)dog is a member of the larger category of canines)

hyponymshyponyms: : YY is a hyponym of is a hyponym of XX if every if every YY is a (kind of) is a (kind of) XX ( (dogdog is a hyponym of is a hyponym of caninecanine))

coordinate termscoordinate terms: : YY is a coordinate term of is a coordinate term of XX if if XX and and YY share a hypernym (share a hypernym (wolfwolf is a coordinate term of is a coordinate term of dogdog, , and and dogdog is a coordinate term of is a coordinate term of wolfwolf))

holonymholonym: : YY is a holonym of is a holonym of XX if if XX is a part of is a part of YY ( (buildingbuilding is a holonym of is a holonym of windowwindow))

meronymmeronym: : YY is a meronym of is a meronym of XX if if YY is a part of is a part of XX ((windowwindow is a meronym of is a meronym of buildingbuilding))

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Verbs in WordnetVerbs in Wordnet

hypernymhypernym: the verb : the verb YY is a hypernym of the verb is a hypernym of the verb XX if the if the activity activity XX is a (kind of) is a (kind of) YY ( (to perceiveto perceive is an hypernym is an hypernym of of to listento listen))

troponymtroponym: the verb : the verb YY is a troponym of the verb is a troponym of the verb XX if the if the activity activity YY is doing is doing XX in some manner ( in some manner (to lispto lisp is a is a troponym of troponym of to talkto talk))

entailmententailment: the verb : the verb YY is entailed by is entailed by XX if by doing if by doing XX you you must be doing must be doing YY ( (to sleepto sleep is entailed by is entailed by to snoreto snore))

coordinate termscoordinate terms: those verbs sharing a common : those verbs sharing a common hypernym (hypernym (to lispto lisp and and to yellto yell))

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Adjectives/Adverbs in WordnetAdjectives/Adverbs in Wordnet

AdjectivesAdjectives

• related nounsrelated nouns

• similar tosimilar to

• participle of verbparticiple of verb

AdverbsAdverbs

• root adjectivesroot adjectives

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Knowledge Structure Example Knowledge Structure Example defined by hypernym or defined by hypernym or IS AIS A relationships relationships

Example:Example:

dog, domestic dog, Canis familiaris dog, domestic dog, Canis familiaris

=> canine, canid => canine, canid

=> carnivore => carnivore

=> placental, placental mammal, eutherian mammal => placental, placental mammal, eutherian mammal

=> mammal => mammal

=> vertebrate, craniate => vertebrate, craniate

=> chordate => chordate

=> animal, animate being, beast, brute, creature, fauna => animal, animate being, beast, brute, creature, fauna

=> ... => ...

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Hypernym/HyponymHypernym/Hyponym

Inverse relationsInverse relations

Hyponym == ISAHyponym == ISA

Hypernym == “contains the subset”Hypernym == “contains the subset”

ExamplesExamples

• car is a hyponym of vehicle car is a hyponym of vehicle vehicle is a hypernym of car vehicle is a hypernym of car

• Dog is hyponym of animal Dog is hyponym of animal animal is a hypernym of dog animal is a hypernym of dog

• Sometimes superordinate used instead of hypernymSometimes superordinate used instead of hypernym

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WordNet as an ontologyWordNet as an ontology

Hyponym == ISAHyponym == ISA

Meronymy – part of relationMeronymy – part of relation

wheel part of car wheel part of car wheel is meronymy of car wheel is meronymy of car

Holnym inverse of meronymyHolnym inverse of meronymy

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Senses and SynonymsSenses and Synonyms

>>> from nltk.corpus import wordnet as wn >>> from nltk.corpus import wordnet as wn

>>> wn.synsets('motorcar') >>> wn.synsets('motorcar')

[Synset('car.n.01')][Synset('car.n.01')]

one meaning the first(01) noun sense(n) of carone meaning the first(01) noun sense(n) of car

>>> wn.synset('car.n.01').lemma_names >>> wn.synset('car.n.01').lemma_names

['car', 'auto', 'automobile', 'machine', 'motorcar']['car', 'auto', 'automobile', 'machine', 'motorcar']

synonymous words (or "lemmas")synonymous words (or "lemmas")

Reference: NLTK Book Chapter 2

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Definitions and examplesDefinitions and examples

>>> wn.synset('car.n.01').definition >>> wn.synset('car.n.01').definition

'a motor vehicle with four wheels; usually propelled 'a motor vehicle with four wheels; usually propelled by an internal combustion engine' by an internal combustion engine'

>>> wn.synset('car.n.01').examples >>> wn.synset('car.n.01').examples

[['he needs a car to get to work']'he needs a car to get to work']

Reference: NLTK Book Chapter 2

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>>> wn.synsets('car') >>> wn.synsets('car')

[Synset('car.n.01'), Synset('car.n.02'), Synset('car.n.03'), [Synset('car.n.01'), Synset('car.n.02'), Synset('car.n.03'), Synset('car.n.04'), Synset('cable_car.n.01')] Synset('car.n.04'), Synset('cable_car.n.01')]

>>> for synset in wn.synsets('car'): >>> for synset in wn.synsets('car'):

... print synset.lemma_names ... print synset.lemma_names

... ...

['car', 'auto', 'automobile', 'machine', 'motorcar'] ['car', 'auto', 'automobile', 'machine', 'motorcar']

['car', 'railcar', 'railway_car', 'railroad_car'] ['car', 'railcar', 'railway_car', 'railroad_car']

['car', 'gondola'] ['car', 'gondola']

['car', 'elevator_car'] ['car', 'elevator_car']

['cable_car', 'car']['cable_car', 'car']Reference: NLTK Book Chapter 2

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The WordNet HierarchyThe WordNet Hierarchy

Hypernyms (up) Hypernyms (up)

Hyponyms (down)Hyponyms (down)

Meronyms- Meronyms- componentscomponents

holonyms - things they holonyms - things they are contained inare contained in

Reference: NLTK Book Chapter 2

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Synonyms and LemmasSynonyms and Lemmas

>>> motorcar = wn.synset('car.n.01') >>> motorcar = wn.synset('car.n.01')

>>> types_of_motorcar = motorcar.hyponyms() >>> types_of_motorcar = motorcar.hyponyms()

>>> types_of_motorcar[26] Synset('ambulance.n.01') >>> types_of_motorcar[26] Synset('ambulance.n.01')

>>> sorted([lemma.name for synset in >>> sorted([lemma.name for synset in types_of_motorcar for lemma in synset.lemmas])types_of_motorcar for lemma in synset.lemmas])

['Model_T', 'S.U.V.', 'SUV', 'Stanley_Steamer', ['Model_T', 'S.U.V.', 'SUV', 'Stanley_Steamer', 'ambulance', 'beach_waggon', … ]'ambulance', 'beach_waggon', … ]

Reference: NLTK Book Chapter 2

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Meronyms and HolonymsMeronyms and Holonyms

>>> wn.synset('tree.n.01').part_meronyms() >>> wn.synset('tree.n.01').part_meronyms() [Synset('burl.n.02'), Synset('crown.n.07'), [Synset('burl.n.02'), Synset('crown.n.07'), Synset('stump.n.01'), Synset('trunk.n.01'), Synset('stump.n.01'), Synset('trunk.n.01'), Synset('limb.n.02')] Synset('limb.n.02')]

>>> wn.synset('tree.n.01').substance_meronyms() >>> wn.synset('tree.n.01').substance_meronyms() [Synset('heartwood.n.01'), Synset('sapwood.n.01')] [Synset('heartwood.n.01'), Synset('sapwood.n.01')]

>>> wn.synset('tree.n.01').member_holonyms() >>> wn.synset('tree.n.01').member_holonyms() [Synset('forest.n.01')][Synset('forest.n.01')]

Reference: NLTK Book Chapter 2

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>>> for synset in wn.synsets('mint', wn.NOUN): >>> for synset in wn.synsets('mint', wn.NOUN):

... print synset.name + ':', synset.definition ... print synset.name + ':', synset.definition

... ...

batch.n.02: (often followed by `of') a large number or amount or batch.n.02: (often followed by `of') a large number or amount or extent extent

mint.n.02: any north temperate plant of the genus Mentha with mint.n.02: any north temperate plant of the genus Mentha with aromatic leaves and small mauve flowers aromatic leaves and small mauve flowers

mint.n.03: any member of the mint family of plants mint.n.03: any member of the mint family of plants

mint.n.04: the leaves of a mint plant used fresh or candied mint.n.04: the leaves of a mint plant used fresh or candied

mint.n.05: a candy that is flavored with a mint oil mint.n.05: a candy that is flavored with a mint oil

mint.n.06: a plant where money is coined by authority of the mint.n.06: a plant where money is coined by authority of the governmentgovernment

Reference: NLTK Book Chapter 2

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EntailmentsEntailments

walking entails stepping walking entails stepping

>>> wn.synset('walk.v.01').entailments() >>> wn.synset('walk.v.01').entailments() [Synset('step.v.01')][Synset('step.v.01')]

>>> wn.synset('eat.v.01').entailments() >>> wn.synset('eat.v.01').entailments() [Synset('swallow.v.01'), Synset('chew.v.01')] [Synset('swallow.v.01'), Synset('chew.v.01')]

>>> wn.synset('tease.v.03').entailments() >>> wn.synset('tease.v.03').entailments() [Synset('arouse.v.07'), Synset('disappoint.v.01')][Synset('arouse.v.07'), Synset('disappoint.v.01')]

Reference: NLTK Book Chapter 2

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AntonymsAntonyms

Reference: NLTK Book Chapter 2

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Semantic SimilaritySemantic Similarity>>> right = wn.synset('right_whale.n.01') >>> right = wn.synset('right_whale.n.01')

>>> orca = wn.synset('orca.n.01') >>> orca = wn.synset('orca.n.01')

>>> minke = wn.synset('minke_whale.n.01') >>> minke = wn.synset('minke_whale.n.01')

>>> tortoise = wn.synset('tortoise.n.01') >>> tortoise = wn.synset('tortoise.n.01')

>>> novel = wn.synset('novel.n.01') >>> novel = wn.synset('novel.n.01')

>>> right.lowest_common_hypernyms(minke) >>> right.lowest_common_hypernyms(minke) [Synset('baleen_whale.n.01')] [Synset('baleen_whale.n.01')]

>>> right.lowest_common_hypernyms(orca) >>> right.lowest_common_hypernyms(orca)

[Synset('whale.n.02')] [Synset('whale.n.02')]

>>> right.lowest_common_hypernyms(tortoise) >>> right.lowest_common_hypernyms(tortoise) [Synset('vertebrate.n.01')] [Synset('vertebrate.n.01')]

>>> right.lowest_common_hypernyms(novel) >>> right.lowest_common_hypernyms(novel)

[Synset('entity.n.01')][Synset('entity.n.01')]

Reference: NLTK Book Chapter 2

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Generality/Specificity and DepthGenerality/Specificity and Depth

>>> wn.synset('baleen_whale.n.01').min_depth() >>> wn.synset('baleen_whale.n.01').min_depth()

1414

>>> wn.synset('whale.n.02').min_depth() >>> wn.synset('whale.n.02').min_depth()

13 13

>>> wn.synset('vertebrate.n.01').min_depth() >>> wn.synset('vertebrate.n.01').min_depth()

8 8

>>> wn.synset('entity.n.01').min_depth() >>> wn.synset('entity.n.01').min_depth()

00

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Similarity Scores from Right WhaleSimilarity Scores from Right Whale

>>> right.path_similarity(minke) >>> right.path_similarity(minke)

0.25 0.25

>>> right.path_similarity(orca) >>> right.path_similarity(orca)

0.16666666666666666 0.16666666666666666

>>> right.path_similarity(tortoise)>>> right.path_similarity(tortoise)

0.076923076923076927 0.076923076923076927

>>> right.path_similarity(novel) >>> right.path_similarity(novel)

0.0434782608695652160.043478260869565216

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Googlecode - HowToGooglecode - HowTo

http://nltk.googlecode.com/svn/trunk/doc/howto/http://nltk.googlecode.com/svn/trunk/doc/howto/wordnet.html wordnet.html

WordNet InterfaceWordNet Interface

>>> from nltk.corpus import wordnet as wn>>> from nltk.corpus import wordnet as wn

Reference: http://nltk.googlecode.com/svn/trunk/doc/howto/wordnet.html