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Grounding the Ontology on the Semantic Interpretation Algorithm Fernando Gomez School of Computer Science University of Central Florida Orlando, Florida

Grounding the Ontology on the Semantic Interpretation Algorithm Fernando Gomez School of Computer Science University of Central Florida Orlando, Florida

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Page 1: Grounding the Ontology on the Semantic Interpretation Algorithm Fernando Gomez School of Computer Science University of Central Florida Orlando, Florida

Grounding the Ontology on the Semantic Interpretation Algorithm

Fernando Gomez

School of Computer Science

University of Central Florida

Orlando, Florida

Page 2: Grounding the Ontology on the Semantic Interpretation Algorithm Fernando Gomez School of Computer Science University of Central Florida Orlando, Florida

Motivation

The changes to the WordNet 1.6 ontology have come about as the result of:

Defining verb predicates for most Wordnet verb classes Implementing an algorithm that uses the predicates to

determine verb meaning, semantic roles, adjuncts attach prepositional phrases and interpret deverbal nominalizations

Page 3: Grounding the Ontology on the Semantic Interpretation Algorithm Fernando Gomez School of Computer Science University of Central Florida Orlando, Florida

Defining Predicates Valid Across Domains/Corpora

The selectional restrictions in the predicates are WordNet noun ontology categories

The selectional restrictions should be valid across any domain

By “valid” is meant that the algorithm using the predicate definitions should determine verb meaning, semantic roles, etc. in most sentences selected randomly from a given corpus

Page 4: Grounding the Ontology on the Semantic Interpretation Algorithm Fernando Gomez School of Computer Science University of Central Florida Orlando, Florida

Failing to Interpret and the Ontology

Some Examples: France invaded Russia in 1812. She burned the letters. The fish hides in a crevice. Blood flew from the wound. The hurricane pushed the fleet into the rocks. She was born on a plantation at Grand Riviere. He spent money on foolish projects. She buried the money under the tree.

Page 5: Grounding the Ontology on the Semantic Interpretation Algorithm Fernando Gomez School of Computer Science University of Central Florida Orlando, Florida

Physical-Thing (entity1)

Physical-Thing(entity1) Location (location1) Physical-object (object1) Substance (substance1) Physical-Group Physical-Process -> Process Natural-Phenomenon -> Phenomenon

Page 6: Grounding the Ontology on the Semantic Interpretation Algorithm Fernando Gomez School of Computer Science University of Central Florida Orlando, Florida

Physical-Object

Physical-Object(object1,except substance1 and location1)

Physical-part (part7)

Plant-Part(plant-part1) -> ANIMATEAnimal-Body-Part(body-part1) -> ANI

Animate (life-form1) Artifact (artifact1)

Page 7: Grounding the Ontology on the Semantic Interpretation Algorithm Fernando Gomez School of Computer Science University of Central Florida Orlando, Florida

Artifact1

This concept has undergone few changes except for:

Structure1 Location Some hyponyms Organization Building1(tavern, library, hotel, restaurant …)

Examples: “The restaurant hired a new chef.” “The library has acquired 300 new books.”

Page 8: Grounding the Ontology on the Semantic Interpretation Algorithm Fernando Gomez School of Computer Science University of Central Florida Orlando, Florida

Location (Location1)

District ((district1)(territory2)) Organization

State-or-Province (state2) Organization

Country ((country1) (state3)) Organization

Continent (continent1) Organization

Residential-District Organization

(residential_district1)

Page 9: Grounding the Ontology on the Semantic Interpretation Algorithm Fernando Gomez School of Computer Science University of Central Florida Orlando, Florida

Modifications to Group1

Physical-Group is formed by all those concepts under group1 which are a collection of physical things, e.g., fleet, flora, fauna … “The hurricane pushed the fleet into the rocks.”

Social_Group1 Human-Agent

Page 10: Grounding the Ontology on the Semantic Interpretation Algorithm Fernando Gomez School of Computer Science University of Central Florida Orlando, Florida

Abstraction (abstraction6)

Possession2 (unique concept in WN) Psychological-Feature1 (unique in WN) Property (property2, property4) Relation (relation1) Space (space1) Time

Page 11: Grounding the Ontology on the Semantic Interpretation Algorithm Fernando Gomez School of Computer Science University of Central Florida Orlando, Florida

Possession (possession2)

Debt_Instrument1 (junk bond, note receivable, etc.) has been made a subconcept of Possession and Written-Communication

Some hyponyms have been extracted: territory2 (dominion, province …) and real_property1 (hacienda, plantation .)

Some concepts have been been tangled to Physical-Thing (property1, belongings, etc.)

Page 12: Grounding the Ontology on the Semantic Interpretation Algorithm Fernando Gomez School of Computer Science University of Central Florida Orlando, Florida

Communication

Act-of-Communicating (communication1, has act2 as hypernym in WN)

Something-Communicated (communication2 a hyponym of social_relation1 relation1 in WN)

Written-Communication Physical-Thing Print-Media (print_media1, a hyponym of

artifact1 in WN)

Page 13: Grounding the Ontology on the Semantic Interpretation Algorithm Fernando Gomez School of Computer Science University of Central Florida Orlando, Florida

Space (space1)

Space (space1) Mathematical-Space (space2) Empty-Area (space3) Location Outer-Space (space5) Location

Note: space3 is not a subconcept of location in WN and space5 is not a subconcept of space in WN.

Page 14: Grounding the Ontology on the Semantic Interpretation Algorithm Fernando Gomez School of Computer Science University of Central Florida Orlando, Florida

Time

Time time-continuum (time5) time-unit (time_unit1) Measure time-period (time-period1)

indefinite-period (time2) time-interval (time-interval1)

clock-time (clock-time1)

Page 15: Grounding the Ontology on the Semantic Interpretation Algorithm Fernando Gomez School of Computer Science University of Central Florida Orlando, Florida

Psychological-Feature (Psychological-Feature1)

Psychological-State state4 Cognitive-StateState4 Personal-Trait (trait1) Abstraction6

Note: These concepts have become subconcepts of psychological_feature1

Page 16: Grounding the Ontology on the Semantic Interpretation Algorithm Fernando Gomez School of Computer Science University of Central Florida Orlando, Florida

Quantity (quantity2)

Mathematical-Quantity (quantity3, a hyponym of psychological-feature in

WN) Measure

Measure-Quantum (measure3) Measurement (measure1) Magnitude-relation (magnitude-relation1)

Page 17: Grounding the Ontology on the Semantic Interpretation Algorithm Fernando Gomez School of Computer Science University of Central Florida Orlando, Florida

Process(process2)

Physical-Process Physical-Thing Natural-Process

Cognitive-Process Psychological-Feature Unconscious-Process (process5) Psychoanalytic-Process

Note: Process5, and Cognitive-Process have Psychological-Feature as hypernym in WN, not Process, while Psychoanalytic-Process has only process

Page 18: Grounding the Ontology on the Semantic Interpretation Algorithm Fernando Gomez School of Computer Science University of Central Florida Orlando, Florida

Unique Upper-Level Concepts

Physical-Thing (entity1) Abstraction (abstraction6) Action (action1) State-R (state4) Event (event1) Process (process2) Phenomenon (phenomenon1)

Page 19: Grounding the Ontology on the Semantic Interpretation Algorithm Fernando Gomez School of Computer Science University of Central Florida Orlando, Florida

Conclusions

We have explained some reorganizations and changes to the WN 1.6 upper-level ontology

These modifications have been dictated by a semantic interpretation algorithm

These modifications are within the principles that inspire WN noun ontology and can be easily integrated within it.