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Owlizr

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Fariz Darari
Fariz Darari

This paper describes a system for representing knowledge from Bahasa Indonesia text using an ontology written in Web Ontology Language Description Logic (OWL DL). The system takes natural language text as input, analyzes it semantically, generates ontology instances and properties, and can answer queries by reasoning over the ontology. It combines prior work on Indonesian natural language processing and using description logics for knowledge representation. An evaluation demonstrates the system representing and reasoning over sample texts about economic activities.

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Knowledge Representation System for Bahasa Indonesia Based on Web Ontology Language Description Logic (OWL DL) Fariz Darari Adila Alfa Krisnadhi Hisar Maruli Manurung Faculty of Computer Science Universitas Indonesia ICACSIS 2010 Download paper: ir.cs.ui.ac.id/publication/2010/owlizr.pdf
Outline Background Knowledge Representation Architecture Evaluation Results Conclusions
Challenge ? KNOWLEDGE TEXT
Why? Automated Knowledge Representation & Reasoning!! We always love automated things: Automated Door Automated Teller Machine Automated Motorbike
Goal KNOWLEDGE TEXT
Background
Previous Works Larasati's Model a syntactic and semantic processing system for QA in Bahasa Mahendra's Extend the system with the axiom addition, e.g., NLP axiom & world knowledge axiom
NLP and Event Representation Reification Turning non-object thing into object Using Neo-davidsonian approach, with thematic roles (agent, patient, theme, time, location)
Description Logic Consists of two components: Previous research by Franconi with KODIAK, representing knowledge from natural language using Description Logic
OWLizr Knowledge Representation
Event Modelling Event Patient Agent Action hasAgent hasAction hasPatient
Event Modelling (cont) event_1 car budi buy_action hasAgent hasAction hasPatient “ Budi buys a car” or “Budi membeli mobil”
Background Knowledge
DL Model Class: Thing, PhysicalObject, AbstractObject, LivingPhysicalObject, NonLivingPhysicalObject Class Definition
DL Model (cont)
OWLizr Architecture
Architecture
Two Modes Knowledge-assertion Mode NLP Semantic Analyzer -> KB Generator -> KB Reasoner Query Mode NLP Semantic Analyzer -> SPARQL Query Generator
NLP Semantic Analyzer Reusing Mahendra's program Analysis using syntax-driven semantic analysis with lambda calculus Divided into 4 parts (Lexicon, Grammar, Lexical Semantics, Semantic Attachment Rules) Example: “ Pabrik memproduksi mobil” or “The factory produces the car” becomes [location(x5,pabrik), event(x1,memproduksi), agent(x1,x5), patient(x1,x6), objectx(x6,mobil)]
KB Generator Transforms semantic notations into OWL Implemented in Java with library Protege-OWL API Two main functions, instance and property assertions
Example Instance Assertion: from [location(x5,pabrik), event(x1,memproduksi), agent(x1,x5), patient(x1,x6), objectx(x6,mobil)] to Factory(factory_1), Event(event_1), Car(car_1) Property Assertion:
Instance assertion
Property Assertion
KB Reasoner Two Main Uses: Consistency Checking Check if there is a contradiction, for example “Mobil membeli radio” or “The car buys the radio” will produce an error. But, of course people can buy the radio. Instance Checking Function to obtain inferred knowledge. It will check whether an instance could be classified in some classes or not.
SPARQL Query Generator Translates semantic notations into SPARQL Query Formed by two components, SELECT and WHERE clause Then, execute query on KB
Example
Evaluation Results
Evaluation Serves as a proof-of-concept Using a specific, domain ontology , that is economic activity Example terms are " price " or "harga", " expensive " or "mahal", " buy " or "membeli", " sell " or "menjual", " buyer " or "pembeli", and " shop " or "toko" The evaluation tests the ontology features , such as subclass, intersection, union. So, we define each terms in the ontology using various ontology features. For example, buyer is defined as:
Knowledge Assertion Mode Input: “Anto buys the car in the shop” or “Anto membeli mobil di toko” NLP Semantic Analyzer: [person(x6,anto), event(x4,membeli), agent(x4,x6), patient(x4,x3), objectx(x3,mobil), di(x4,x1), location(x1,toko)] Asserted Knowledge (Instance):
Assertion Mode (cont) Asserted Knowledge (Property)
Assertion Mode (cont) Inferred Knowledge
Query Mode Input: “Who buys the car?” or “Siapa yang membeli mobil?” NLP Semantic Analyzer: [ans(x7), person(x5,x7), event(x4,membeli), agent(x4,x5), patient(x4,x2), objectx(x2,mobil)] SPARQL Query Form: ans(x7) -> SELECT ?x7 person(x5,x7), event(x4,membeli), agent(x4,x5), patient(x4,x2), objectx(x2,mobil ) -> WHERE { ?event :hasAction :buy_action . ?event :hasAgent ?x7 . ?event :hasPatient ?ins . ?ins rdf:type :Car }
Conclusions This research works as a bridge between NLP (Mahendra's) and DL (Franconi's) The research value is in the formalization attempt to natural language text The resulting knowledge can be shared and reused across the web Next , implementing TBox assertion and increasing OWL version to OWL 2

More Related Content

Owlizr

  • 1. Knowledge Representation System for Bahasa Indonesia Based on Web Ontology Language Description Logic (OWL DL) Fariz Darari Adila Alfa Krisnadhi Hisar Maruli Manurung Faculty of Computer Science Universitas Indonesia ICACSIS 2010 Download paper: ir.cs.ui.ac.id/publication/2010/owlizr.pdf
  • 2. Outline Background Knowledge Representation Architecture Evaluation Results Conclusions
  • 4. Why? Automated Knowledge Representation & Reasoning!! We always love automated things: Automated Door Automated Teller Machine Automated Motorbike
  • 7. Previous Works Larasati's Model a syntactic and semantic processing system for QA in Bahasa Mahendra's Extend the system with the axiom addition, e.g., NLP axiom & world knowledge axiom
  • 8. NLP and Event Representation Reification Turning non-object thing into object Using Neo-davidsonian approach, with thematic roles (agent, patient, theme, time, location)
  • 9. Description Logic Consists of two components: Previous research by Franconi with KODIAK, representing knowledge from natural language using Description Logic
  • 11. Event Modelling Event Patient Agent Action hasAgent hasAction hasPatient
  • 12. Event Modelling (cont) event_1 car budi buy_action hasAgent hasAction hasPatient “ Budi buys a car” or “Budi membeli mobil”
  • 14. DL Model Class: Thing, PhysicalObject, AbstractObject, LivingPhysicalObject, NonLivingPhysicalObject Class Definition
  • 18. Two Modes Knowledge-assertion Mode NLP Semantic Analyzer -> KB Generator -> KB Reasoner Query Mode NLP Semantic Analyzer -> SPARQL Query Generator
  • 19. NLP Semantic Analyzer Reusing Mahendra's program Analysis using syntax-driven semantic analysis with lambda calculus Divided into 4 parts (Lexicon, Grammar, Lexical Semantics, Semantic Attachment Rules) Example: “ Pabrik memproduksi mobil” or “The factory produces the car” becomes [location(x5,pabrik), event(x1,memproduksi), agent(x1,x5), patient(x1,x6), objectx(x6,mobil)]
  • 20. KB Generator Transforms semantic notations into OWL Implemented in Java with library Protege-OWL API Two main functions, instance and property assertions
  • 21. Example Instance Assertion: from [location(x5,pabrik), event(x1,memproduksi), agent(x1,x5), patient(x1,x6), objectx(x6,mobil)] to Factory(factory_1), Event(event_1), Car(car_1) Property Assertion:
  • 24. KB Reasoner Two Main Uses: Consistency Checking Check if there is a contradiction, for example “Mobil membeli radio” or “The car buys the radio” will produce an error. But, of course people can buy the radio. Instance Checking Function to obtain inferred knowledge. It will check whether an instance could be classified in some classes or not.
  • 25. SPARQL Query Generator Translates semantic notations into SPARQL Query Formed by two components, SELECT and WHERE clause Then, execute query on KB
  • 28. Evaluation Serves as a proof-of-concept Using a specific, domain ontology , that is economic activity Example terms are " price " or "harga", " expensive " or "mahal", " buy " or "membeli", " sell " or "menjual", " buyer " or "pembeli", and " shop " or "toko" The evaluation tests the ontology features , such as subclass, intersection, union. So, we define each terms in the ontology using various ontology features. For example, buyer is defined as:
  • 29. Knowledge Assertion Mode Input: “Anto buys the car in the shop” or “Anto membeli mobil di toko” NLP Semantic Analyzer: [person(x6,anto), event(x4,membeli), agent(x4,x6), patient(x4,x3), objectx(x3,mobil), di(x4,x1), location(x1,toko)] Asserted Knowledge (Instance):
  • 30. Assertion Mode (cont) Asserted Knowledge (Property)
  • 31. Assertion Mode (cont) Inferred Knowledge
  • 32. Query Mode Input: “Who buys the car?” or “Siapa yang membeli mobil?” NLP Semantic Analyzer: [ans(x7), person(x5,x7), event(x4,membeli), agent(x4,x5), patient(x4,x2), objectx(x2,mobil)] SPARQL Query Form: ans(x7) -> SELECT ?x7 person(x5,x7), event(x4,membeli), agent(x4,x5), patient(x4,x2), objectx(x2,mobil ) -> WHERE { ?event :hasAction :buy_action . ?event :hasAgent ?x7 . ?event :hasPatient ?ins . ?ins rdf:type :Car }
  • 33. Conclusions This research works as a bridge between NLP (Mahendra's) and DL (Franconi's) The research value is in the formalization attempt to natural language text The resulting knowledge can be shared and reused across the web Next , implementing TBox assertion and increasing OWL version to OWL 2