Natural Intelligence -Commonsense Question Answeringwith Conceptual Graphs Fatih Mehmet Güler and Aysenur Birturk Department of Computer Engineering, METU 06531, Ankara/TURKEY firstname.lastname@example.org email@example.com
Motivation Massive Knowledge found as Natural Language Text based Question Answering (no tagging) Open Domain Question Answering Address Commonsense Reasoning Problem Linguistically motivated KRR Intelligence is the accumulation of knowledge Integrate State of the Art Tools Ultimate goal: Getting closer to strong AI
Summary of the System Natural Language is parsed Utterances are represented using CGs Concepts and Relation types are mapped to Cyc equivalent counterparts Type hierarchies are computed Knowledge is accumulated If the input is a question Search for answer (projection)
Summary of the System (Cont’d) NI NLP KRR Commonsense CCG CGs Open Cyc C&C Tools Cogitant
Combinatory Categorial Grammar (CCG) Lexicalized Theory of Grammar based on Categorial Grammar ( Steedman 2001). Functions can be applied or composed Arguments can be picked up or turned into functors (Type raising) Easy for Semantic Representations Small number of semantically transparent combinatory rules to combine CCG categories. Assign semantic representations to the lexical entries Interpret combinatory rules
C&C Tools Linguistically Motivated Large-Scale NLP with C&C and Boxer. (Curran, Clark, Bos, 2007) C&C Parser POS Tagging, Supertagging Parsing, Chunking Named Entity Recognition Boxer Uses CCG parser output Generates DRS Semantic Representations Freely available for research http://svn.ask.it.usyd.edu.au/trac/candc/wiki
C&C Tools Large Scale NLP is possible with C&C and Boxer C&C Parser: state of the art parser for CCG Boxer: Semantic representations in DRS
Open Cyc Open source version of Cyc system Cyc: greatest effort to encode Common Sense knowledge in machine processable way 500.000 concepts 26.000 relations and 5.000.000 assertions CycL language similar to Lisp We use Cyc to map parsed words to common sense counterparts such as person to #$Person (disambiguation)
Natural Intelligence – CommonsenseQuestion Answering with CGs Augment Common Sense knowledge Modular Approach Separation of Concerns State of the art tools
Architecture - Modules Natural Language Processing (C&C Tools are used for implementation) Convert natural language to CGIF Reasoning (Cogitant library is used for implementation) CG operations Common Sense (Open Cyc is used for implementation) Common sense mapping Storage (Conceptual Graphs are stored in a database) Persistence of CGs
System Definition User enters a sentence from web interface; This sentence is converted to CGIF using the NLP module; CGIF is converted to CGs using the reasoning module; Support is generated to CGs using the common sense module; Common sense rules gathered from common sense module are applied to CGs using reasoning module; CGs are merged to the previous ones using reasoning module; If the input sentence is a question sentence, same operations take place, except the resulting graph is used to query existing CGs using the reasoning service, and if there are projections from this query graph to previous CGs, results are displayed to the user; CGs are persisted using the storage module.
Common Sense Mapping Cyc: (prettyString TERM STRING) Chain up to #$Thing using #$genls relations Same for relations using #$genlPreds Relation hierarchies are converted to forward rules #$performedBy -> #$temporallyRelated
Significance Sentences like; What are the intangible things in this situation? Was Mr. Hyde there while eating the apples? Does Mr. Hyde exist after eating the apples? Do the apples exist after Mr. Hyde ate them? Deep Natural Language Understanding State of the art tools Open domain question answering
Difficulties Open Cyc API is broken Does not work in Turkish locale (fixes are sent to maintainers) Still, provided API sends one IP packet per character, way too slow over network Custom socket API is developed and used over TCP Custom Lisp functions for generalization hierarchy and concept mapping Cogitant problematic Java API is very limited (compared to C++) Only works over XML files
Conclusion Central Integrated Common Sense QAS CCG for Natural Language Processing Conceptual Graphs for KRR Cyc for Common Sense
Future Work Implement Rule Induction Backward Chaining (Resolution) Improve NLP module and Common Sense mapping Probabilistic Reasoning Question Answering System (QAS) to be used in; Education (Learning Management Systems) Semantic Search (Content Management Systems) Intelligent Help
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