This document describes a commonsense question answering system that uses conceptual graphs for knowledge representation and reasoning. It uses several state-of-the-art NLP and knowledge representation tools including C&C Tools for parsing natural language into conceptual graph interchange format (CGIF), Cogitant for conceptual graph operations, and OpenCyc for commonsense knowledge. The system converts natural language sentences into conceptual graphs, uses commonsense rules from OpenCyc to augment the graphs, and can answer questions by querying the graph knowledge base. Future work includes improving the system with probabilistic reasoning and rule induction capabilities.
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Natural Intelligence - Commonsense Question Answering with Conceptual Graphs
1. Natural Intelligence -
Commonsense Question Answering
with Conceptual Graphs
Fatih Mehmet Güler and Aysenur Birturk
Department of Computer Engineering, METU
06531, Ankara/TURKEY
fmguler@gmail.com
birturk@ceng.metu.edu.tr
2. 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
3. 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)
4. Summary of the System (Cont’d)
NI
NLP KRR Commonsense
CCG CGs Open Cyc
C&C Tools Cogitant
6. 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
10. 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
11. 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
12. 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 #$Person
(disambiguation)
15. Natural Intelligence – Commonsense
Question Answering with CGs
Augment Common Sense knowledge
Modular Approach
Separation of Concerns
State of the art tools
16. Architecture - Modules
Natural Language Processing (C&C Tools are used
for implementation)
Convert natural language to CGIF
Reasoning (Cogitant library is used for
implementation)
CG operations
Common Sense (Open Cyc is used for
implementation)
Common sense mapping
Storage (Conceptual Graphs are stored in a
database)
Persistence of CGs
17. System Definition
User enters a sentence from web interface;
This sentence is converted to CGIF using the NLP module;
CGIF is converted to CGs using the reasoning module;
Support is generated to CGs using the common sense module;
Common sense rules gathered from common sense module are
applied to CGs using reasoning module;
CGs are merged to the previous ones using reasoning module;
If the input sentence is a question sentence, same operations
take place, except the resulting graph is used to query existing
CGs using the reasoning service, and if there are projections
from this query graph to previous CGs, results are displayed to
the user;
CGs are persisted using the storage module.
18. Common Sense Mapping
Cyc: (prettyString TERM STRING)
Chain up to #$Thing using #$genls relations
Same for relations using #$genlPreds
Relation hierarchies are converted to forward
rules
#$performedBy -> #$temporallyRelated
20. Conversion to Cogitant Support
Convert Cyc hierarcy to Cogitant support
format
Concept Types
Relation Types
Individuals
Rules
Convert assertions to Cogitant graph format
Apply forward rules
22. 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
23. 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
24. Conclusion
Central Integrated Common Sense QAS
CCG for Natural Language Processing
Conceptual Graphs for KRR
Cyc for Common Sense
25. 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