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Knowledge Structure

Knowledge Structure. Vijay Meena (99005027) Gaurav Meena (00005020). Introduction. Human being involve a lot of common sense - A child is always youngr than his mother - You can push something with a straight stick But computer fails to recognize them

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Knowledge Structure

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  1. Knowledge Structure Vijay Meena (99005027) Gaurav Meena (00005020)

  2. Introduction • Human being involve a lot of common sense - A child is always youngr than his mother - You can push something with a straight stick But computer fails to recognize them We can write programs that exceeds the capability of experts, but yet we cant write program that match the level of a three years old child at recognizing objects.

  3. Common sense knowledge • Computer lacks common sense • Why has it been so hard to give computer this common sense ? - involves a great amount of knowledge • Many kind of representation. • AI has become the gold mine of techniques • We don’t want to give computers knowledge about particular areas, but instead want to give common sense.

  4. Common sense is problem of great scale and diversity Two parts 1. How to give millions of piece of knowledge to computer? - need a database. 2. How to give computers the capacity for common sense reasoning? - having database is not enough - don’t have enough idea about how to represent, organize and use common sense knowledge

  5. Some projects • CyC – Started by Douglous lelant in 1994 • WordNet – Started by Fellbaum in 1998 • ConceptNet - Started by MIT Media LabTeam after WorldNet

  6. CyC - CyCorp • To create the world’s first true AI having both common sense and the ability to reason about it • CyC represents it’s common knowledge in a language call CyCL. • CyCL is a second order logical language with second order features such as quantification over predicates.

  7. WordNet • WordNet is an online lexical reference system • Have semantic network representation • Nodes have lexical notation • English nouns, verbs, adjectives and adverbs are organized into synonym sets, each representing one underlying lexical concept.

  8. Concept Net • Very large semantic network of common sense knowledge. • Structured as a network of semi structured natural language fragment. • Captures a wide range of common sense concept and relations. • Ease of use camparable to WordNet.

  9. ConceptNet • Presently consists of 250,000 elements of common sense knowledge. • Combined the best of both CyC and WordNet. • Extended Sementic network representation of WordNet.

  10. WordNet lexical notation of node Small ontology of semantic relations ConceptNet Conceptual notation of node Richer set of relation appropiate to concept level node WordNet v/s ConceptNet At present there are 19 semantic relations used in ConceptNet representing different categories.

  11. Representation of Knowledge • Logic representation : unambigious • Natutal language representation : ambigious • Maintaining some ambiguity lends us greater flexibility. • Methodology for reasoning over natural language fragment

  12. Concept Net Focus on the Knowledge representation aspects of ConceptNet

  13. Origin • machine-usable resource mined OMCS • CRIS mined predicate argument structures from OMCS • Is produced by an automatic process which applies a set of ‘common sense extraction rules’ to the semi-structured English sentences of the OMCS corpus.

  14. Structure Semi-Structured natural-language fragment nodes falls into three general classes : • Noun Phrases (things, places, people) • Attributes (modifiers) • Activity Phrases (actions and actions compounded with a noun phrase or prepositional phrase)

  15. Grammer for partially structuring natural language cncepts

  16. Semantic Relation Types currently in ConceptNet • Category • Things • Events • Actions • Saptial • Goals • Functions • Generic

  17. Methodology for Reasoning over Natural Language Concepts • Computing conceptual similarity • Flexible Inference: context finding, inference chaining, conceptual analogy.

  18. Conclusion • To support several kinds of practical inferences over text • To maintain an easy-to-use knowledge representation • ConceptNet follows the easy-to-use semantic network structure of WordNet, but incorporates a greater diversity of relations and concepts inspired by Cyc.

  19. References • www.openmind.org • www.conceptnet.org • http://www.cogsci.princeton.edu/~wn/ • Focusing on ConceptNet's natural language knowledge representation, Liu, H. & Singh, P. (2004)

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