Semantic Memory Architecture for Knowledge Acquisition and Management

1. Semantic Memory Architecture for Knowledge Acquisition and Management Włodzisław Duch Julian Szymański

2. Semantic Memory Endel Tulving „Episodic and Semantic Memory” 1972 Semantic memory refers to the memory of meanings and understandings. It stores koncept-based, generic, context-free knowledge. One of types of long-term memory. Together with episodic memory make up the category of declarative memory. (the others are episodic and procedural) Semantic memory includes generalized knowledge that does not involve memory of a specific event. Pernament container for general knowledge (facts, ideas, words, problem solving)

3. Hierarchical Model Collins & Quillian, 1969

4. Semantic network Collins & Loftus, 1975

5. Knowledge representationwCRK

6. Interactive semantic space

7. Concept Description Vectors

8. Semantic Space exploration • Binary dictionary search 220 = 1048576 • Binary search – not acceptable in complex semantical applications • Semantic space can be search using context – based algorithm. Similar to word game. • Concept space narrowed by subsequent user answers

9. 20 questions game algorithm , where p(keyword=vi) is fraction of concepts for which the keyword has value vi Subspace of candidate concepts O(A) are selected according to: O(A) = {i; d=|CDVi-ANSW| is minimal} ,whereCDVi is a vector for i-concept and ANSW is a partial vector of retrieved answers ● we can deal with user mistakes choosing d > minimal

10. Data aquisition • How to obtain semantic data? • Wordnet • Relations for Semantic category: animal • 7543 objects and 1696 features • Truncated using word popularity rank: • IC – information content is an amount of appearances of the particular word in WordNet descriptions • GR - GoogleRank is an amount of web pages returned by Google search engine for a given word • BNC are the words statistics taken from British National Norpus. - Semantic Space reduced to 889 objects and 420 features

11. Active learning • Data from wordnet: • Not complete • Not common sence • Sometimes specialised concepts • Basic dialogs for obtaining new relations • I give up. Tell me what did you think of? • Tell me what is characteristic for <concept>? • Knowledge correction : , where: W0 – initial weight, initial knowledge ANS – answer given by user N – amount of answers β - parametr for indicating importance initial knowledge

12. The game • Giraffe: [is vertebrate] Y,[is mammal] Y, [has hoof] Y, [is equine] N, [is bovine] N, [is deer] N, [is swine] N, [has horn] N, [has horn] N,[is sheep] N,[is antelope] N,[is bison] N. System correctly guess concept giraffe. • Yuppi i’ve won! Let’s talk about giraffe. Tell me what is characteristic forgiraffe? After entering keyword. Semantic memory is reorganised, and ready to play new games. • Lion: [is vertebrate] Y, [is mammal] Y, [has hoof] N, [has paw] Y, [is canine] N, [is cat] Y, [is wildcat] Y The different way for organizing concept lion in WordNet taxonomy, causes the game goes in wrong way and system fails guess this concept: [is leopard] N, [is painter] N, [is puma] N, [is lynx] N, [is lynx] N. I give up. What it was? Lion … After giving right answer system reorganizes its knowledge and next game for searching concept lion is finished with success: [is vertebrate] Y, [is mammal] Y, [has hoof] N, [has paw] Y, [is canine] N, [is cat] Y , [is wildcat] Y, [is leopard] N, [has mane] Y, I guess it is lion.

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14. Experimental results • How many games do we need do clarify semantic space? • proportion failed games Nfperformed to achieve first success. • The semantic memory error: where Nsis amount of the games finished with success and N is total games amount, for searching first 10 concepts were 0.22 • How it changes during learning process? • Avg Density features / object

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