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

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)

Hierarchical Model Collins & Quillian, 1969

Semantic network Collins & Loftus, 1975

Knowledge representation wCRK

Interactive semantic space

Concept Description Vectors Cobra is_aanimal is_abeast is_abeing is_abrute is_acreature is_aentity is_afauna is_aobject is_aorganism is_areptile is_aserpent is_asnake is_avertebrate hasbelly hasbody part hascell haschest hascosta hasdigit hasface hashead hasrib hastail hasthorax

Semantic Space exploration Binary dictionary search Binary dictionary search 2 20 = Binary search – not acceptable in complex semantical applications Binary search – not acceptable in complex semantical applications Semantic space can be search using context – based algorithm. Similar to word game. Semantic space can be search using context – based algorithm. Similar to word game. Concept space narrowed by subsequent user answers Concept space narrowed by subsequent user answers

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

Data aquisition How to obtain semantic data? How to obtain semantic data? Wordnet Wordnet Relations for Semantic category: animal Relations for Semantic category: animal 7543 objects and 1696 features 7543 objects and 1696 features Truncated using word popularity rank: 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

Active learning Data from wordnet: Data from wordnet: Not complete Not complete Not common sence Not common sence Sometimes specialised concepts Sometimes specialised concepts Basic dialogs for obtaining new relations Basic dialogs for obtaining new relations I give up. Tell me what did you think of? I give up. Tell me what did you think of? Tell me what is characteristic for ? Tell me what is characteristic for ? Knowledge correction : Knowledge correction :, where: W 0 – initial weight, initial knowledge ANS – answer given by user N – amount of answers β - parametr for indicating importance initial knowledge

The game Giraffe: 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 for giraffe? After entering keyword. Semantic memory is reorganised, and ready to play new games. Lion: 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 leopard] N,[is painter] N,[is puma] N,[is lynx] N, [is lynx] N. I give up. What it was? Lion … [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, [is canine] N, [is cat] Y,[is wildcat] Y, [is leopard] N, [has mane] Y, I guess it is lion.

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Experimental results How many games do we need do clarify semantic space? How many games do we need do clarify semantic space? proportion failed games N f performed to achieve first success. proportion failed games N f performed to achieve first success. The semantic memory error: The semantic memory error: where N s is 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? How it changes during learning process? Avg Density features / object Avg Density features / object

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