Presented at: Nature inspired Reasoning for the Semantic Web (NatuReS2008)
by Jose Quesada
The human mind has been designed to evaluate similarity fast and efficiently. When building/using a data format to make the web content more machine-friendly, can we learn something useful from how the mind represents data? We present four theories psychological theories that tried to solve the problem and how they relate to semantic web practices. Metric models (such as the vector space model and LSA) were the first-comers and still hold important advantages. advances in Bayesian methods pushed Feature models( e.g., Topics model). Structural mapping models propose that for similarity, shared structure matters more, although the formalisms that express these ideas are still developing. Transformational distance models (e.g., SP model) reduce similarity to information transmission. Topics and SP do not require preexisting classes but still have a long way to go; the need of automatically generating structure is less pressing when one of the driving forces of the semantic web is the creation of ontologies.
Keywords: cognition, information extraction, representation, semantics, similarity
Resource URI on the dog food server: http://data.semanticweb.org/workshop/natures/2008/paper/main/7
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