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Celebrities are influential nodes in Social Network Services (SNSs) whose domain classification can assist researchers to better analyze normal users' interests. In this paper, we propose an automatic classification method that extracts the celebrity's name and description as the input and the associated domain can be mapped to a domain ontology by performing Spreading activation on the knowledge base and reasoning on the domain ontology. The knowledge base is merged from YAGO ( Wikipedia and WordNet) and WordNet Domain ontology. We fetch 15,879 celebrity profiles, map them to WordNet Domain ontology, and demonstrate a satisfactory result of the mapping ratio. [ABSTRACT FROM PUBLISHER]

For cognitive agents that decide based on experiences, memory retrieval is a significant issue. In this article a contribution to this field is made by introducing the concept of psychic spreading activation for memory retrieval in an autonomous agent. Using the psychoanalytical foundation of that as a constraint, a variant of spreading activation was created to regard perceptional as well as motivational inputs and considering the emotional component of a situation. Results show how this type of memory retrieval can influence the decision making process. [ABSTRACT FROM PUBLISHER]

This work presents a discovery browsing system based on the Information Foraging Theory (IFT). Discovery browsing is a type of information seeking behavior where the expert user interacts iteratively with a literature-based discovery system to explore poorly understood relationships with the end goal of formulating a hypothesis or gaining insight by uncovering novel points of view. The mathematical model underlying the IFT is predictive of information seeking behavior of foragers on the World Wide Web (WWW) in a plethora of scenarios. We hypothesize that a discovery browsing system based upon the IFT can assist the user in the process of discovery by automatically making available the concepts to which the user would most likely attend. Given initial terms from a user, the discovery browsing system mines a semantic network of over 26 million object-relation-object pairs from 7.8 million MEDLINE citations and presents a ranked sub-graph, which is the prediction of where the interesting concepts (ideally discoveries) lie. In this work, we present the theoretical foundations and design of the discovery browsing system. To demonstrate its efficacy, we replicate two recent discoveries and demonstrate that it is able to predict the concepts that were determined as playing a role in novel hypotheses proposed by scientists. [ABSTRACT FROM PUBLISHER]