Most of the existing document and web search engines rely on keyword-based queries. To find matches, these queries are processed using retrieval algorithms that rely on word frequencies, topic recentness, document authority, and (in some cases) available ontologies. In this paper, we propose an innovative approach to exploring text collections using a novel keywords-by-concepts (KbC) graph, whi...

This presentation will detail the inception, development and evaluation of ASKNet. A system which uses natural language processing tools in order to create psycholinguistically inspired, spreading activation based semantic networks from natural language texts.

Current recommender systems make recommendations mostly independent of context. The recommender systems field is starting to acknowledge that systems need to incorporate the context in or for which they make recommendations to improve results. We show how recommender systems can make use of CASAN nets, our extension of the classical associative spreading activation network formalism. In CASAN n...

The paper presents a wordnet expansion algorithm, which is based on lexicosemantic relations extracted from large text corpora. We do not assume that the extracted relation instances (i.e. word pairs) are described by probabilities. Thus, results produced by any method, including pattern-based and Distributional Semantics approaches can be used. The algorithm is based on a general spreading act...

SNIF-ACT (Scent-based Navigation and Information Foraging in the ACT architecture) has been developed to simulate users as they perform unfamiliar information-seeking tasks on the World Wide Web (WWW). SNIF-ACT selects actions based on the measure of information scent, which is calculated by a spreading activation mechanism that captures the mutual relevance of the contents of a WWW page to the...

Keyword-matching systems based on simple models of semantic relatedness are inadequate at modelling the ambiguities in natural language text, and cannot reliably address the increasingly complex information needs of users. In this paper we propose novel methods for computing semantic relatedness by spreading activation energy over the hyperlink structure of Wikipedia. We demonstrate that our te...

This work presents a method for generating intuitive visualizations for high-level user queries to an hierarchical surface-based neuroanatomical atlas. We combine a spreading activation approach for computing focus and context in an ontology with a specific level-of-detail strategy for hierarchical structures. We demonstrate our method on an atlas of the bee brain.

This paper compares the utilization of ConceptNet and WordNet in query expansion. Spreading activation selects candidate terms for query expansion from these two resources. Three measures including discrimination ability, concept diversity, and retrieval performance are used for comparisons. The topics and document collections in the ad hoc track of TREC-6, TREC-7 and TREC-8 are adopted in the ...

Hass and Hermann (2012) have shown that only variance-based processes will lead to the scalar growth of error that is characteristic of human time judgments. Secondly, a major meta-review of over one hundred studies (Block et al., 2010) reveals a striking interaction between the way in which temporal judgments are queried and cognitive load on participants’ judgments of interval duration. For r...

The nature of persisting spreading activation from list presentation in eliciting false recognition in the Deese–Roediger–McDermott (DRM) paradigm was examined in two experiments. We compared the time course of semantic priming in the lexical decision task (LDT) and false alarms in speeded recognition under identical study and test conditions. The results revealed priming on the LDT only when a...