Context shifting for effective search over large knowledge bases

Abstract

The problem of searching large knowledge bases is becoming an important facet of the current web of steadily proliferating semantic content. By pushing the notion of a context for partitioning large knowledge bases, performance of search is improved by narrowing the search space to a context of interest. On the other hand, by restricting the search only to a particular context, some answer can be missed, downgrading the search accuracy. In order to mitigate this drawback, we propose to extend the standard query algorithms with the operation of context shifting, i.e., the operation that allows switching to a close context, if the current context does not contain satisfactory information to answer a query. The paper provides a conceptual description of shifting in contextualized knowledge bases (CKB); and a prototypical implementation of a CKB that supports context shifting. For the conceptual description we adopt and extend the contextas-a-box paradigm introduced in [15]. In such a framework, a context is identified by a set of dimensions, whose values are taken from value-sets structured in hierarchies. Context shifting allows to switch from one context to another by changing the value of one or more dimensions along the corresponding hierarchies. For the prototypical implementation of a CKB we adopt and extend Sesame RDF store in order to support context shifting.

A Contextualized Knowledge Framework for Semantic Web

“…Practically, we grounded our prototype on Sesame RDF(S) storage and querying framework 8 . We also demonstrated with this prototype application how search efficiency and accuracy on semantic web can be benefited with our notion of contexts [15]. In this work we demonstrated the use of context dimensions to form a hierarchical structure of contexts, which can guide the order of search.…”

Visually searching the web for structural content

“…Context-based search is another promising trial which adopts contextual information such as time, location, and nearby people as search keys [5], [6]. The user can be freed from a burden of placing excessive query commands or keywords because the system recognizes the context the user is situated and knows the information he/she wants to get.…”