Generating Tensor Representation from Concept Tree in Meaning Based Search

Abstract

Meaning based search retrieves objects from search index repository based on user's search Meanings and meaning of objects rather than keyword matching. It requires techniques to capture user's search Meanings and meanings of objects, transform them to a representation that can be stored and compared efficiently on computers. Meaning of objects can be adequately captured in terms of a hierarchical composition structure called concept tree. This thesis describes the design and development of an algorithm that transforms the hierarchical concept tree to a tensor representation using tensor algebra theory. These tensor representations can capture the information need of a user in a better way and can be used for similarity comparisons in meaning based search. A preliminary evaluation showed that the proposed framework outperforms the TF-IDF vector model in 95% of the cases and vector based conceptual search model in 92% of the cases in adequately comparing meaning of objects. The tensor conversion tool also was used to verify the salient properties of the meaning comparison framework. The results show that the salient properties are consistent with the tensor similarity values of the meaning comparison framework.