A Hill-Climbing based Information Retrieval Mechanism for Formal Concept Analysis in Folksonomies

Abstract

Folksonomies are a means by which communities of people annotate resources with tags. The information contributed by this action can be used to effectively search for an object. A method is required to represent and categorize objects based on their tags and search them effectively by providing words that are relevant to any object. Few existing systems use lattice structure that conceptually groups tagged objects into nodes. The lattice structure facilitates fast search and creates relations between groups of objects. In order to search objects existing statistical and mathematical measures solely take into account a few factors such as the co-occurrence of two tags rather than their meanings to determine their similarity. Hence, when searching for an object based on a user query in a lattice structure there is a need for semantic similarity measure for comparing the meaning of each query with the tags at each lattice node. Also, existing search algorithms like breadth first search process more number of concepts than necessary and so there is a need for a mechanism that performs a narrower search through the lattice. The proposed system represents and categorizes objects with a lattice structure and specifies an optimized search technique based on semantic similarity. Semantic similarity is found by extracting synonyms of the user's query and mapping them to the tags of the object in the lattice structure. In order to achieve an optimized top-down search a novel hill-climbing approach is performed over the lattice that processes minimal number of lattice nodes. Experimental results show the efficiency of the search algorithm along with the similarity measures with near accurate results to the user's query.