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Graph structured data on the web is now massive as well as diverse, rangingfrom social networks, web graphs to knowledge-bases. Effectively querying thisgraph structured data is non-trivial and has led to research in a variety ofdirections -- structured queries, keyword and natural language queries,automatic translation of these queries to structured queries, etc. We areconcerned with a class of queries called relationship queries, which areusually expressed as a set of keywords (each keyword denoting a named entity).The results returned are a set of ranked trees, each of which denotesrelationships among the various keywords. The result list could consist ofhundreds of answers. The problem of keyword search on graphs has been exploredfor over a decade now, but an important aspect that is not as extensivelystudied is that of user experience. We propose KlusTree, which presentsclustered results to the users instead of a list of all the results. In ourapproach, the result trees are represented using language models and areclustered using JS divergence as a distance measure. We compare KlusTree withthe well-known approaches based on isomorphism and tree-edit distance basedclustering. The user evaluations show that KlusTree outperforms the other twoin providing better clustering, thereby enriching user experience, revealinginteresting patterns and improving result interpretation by the user.

KlusTree: Clustering Answer Trees from Keyword Search on Graphs