A search query, being a very concise grounding of user intent, could potentially have many possible interpretations. Search engines hedge their bets by diversifying top results to cover multiple such possibilities so that the user is likely to be satisfied, whatever be her intended interpretation. Diversified Query Expansion is the problem of diversifying query expansion suggestions, so that the user can specialize the query to better suit her intent, even before perusing search results. We propose a method, Select-Link-Rank, that exploits semantic information from Wikipedia to generate diversified query expansions. SLR does collective processing of terms and Wikipedia entities in an integrated framework, simultaneously diversifying query expansions and entity recommendations. SLR starts with selecting informative terms from search results of the initial query, links them to Wikipedia entities, performs a diversity-conscious entity scoring and transfers such scoring to the term space to arrive at query expansion suggestions. Through an extensive empirical analysis and user study, we show that our method outperforms the state-of-the-art diversified query expansion and diversified entity recommendation techniques.
|Title of host publication||17th International Conference on Web Information Systems Engineering (WISE 2016)|
|Number of pages||17|
|Publication status||Published - 02 Nov 2016|
|Event||WISE 2016: 17th International Conference on Web Information Systems Engineering - Shanghai, China|
Duration: 07 Nov 2016 → 10 Nov 2016
|Name||Lecture Notes in Computer Science|
|Period||07/11/2016 → 10/11/2016|