On Suggesting Entities as Web Search Queries

The Web of Data is growing in popularity and dimension, and named entity exploitation is gaining importance in many research fields. In this paper, we explore the use of entities that can be extracted from a query log to enhance query recommendation. In particular, we extend a state-of-the-art recommendation algorithm to take into account the semantic information associated with submitted queries. Our novel method generates highly related and diversified suggestions that we assess by means of a new evaluation technique. The manually annotated dataset used for performance comparisons has been made available to the research community to favor the repeatability of experiments. 1 Semantic Query Recommendation Mining the past interactions of users with the search system recorded in query logs is an effective approach to produce relevant query suggestions. This is based on the assumption that information searched by past users can be of interest to others. The typical interaction of a user with a Web search engine consists in translating her information need in a textual query made of few terms. We believe that the “Web of Data” can be profitably exploited to make this process more user-friendly and alleviate possible vocabulary mismatch problems. We adopt the Search Shortcuts (SS) model proposed in [1, 2]. The SS algorithm aims to generate suggestions containing only those queries appearing as final in successful sessions. The goal is to suggest queries having a high potentiality of being useful for people to reach their initial goal. The SS algorithm works by efficiently computing similarities between partial user sessions (the one currently performed) and historical successful sessions recorded in a query log. Final queries of most similar successful sessions are suggested to users as search shortcuts. A virtual document is constructed by merging successful session, i.e., ending with a clicked query. We annotate virtual documents to extract relevant named entities. Common annotation approaches on query logs consider a single query and try to map it to an entity (if any). If a query is ambiguous, the risk is to always map it to the most popular entity. On the other hand, in case of ambiguity, we can select the entity with the highest likelihood of representing the semantic context of a query. We define Semantic Search Shortcuts (S) the query recommender system exploiting this additional knowledge. Please note that S provides a list of related entities, differently from traditional query recommenders as SS that for a given query produce a flat list of recommendations. We assert that entities can potentially deliver to users much more information than raw queries. In order to compute the entities to be suggested, given an input query q, we first retrieve the top-k most relevant virtual documents by processing the query over the SS inverted index built as described above. The result set Rq contains the top-k relevant virtual documents along with the entities associated with them. Given an entity e in the result set, we define two measures: score(e,VD) = { conf(e)× score(VD), if e ∈ VD.entities