We study the problem of linking the terms of a web-search query to a semantic representation given by the set of entities (a.k.a. concepts) mentioned in it. We introduce SMAPH, a system that performs this task using the information coming from a web search engine, an approach we call “piggybacking.” We employ search engines to alleviate the noise and irregularities that characterize the language of queries. Snippets returned as search results also provide a context for the query that makes it easier to disambiguate the meaning of the query. From the search results, SMAPH builds a set of candidate entities with high coverage. This set is filtered by linking back the candidate entities to the terms occurring in the input query, ensuring high precision. A greedy disambiguation algorithm performs this filtering; it maximizes the coherence of the solution by iteratively discovering the pertinent entities mentioned in the query. We propose three versions of SMAPH that outperform state-of-the-art solutions on the known benchmarks and on the GERDAQ dataset, a novel dataset that we have built specifically for this problem via crowd-sourcing and that we make publicly available.

In this paper we study the problem of linking open-domain web-search queries towards entities drawn from the full entity inventory of Wikipedia articles. We introduce SMAPH-2, a second-order approach that, by piggybacking on a web search engine, alleviates the noise and irregularities that characterize the language of queries and puts queries in a larger context in which it is easier to make sense of them. The key algorithmic idea underlying SMAPH-2 is to first discover a candidate set of entities and then link-back those entities to their mentions occurring in the input query. This allows us to confine the possible concepts pertinent to the query to only the ones really mentioned in it. The link-back is implemented via a collective disambiguation step based upon a supervised ranking model that makes one joint prediction for the annotation of the complete query optimizing directly the F1 measure. We evaluate both known features, such as word embeddings and semantic relatedness among entities, and several novel features such as an approximate distance between mentions and entities (which can handle spelling errors). We demonstrate that SMAPH-2 achieves state-of-the-art performance on the [email protected] benchmark. We also publish GERDAQ (General Entity Recognition, Disambiguation and Annotation in Queries), a novel, public dataset built specifically for web-query entity linking via a crowdsourcing effort. SMAPH-2 outperforms the benchmarks by comparable margins also on GERDAQ.