Search

Query recommendation, which suggests related queries to search engine users, has attracted a lot of attention in recent years. Most of the existing solutions, which perform analysis of users' search history (or query logs), are often insufficient for long-tail queries that rarely appear in query logs. To handle such queries, we study the use of entities found in queries to provide recommendations. Specifically, we extract entities from a query, and use these entities to explore new ones by consulting an information source. The discovered entities are then used to suggest new queries to the user. In this article, we examine two information sources: (1) a knowledge base (or KB), such as YAGO and Freebase; and (2) a click log, which contains the URLs accessed by a query user. We study how to use these sources to find new entities useful for query recommendation. We further study a hybrid framework that integrates different query recommendation methods effectively. As shown in the experiments, our proposed approaches provide better recommendations than existing solutions for long-tail queries. In addition, our query recommendation process takes less than 100ms to complete. Thus, our solution is suitable for providing online query recommendation services for search engines. [ABSTRACT FROM AUTHOR]

A query facet is a significant list of information nuggets that explains an underlying aspect of a query. Existing algorithms mine facets of a query by extracting frequent lists contained in top search results. The coverage of facets and facet items mined by these kind of methods might be limited, because only a small number of search results are used. In order to solve this problem, we propose mining query facets by using knowledge bases which contain high-quality structured data. Specifically, we first generate facets based on the properties of the entities which are contained in Freebase and correspond to the query. Second, we mine initial query facets from search results, then expanding them by finding similar entities from Freebase. Experimental results show that our proposed method can significantly improve the coverage of facet items over the state-of-the-art algorithms. [ABSTRACT FROM PUBLISHER]

There is a range of large knowledge bases, such as Freebase and DBpedia, as well as linked data sets available on the web, but they typically lack lexical information stating how the properties and classes they comprise are realized lexically. Often only one label is attached, if at all, thus lacking rich linguistic information, e.g. about morphological forms, syntactic arguments or possible lexical variants and paraphrases. While ontology lexicon models like lemon allow for defining such linguistic information with respect to a given ontology, the cost involved in creating and maintaining such lexica is substantial, requiring a high manual effort. Towards lowering this effort we present ATOLL, a framework for the automatic induction of ontology lexica, based both on existing labels and on dependency paths extracted from a text corpus. We instantiate ATOLL with respect to DBpedia as dataset and Wikipedia as corresponding corpus, and evaluate it by comparing the automatically generated lexicon with a manually constructed one. Our results clearly corroborate that our approach shows a high potential to be applied in a semi-automatic fashion in which a lexicon engineer can validate, reject or refine the automatically generated lexical entries, thus having a clear potential to contributing to the reduction of the overall cost of creating ontology lexica. [ABSTRACT FROM AUTHOR]

This paper introduces the problem of searching for social network accounts, e.g., Twitter accounts, with the rich information available on the Web, e.g., people names, attributes, and relationships to other people. For this purpose, we need to map Twitter accounts with Web entities. However, existing solutions building upon naive textual matching inevitably suffer low precision due to false positives (e.g., fake impersonator accounts) and false negatives (e.g., accounts using nicknames). To overcome these limitations, we leverage 'relational' evidences extracted from the Web corpus. We consider two types of evidence resources- First, web-scale entity relationship graphs, extracted from name co-occurrences crawled from the Web. This co-occurrence relationship can be interpreted as an 'implicit' counterpart of Twitter follower relationships. Second, web-scale relational repositories, such as Freebase with complementary strength. Using both textual and relational features obtained from these resources, we learn a ranking function aggregating these features for the accurate ordering of candidate matches. Another key contribution of this paper is to formulate confidence scoring as a separate problem from relevance ranking. A baseline approach is to use the relevance of the top match itself as the confidence score. In contrast, we train a separate classifier, using not only the top relevance score but also various statistical features extracted from the relevance scores of all candidates, and empirically validate that our approach outperforms the baseline approach. We evaluate our proposed system using real-life internet-scale entity-relationship and social network graphs. [ABSTRACT FROM AUTHOR]