Effective and Efficient Entity Search in RDF Data

Blanco, Roi; Mika, Peter; Vigna, Sebastiano

doi:10.1007/978-3-642-25073-6_6

Cited by 92 publications

(92 citation statements)

References 20 publications

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“…acting together in a movie). We believe that the fact that it's picked by the model for the first split also reflects the fact that different signals for ranking naturally differ in their importance across entity types, as noted in [1]. Signals computed from the entity graph also make a remarkable contribution to the final ranking model, as well as co-occurrence features, especially those computed over Flickr data and user query sessions.…”

Section: Relevance Assessmentmentioning

confidence: 88%

Entity Recommendations in Web Search

Blanco

Cambazoğlu

Mika

et al. 2013

Lecture Notes in Computer Science

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100

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Abstract. While some web search users know exactly what they are looking for, others are willing to explore other topics related to an initial interest. Often, the user's initial interest can be uniquely linked to an entity in a knowledge base, and in this case it is natural to recommend the explicitly linked entities for further exploration. In real world knowledge bases, however, the number of linked entities may be very large and not all related entities may be equally relevant. Thus there is a need for ranking related entities. In this paper, we describe Spark, a recommendation engine that links a users' initial query to an entity within a knowledge base and provides a ranking of the related entities. Spark extracts several signals from a variety of data sources, including user sessions, Twitter and Flickr, using a large cluster of computers running Hadoop. These signals are combined with a machine learned ranking model in order to produce a final recommendation of entities to user queries, which is currently powering Yahoo! Search results pages.

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Section: Relevance Assessmentmentioning

confidence: 88%

Entity Recommendations in Web Search

Blanco

Cambazoğlu

Mika

et al. 2013

Lecture Notes in Computer Science

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100

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“…The content-based algorithms are based on matching words in documents. TF-IDF [7] and BM25F [8] are samples of these algorithms. Connectivity-based algorithms use links between pages on the web of documents.…”

Section: Ranking Algorithms Of Web Of Documentsmentioning

confidence: 99%

A Novel Connectivity-based Reinforcement Learning Algorithm for Ranking Linked Data on the Social Semantic Web

Farhadi¹

2017

IJCA

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“…Entity search has been studied in many approaches [1,[27][28][29][30]. The aforementioned approaches assume a central index comprising the entire data collection.…”

Section: Related Workmentioning

confidence: 99%

Federated Entity Search Using On-the-Fly Consolidation

Herzig

Mika

Blanco

et al. 2013

Lecture Notes in Computer Science

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Abstract. Nowadays, search on the Web goes beyond the retrieval of textual Web sites and increasingly takes advantage of the growing amount of structured data. Of particular interest is entity search, where the units of retrieval are structured entities instead of textual documents. These entities reside in different sources, which may provide only limited information about their content and are therefore called "uncooperative". Further, these sources capture complementary but also redundant information about entities. In this environment of uncooperative data sources, we study the problem of federated entity search, where redundant information about entities is reduced on-the-fly through entity consolidation performed at query time. We propose a novel method for entity consolidation that is based on using language models and completely unsupervised, hence more suitable for this on-the-fly uncooperative setting than state-of-the-art methods that require training data. Further, we apply the same language model technique to deal with the federated search problem of ranking results returned from different sources. Particular novel are the mechanisms we propose to incorporate consolidation results into this ranking. We perform experiments using real Web queries and data sources. Our experiments show that our approach for federated entity search with on-the-fly consolidation improves upon the performance of a state-of-the-art preference aggregation baseline and also benefits from consolidation.

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Effective and Efficient Entity Search in RDF Data

Cited by 92 publications

References 20 publications

Entity Recommendations in Web Search

Entity Recommendations in Web Search

A Novel Connectivity-based Reinforcement Learning Algorithm for Ranking Linked Data on the Social Semantic Web

Federated Entity Search Using On-the-Fly Consolidation

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