From Web Data to Entities and Back

Miklós, Zoltán; Bonvin, Nicolas; Bouquet, Paolo; Catasta, Michele; Cordioli, Daniele; Fankhauser, Péter; Gaugaz, Julien; Ioannou, Ekaterini; Koshutanski, Hristo; Maña, Antonio; Niederée, Claudia; Palpanas, Themis; Stoermer, Heiko

doi:10.1007/978-3-642-13094-6_25

Cited by 11 publications

(12 citation statements)

References 24 publications

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“…We obtained a real dataset, D-OKKAM, by observing the behavior of users when interacting with the OKKAM [33] entity search engine. The system allows users to search for a named entity, and to select the correct entity from the result list.…”

Section: D-okkam Datasetsmentioning

confidence: 99%

“…Furthermore, the semantic web community is interested in building the infrastructure that will transform the current web from a web of documents into a web of objects, i.e., entities [38]. Entity search engines have already appeared both in industry [14,42,45,47], and in academia [9,33], with major industrial players like Microsoft's Bing [11,34], Yahoo! [40, 44], and Google [37], indicating a strong interest.…”

Section: Introductionmentioning

confidence: 99%

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Entity ranking using click-log information

Mottin

Palpanas

Velegrakis

2013

IDA

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Log information describing the items the users have selected from the set of answers a query engine returns to their queries constitute an excellent form of indirect user feedback that has been extensively used in the web to improve the effectiveness of search engines.In this work we study how the logs can be exploited to improve the ranking of the results returned by an entity search engine. Entity search engines are becoming more and more popular as the web is changing from a web of documents into a "web of things". We show that entity search engines pose new challenges since their model is different than the one documents are based on. We present a novel framework for feature extraction that is based on the notions of entity matching and attribute frequencies. The extracted features are then used to train a ranking classifier.We introduce different methods and metrics for ranking, we combine them with existing traditional techniques and we study their performance using real and synthetic data. The experiments show that our technique provides better results in terms of accuracy.

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Section: D-okkam Datasetsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Entity ranking using click-log information

Mottin

Palpanas

Velegrakis

2013

IDA

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“…Much of this work is based on entities as the basic data unit, driven by their recent popularity and expressiveness. Many of our developments were fundamental components in the OKKAM project that aims at the creation of an infrastructure for global identifiers for every web object [12,5,33,27,39] Our collaborations with colleagues from the Semantic Web community and our participation in many projects from that area has revealed numerous challenging data management issues, that were recently summarized in a related tutorial [24].…”

Section: Information Integrationmentioning

confidence: 99%

dbTrento

Palpanas

Velegrakis

2012

SIGMOD Rec.

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“…Entity matching is also needed to create such large entity collections themselves. This process is described in [9]. Linking entities present in unstructured Web documents to each other can in many ways contribute to the development of the Semantic Web, independently of whether such large entity repositories will emerge.…”

Section: Introductionmentioning

confidence: 99%

Quality-aware similarity assessment for entity matching in Web data

Yerva

Miklós

Aberer

2012

Information Systems

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a b s t r a c tOne of the key challenges to realize automated processing of the information on the Web, which is the central goal of the Semantic Web, is related to the entity matching problem. There are a number of tools that reliably recognize named entities, such as persons, companies, geographic locations, in Web documents. The names of these extracted entities are, however, non-unique; the same name on different Web pages might or might not refer to the same entity. The entity matching problem concerns of identifying the entities, which are referring to the same real-world entity. This problem is very similar to the entity resolution problem studied in relational databases, however, there are also several differences. Most importantly Web pages often only contain partial or incomplete information about the entities.Similarity functions try to capture the degree of belief about the equivalence of two entities, thus they play a crucial role in entity matching. The accuracy of the similarity functions highly depends on the applied assessment techniques, but also on some specific features of the entities. We propose systematic design strategies for combined similarity functions in this context. Our method relies on the combination of multiple evidences, with the help of estimated quality of the individual similarity values and with particular attention to missing information that is common in Web context. We study the effectiveness of our method in two specific instances of the general entity matching problem, namely the person name disambiguation and the Twitter message classification problem. In both cases, using our techniques in a very simple algorithmic framework we obtained better results than the state-of-the-art methods.

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From Web Data to Entities and Back

Cited by 11 publications

References 24 publications

Entity ranking using click-log information

Entity ranking using click-log information

dbTrento

Quality-aware similarity assessment for entity matching in Web data

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