By Valentina Presutti, Eva Blomqvist, Raphaël Troncy, Harald Sack, Ioannis Papadakis, Anna Tordai
This ebook constitutes the completely refereed post-conference court cases of the satellite tv for pc occasions of the eleventh overseas convention at the Semantic internet, ESWC 2014, held in Anissaras, Crete, Greece, in could 2014. the quantity includes 20 poster and forty three demonstration papers, chosen from 113 submissions, in addition to 12 top workshop papers chosen from 60 papers provided on the workshop at ESWC 2014. top papers from AI Mashup problem also are integrated. The papers hide a variety of elements of the Semantic Web.
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Extra resources for The Semantic Web: ESWC 2014 Satellite Events: ESWC 2014 Satellite Events, Anissaras, Crete, Greece, May 25-29, 2014, Revised Selected Papers
For this, users should be involved in the source selection process – choosing which sources contribute to their search results. Previous work, however, solely aims at source contextualization for “Web tables”, while relying on schema information and simple relational entities. Addressing these shortcomings, we exploit work from the ﬁeld of data mining and show how to enable Web data source contextualization. Based on a real-world use case, we built a prototype contextualization engine, which we integrated in a system for searching the Web of data.
1 and each of them stands for an entity. Every entity description, Ge , is a one-hop graph. For instance, the description for entity es:data/tec0001 comprises all triples in Src. 1. Kernel Functions. We compare diﬀerent entities by comparing their descriptions, Ge . For this, we make use of kernel functions : Entity-Based Data Source Contextualization for Searching the Web of Data 29 Deﬁnition 3 (Kernel function). Let κ : X × X → R denote a kernel function such that κ(x1 , x2 ) = ϕ(x1 ), ϕ(x2 ) , where ϕ : X → H projects a data space X to a feature space H and ·, · refers to the scalar product.
For each blank node add every triple containing a blank node with the same identiﬁer as a subject to the description. Finally, CBD repeats these steps d times. CBD conﬁgured with d = 1 retrieves only triples with r as subject although triples with r as object could contain useful information. , (3) extract all triples with r as object, which is called Symmetric Concise Bounded Description (SCDB) . Second, CROCUS needs to calculate a numeric representation of an instance to facilitate further clustering steps.