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By Riccardo Albertoni, Monica De Martino (auth.), Stefano Spaccapietra (eds.)

ISBN-10: 3540776877

ISBN-13: 9783540776871

• internet semantics and semi-structured info • Semantic caching • information warehousing and semantic info mining • Spatial, temporal, multimedia and multimodal semantics • Semantics in facts visualization • Semantic providers for cellular clients • helping instruments • purposes of semantic-driven ways those subject matters are to be understood as particularly relating to semantic matters. Contributions submitted to the magazine and working with semantics of knowledge might be thought of whether they aren't from the themes within the checklist. whereas the actual visual appeal of the magazine matters is just like the books from the we- identified Springer LNCS sequence, the mode of operation is that of a magazine. Contributions may be freely submitted by means of authors and are reviewed by way of the Editorial Board. Contributions can also be invited, and however conscientiously reviewed, as within the case for concerns that comprise prolonged types of most sensible papers from significant meetings addressing facts semantics matters. unique concerns, concentrating on a selected subject, are coordinated by means of visitor editors as soon as the notion for a unique factor is authorized by way of the Editorial Board. eventually, it's also attainable magazine factor be dedicated to a unmarried textual content. The Editorial Board contains an Editor-in-Chief (with total responsibility), a Co-editor-in-Chief, and a number of other individuals. The Editor-in-Chief has a four-year mandate. contributors of the board have a three-year mandate. Mandates are renewable and new individuals will be elected anytime.

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Extra resources for Journal on Data Semantics X

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T n ) in the relaxation graph of Q denotes the conjunctive query Head(Q) ← t 1 , . . , t n . X)}. X)). Figure 4 (B) shows the direct product of the graphs of Figure 4 (A), which is a simplified version of the relaxation graph of the query. X) (A) (B) Fig. 4. X). (B) The direct product of the graphs given in (A). Nodes are enumerated from 0 (base query) to 3 (top query). 4 Types of Relaxation The notion of relaxation that we have presented in this section encompasses the following types of relaxation (the examples given use the ontology of Figure 1): 1.

A) (b) Fig. 4. 2 Evaluations Two kinds of evaluations of the results concerning the similarity obtained with respect to research interests (Fig. b) were performed. The first evaluation was based on the concept of recall and precision, calculated considering the same adaptation of recall and precision made by [20]. More precisely, considering an entity x, the recall and precision were defined, respectively, Asymmetric and Context-Dependent Semantic Similarity among Ontology Instances 25 as (A ∩ B)/A and (A ∩ B)/B , where A is the set of entities expected to be similar to x and B is the set of similar entities calculated by a model.

People who have not edited proceedings). Y ) we may relax this triple pattern by using the ontology of Figure 1. As an example, though the user may want to retrieve editors of proceedings at first, she/he might also be interested in knowing about people who have contributed to publications in other roles, along with the publications themselves. Y ), yielding a new, relaxed query that returns editors of publications along with their publications. Y ) to obtain more general answers. In order to save the user the effort of inspecting the ontology and rewriting the query to return more relaxed answers for the same original query, the system could perform this process automatically.

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Journal on Data Semantics X by Riccardo Albertoni, Monica De Martino (auth.), Stefano Spaccapietra (eds.)


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