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Institutional Repository [SANDBOX]
Technical University of Crete
Technical University of Crete
EN
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EL
| URI | http://purl.tuc.gr/dl/dias/E1B487D9-C8DA-4E3D-99CE-96BBE4ED4ACC | - |
| Identifier | https://link.springer.com/chapter/10.1007%2F978-3-662-49521-6_7 | - |
| Identifier | https://doi.org/10.1007/978-3-662-49521-6_7 | - |
| Language | en | - |
| Extent | 16 pages | en |
| Title | Searching Web 2.0 data through entity-based aggregation | en |
| Creator | Ioannou Aikaterini | en |
| Creator | Ιωαννου Αικατερινη | el |
| Creator | Velegrakis, Yannis | en |
| Publisher | Springer Verlag | en |
| Content Summary | Entity-based searching has been introduced as a way of allowing users and applications to retrieve information about a specific real world object such as a person, an event, or a location. Recent advances in crawling, information extraction, and data exchange technologies have brought a new era in data management, typically referred to through the term Web 2.0. Entity searching over Web 2.0 data facilitates the retrieval of relevant information from the plethora of data available in semantic and social web applications. Effective entity searching over a variety of sources requires the integration of the different pieces of information that refer to the same real world entity. Entity-based aggregation of Web 2.0 data is an effective mechanism towards this direction. Adopting the suggestions of the Linked Data movement, aggregators are able to efficiently match and merge the data that refer to the same real world object | en |
| Type of Item | Πλήρης Δημοσίευση σε Συνέδριο | el |
| Type of Item | Conference Full Paper | en |
| License | http://creativecommons.org/licenses/by/4.0/ | en |
| Date of Item | 2018-10-29 | - |
| Date of Publication | 2016 | - |
| Subject | Data integration | en |
| Subject | Semantic data management | en |
| Subject | Semantic web | en |
| Bibliographic Citation | E. Ioannou and Y. Velegrakis, "Searching Web 2.0 data through entity-based aggregation," in 8th International Conference on Computational Collective Intelligence, 2016, pp. 159-174. doi: 10.1007/978-3-662-49521-6_7 | en |
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