Ιδρυματικό Αποθετήριο
Πολυτεχνείο Κρήτης
Πολυτεχνείο Κρήτης
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| URI | http://purl.tuc.gr/dl/dias/E1B487D9-C8DA-4E3D-99CE-96BBE4ED4ACC | - |
| Αναγνωριστικό | https://link.springer.com/chapter/10.1007%2F978-3-662-49521-6_7 | - |
| Αναγνωριστικό | https://doi.org/10.1007/978-3-662-49521-6_7 | - |
| Γλώσσα | en | - |
| Μέγεθος | 16 pages | en |
| Τίτλος | Searching Web 2.0 data through entity-based aggregation | en |
| Δημιουργός | Ioannou Aikaterini | en |
| Δημιουργός | Ιωαννου Αικατερινη | el |
| Δημιουργός | Velegrakis, Yannis | en |
| Εκδότης | Springer Verlag | en |
| Περίληψη | 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 |
| Τύπος | Πλήρης Δημοσίευση σε Συνέδριο | el |
| Τύπος | Conference Full Paper | en |
| Άδεια Χρήσης | http://creativecommons.org/licenses/by/4.0/ | en |
| Ημερομηνία | 2018-10-29 | - |
| Ημερομηνία Δημοσίευσης | 2016 | - |
| Θεματική Κατηγορία | Data integration | en |
| Θεματική Κατηγορία | Semantic data management | en |
| Θεματική Κατηγορία | Semantic web | en |
| Βιβλιογραφική Αναφορά | 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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