Inproceedings3564: Unterschied zwischen den Versionen
Qz7954 (Diskussion | Beiträge) |
Qz7954 (Diskussion | Beiträge) |
||
Zeile 26: | Zeile 26: | ||
|Abstract=A lot of current web pages include structured data which can di- rectly be processed and used. Search engines, in particular, gather that structured data and provide question answering capabilities over the integrated data with an entity-centric presentation of the results. Due to the decentralized nature of the web, multiple struc- tured data sources can provide similar information about an entity. But data from di erent sources may involve di erent vocabular- ies and modeling granularities, which makes integration di cult. We present an approach that identi es similar entity-speci c data across sources, independent of the vocabulary and data modeling choices. We apply our method along the scenario of a trustable knowledge panel, conduct experiments in which we identify and process entity data from web sources, and compare the output to a competing system. The results underline the advantages of the presented entity-centric data fusion approach. | |Abstract=A lot of current web pages include structured data which can di- rectly be processed and used. Search engines, in particular, gather that structured data and provide question answering capabilities over the integrated data with an entity-centric presentation of the results. Due to the decentralized nature of the web, multiple struc- tured data sources can provide similar information about an entity. But data from di erent sources may involve di erent vocabular- ies and modeling granularities, which makes integration di cult. We present an approach that identi es similar entity-speci c data across sources, independent of the vocabulary and data modeling choices. We apply our method along the scenario of a trustable knowledge panel, conduct experiments in which we identify and process entity data from web sources, and compare the output to a competing system. The results underline the advantages of the presented entity-centric data fusion approach. | ||
|Download=Entity-centric Data Fusion on the Web.pdf, | |Download=Entity-centric Data Fusion on the Web.pdf, | ||
− | |DOI Name= | + | |DOI Name=https://doi.org/10.1145/3078714.3078717 |
|Projekt=SemData, SumOn | |Projekt=SemData, SumOn | ||
|Forschungsgruppe=Web Science | |Forschungsgruppe=Web Science | ||
}} | }} |
Version vom 21. Juli 2017, 15:23 Uhr
Entity-centric Data Fusion on the Web
Entity-centric Data Fusion on the Web
Published: 2017
Juli
Buchtitel: Proceedings of the 28th ACM Conference on Hypertext and Social Media
Verlag: ACM
Referierte Veröffentlichung
BibTeX
Kurzfassung
A lot of current web pages include structured data which can di- rectly be processed and used. Search engines, in particular, gather that structured data and provide question answering capabilities over the integrated data with an entity-centric presentation of the results. Due to the decentralized nature of the web, multiple struc- tured data sources can provide similar information about an entity. But data from di erent sources may involve di erent vocabular- ies and modeling granularities, which makes integration di cult. We present an approach that identi es similar entity-speci c data across sources, independent of the vocabulary and data modeling choices. We apply our method along the scenario of a trustable knowledge panel, conduct experiments in which we identify and process entity data from web sources, and compare the output to a competing system. The results underline the advantages of the presented entity-centric data fusion approach.
Download: Media:Entity-centric Data Fusion on the Web.pdf
DOI Link: https://doi.org/10.1145/3078714.3078717