Home |  ENGLISH |  Kontakt |  Impressum |  Anmelden |  KIT

Inproceedings3504

Aus Aifbportal

Wechseln zu: Navigation, Suche

(This page contains COinS metadata)

LinkSUM: Using Link Analysis to Summarize Entity Data




Published: 2016 Juni
Herausgeber: Bozzon, Alessandro and Cudré-Mauroux, Philippe and Pautasso, Cesare
Buchtitel: Web Engineering, 16th International Conference, ICWE 2016, Lugano, Switzerland, June 6-9, 2016. Proceedings
Ausgabe: 9671
Reihe: Lecture Notes in Computer Science
Seiten: 244-261
Verlag: Springer International Publishing
Erscheinungsort: Cham
Referierte Veröffentlichung
BibTeX




Kurzfassung
The amount of structured data published on the Web is constantly growing. A significant part of this data is published in accordance to the Linked Data principles. The explicit graph structure enables machines and humans to retrieve descriptions of entities and discover information about relations to other entities. In many cases, descriptions of single entities include thousands of statements and for human users it becomes difficult to comprehend the data unless a selection of the most relevant facts is provided.

In this paper we introduce LinkSUM, a lightweight link-based approach for the relevance-oriented summarization of knowledge graph entities. LinkSUM optimizes the combination of the PageRank algorithm with an adaption of the Backlink method together with new approaches for predicate selection. Both, quantitative and qualitative evaluations have been conducted to study the performance of the method in comparison to an existing entity summarization approach. The results show a significant improvement over the state of the art and lead us to conclude that prioritizing the selection of related resources leads to better summaries.

ISBN: 978-3-319-38791-8
Download: Media:LinkSUM.pdf
DOI Link: 10.1007/978-3-319-38791-8_14

Projekt

SumOnXLiMe


Verknüpfte Tools

LinkSUM


Verknüpfte Datasets

DBpedia PageRank


Forschungsgruppe

Web Science und Wissensmanagement


Forschungsgebiet
Entitätszusammenfassung, Semantische Suche, Vernetzte Daten, Maschinelles Lernen