Dedalo: Looking for clusters explanations in a labyrinth of linked data

Ilaria Tiddi, Mathieu D'Aquin, Enrico Motta

Research output: Chapter in Book / Report / Conference proceedingConference contributionAcademicpeer-review


We present Dedalo, a framework which is able to exploit Linked Data to generate explanations for clusters. In general, any result of a Knowledge Discovery process, including clusters, is interpreted by human experts who use their background knowledge to explain them. However, for someone without such expert knowledge, those results may be difficult to understand. Obtaining a complete and satisfactory explanation becomes a laborious and time-consuming process, involving expertise in possibly different domains. Having said so, not only does the Web of Data contain vast amounts of such background knowledge, but it also natively connects those domains. While the efforts put in the interpretation process can be reduced with the support of Linked Data, how to automatically access the right piece of knowledge in such a big space remains an issue. Dedalo is a framework that dynamically traverses Linked Data to find commonalities that form explanations for items of a cluster. We have developed different strategies (or heuristics) to guide this traversal, reducing the time to get the best explanation. In our experiments, we compare those strategies and demonstrate that Dedalo finds relevant and sophisticated Linked Data explanations from different areas.

Original languageEnglish
Title of host publicationThe Semantic Web
Subtitle of host publicationTrends and Challenges - 11th International Conference, ESWC 2014, Proceedings
PublisherSpringer Verlag
Number of pages16
ISBN (Print)9783319074429
Publication statusPublished - 1 Jan 2014
Externally publishedYes
Event11th International Conference on Semantic Web: Trends and Challenges, ESWC 2014 - Anissaras, Crete, Greece
Duration: 25 May 201429 May 2014

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8465 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference11th International Conference on Semantic Web: Trends and Challenges, ESWC 2014
CityAnissaras, Crete


  • Hypothesis Generation
  • Knowledge Discovery
  • Linked Data


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