Matches in ScholarlyData for { ?s ?p ?o. }
- 127 creator ilaria-tiddi.
- 127 creator mathieu-daquin.
- 127 type InProceedings.
- 127 label "Dedalo: looking for Clusters Explanations in a Labyrinth of Linked Data".
- 127 sameAs 127.
- 127 abstract "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.".
- 127 hasAuthorList authorList.
- 127 isPartOf proceedings.
- 127 keyword "Hypothesis Generation".
- 127 keyword "Knowledge Discovery".
- 127 keyword "Linked Data".
- 127 title "Dedalo: looking for Clusters Explanations in a Labyrinth of Linked Data".
- authorList type List.
- authorList hasFirstItem item-1.
- authorList hasItem item-1.
- authorList hasItem item-2.
- authorList hasItem item-3.
- authorList hasLastItem item-3.
- item-1 type ListItem.
- item-1 hasContent ilaria-tiddi.
- item-1 next item-2.
- item-2 type ListItem.
- item-2 hasContent mathieu-daquin.
- item-2 next item-3.
- item-2 previous item-1.
- item-3 type ListItem.
- item-3 hasContent enrico-motta.
- item-3 previous item-2.
- 133 creator antonio-maccioni.
- 133 creator roberto-de-virgilio.
- 133 type InProceedings.
- 133 label "Distributed Keyword Search over RDF via MapReduce".
- 133 sameAs 133.
- 133 abstract "Non expert users need support to access linked data available on the Web. To this aim, keyword-based search is considered an essential feature of database systems. The distributed nature of the Semantic Web demands query processing techniques to evolve towards a scenario where data is scattered on distributed data stores. Existing approaches to keyword search cannot guarantee scalability in a distributed environment, because, at runtime, they are unaware of the location of the relevant data to the query and thus, they cannot optimize join tasks. In this paper, we illustrate a novel distributed approach to keyword search over RDF data that exploits the MapReduce paradigm by switching the problem from graph-parallel to data-parallel processing. Moreover, our framework is able to consider ranking during the building phase to return directly the best (top-k) answers in the first (k) generated results, reducing greatly the overall computational load and complexity. Finally, a comprehensive evaluation demonstrates that our approach exhibits very good efficiency guaranteeing high level of accuracy, especially with respect to state-of-the-art competitors.".
- 133 hasAuthorList authorList.
- 133 isPartOf proceedings.
- 133 keyword "Distributed Keyword Search".
- 133 keyword "MapReduce".
- 133 keyword "RDF".
- 133 keyword "Semantic Web".
- 133 title "Distributed Keyword Search over RDF via MapReduce".
- authorList type List.
- authorList hasFirstItem item-1.
- authorList hasItem item-1.
- authorList hasItem item-2.
- authorList hasLastItem item-2.
- item-1 type ListItem.
- item-1 hasContent roberto-de-virgilio.
- item-1 next item-2.
- item-2 type ListItem.
- item-2 hasContent antonio-maccioni.
- item-2 previous item-1.
- 134 creator arnab-dutta.
- 134 creator christian-meilicke.
- 134 creator simone-paolo-ponzetto.
- 134 type InProceedings.
- 134 label "A Probabilistic Approach for Integrating Heterogeneous Knowledge Sources".
- 134 sameAs 134.
- 134 abstract "In the past years, open-domain Information Extraction (IE) systems like Nell and ReVerb have achieved impressive results by harvesting massive amounts of machine-readable knowledge with minimal supervision. However, the knowledge bases they produce still lack a clean, explicit semantic data model. This, on the other hand, could be provided by full-fledged semantic networks like DBpedia or Yago, which, in turn, would benefit from the additional coverage provided by Web-scale IE. In this paper, we bring these two strains of research together, and present a method to align terms from Nell with instances in DBpedia. Our approach is unsupervised in nature and relies on two key components. First, we automatically acquire probabilistic type information for Nell terms given a set of matching hypotheses. Second, we view the mapping task as the statistical inference problem of finding the most likely coherent mapping – i.e., the maximum a posteriori (MAP) mapping – based on the outcome of the first component used as soft constraint. These two steps are highly intertwined: accordingly, we propose an approach that iteratively refines type acquisition based on the output of the mapping generator, and vice versa. Experimental results on gold-standard data indicate that our approach outperforms a strong baseline, and is able to produce ever-improving mappings consistently across iterations.".
- 134 hasAuthorList authorList.
- 134 isPartOf proceedings.
- 134 keyword "Knowledge refinement".
- 134 keyword "Markov Logic Network".
- 134 keyword "Open Information Extraction".
- 134 keyword "Probabilistic Reasoning".
- 134 title "A Probabilistic Approach for Integrating Heterogeneous Knowledge Sources".
- authorList type List.
- authorList hasFirstItem item-1.
- authorList hasItem item-1.
- authorList hasItem item-2.
- authorList hasItem item-3.
- authorList hasLastItem item-3.
- item-1 type ListItem.
- item-1 hasContent arnab-dutta.
- item-1 next item-2.
- item-2 type ListItem.
- item-2 hasContent christian-meilicke.
- item-2 next item-3.
- item-2 previous item-1.
- item-3 type ListItem.
- item-3 hasContent simone-paolo-ponzetto.
- item-3 previous item-2.
- 139 creator rakebul-hasan.
- 139 type InProceedings.
- 139 label "Generating and Summarizing Explanations for Linked Data".
- 139 sameAs 139.
- 139 abstract "Linked Data consumers may need explanations for debugging or understanding the reasoning behind producing the data. They may need the possibility to transform long explanations into more understandable short explanations. In this paper, we discuss an approach to explain reasoning over Linked Data. We introduce a vocabulary to describe explanation related metadata and we discuss how publishing these metadata as Linked Data enables explaining reasoning over Linked Data. Finally, we present an approach to summarize these explanations taking into account user specified explanation filtering criteria.".
- 139 hasAuthorList authorList.
- 139 isPartOf proceedings.
- 139 keyword "Explanation".
- 139 keyword "Linked Data".
- 139 keyword "Summarization".
- 139 title "Generating and Summarizing Explanations for Linked Data".
- authorList type List.
- authorList hasFirstItem item-1.
- authorList hasItem item-1.
- authorList hasLastItem item-1.
- item-1 type ListItem.
- item-1 hasContent rakebul-hasan.
- 140 creator alessio-bosca.