Data Portal @ linkeddatafragments.org

Matches in ScholarlyData for { ?s ?p ?o. }

• authorList hasLastItem item-6.
• item-1 type ListItem.
• item-1 hasContent yuan-ren.
• item-1 next item-2.
• item-2 type ListItem.
• item-2 hasContent artemis-parvizi.
• item-2 next item-3.
• item-2 previous item-1.
• item-3 type ListItem.
• item-3 hasContent chris-mellish.
• item-3 next item-4.
• item-3 previous item-2.
• item-4 type ListItem.
• item-4 hasContent jeff-z-pan.
• item-4 next item-5.
• item-4 previous item-3.
• item-5 type ListItem.
• item-5 hasContent kees-van-deemter.
• item-5 next item-6.
• item-5 previous item-4.
• item-6 type ListItem.
• item-6 hasContent robert-stevens.
• item-6 previous item-5.
• 146 creator giorgos-stoilos.
• 146 type InProceedings.
• 146 label "Ontology-Based Data Access Using Rewriting, OWL 2 RL Systems and Repairing".
• 146 sameAs 146.
• 146 abstract "In previous work it has been shown how an OWL 2 DL ontology O can be ‘repaired’ for an OWL 2 RL system ans—that is, how we can compute a set of axioms R that is independent from the data and such that ans that is generally incomplete for O becomes complete for all SPARQL queries when used with O\cup R. However, the initial implementation and experiments were very preliminary and hence it is currently unclear whether the approach can be applied to large and complex ontologies. Moreover, the approach so far can only support instance queries. In the current paper we thoroughly investigate repairing as an approach to scalable (and complete) ontology-based data access. First, we present several non-trivial optimisations to the first prototype. Second, we show how (arbitrary) conjunctive queries can be supported by integrating well-known query rewriting techniques with OWL 2 RL systems via repairing. Third, we perform an extensive experimental evaluation obtaining encouraging results. In more detail, our results show that we can compute repairs even for very large real-world ontologies in a reasonable amount of time, that the performance overhead introduced by repairing is negligible in small to medium sized ontologies and noticeable but manageable in large and complex one, and that the hybrid reasoning approach can very efficiently compute the correct answers for real-world challenging scenarios.".
• 146 hasAuthorList authorList.
• 146 isPartOf proceedings.
• 146 keyword "OWL 2 RL Systems".
• 146 keyword "Query Rewriting".
• 146 keyword "Repairing Incompleteness".
• 146 keyword "Scalable Query Answering".
• 146 title "Ontology-Based Data Access Using Rewriting, OWL 2 RL Systems and Repairing".
• authorList type List.
• authorList hasFirstItem item-1.
• authorList hasItem item-1.
• authorList hasLastItem item-1.
• item-1 type ListItem.
• item-1 hasContent giorgos-stoilos.
• 154 creator enrico-motta.
• 154 creator francesco-osborne.
• 154 creator giuseppe-scavo.
• 154 type InProceedings.
• 154 label "Identifying diachronic topic-based research communities by clustering shared research trajectories".
• 154 sameAs 154.
• 154 abstract "Communities of academic authors are usually identified by means of standard community detection algorithms, which exploit ‘static’ relations, such as co-authorship or citation networks. In contrast with these approaches, here we focus on diachronic topic-based communities –i.e., communities of people who appear to work on semantically related topics at the same time. These communities are interesting because their analysis allows us to make sense of the dynamics of the research world –e.g., migration of researchers from one topic to another, new communities being spawn by older ones, communities splitting, merging, ceasing to exist, etc. To this purpose, we are interested in developing clustering methods that are able to handle correctly the dynamic aspects of topic-based community formation, prioritizing the relationship between researchers who appear to follow the same research trajectories. We thus present a novel approach called Temporal Semantic Topic-Based Clustering (TST), which exploits a novel metric for clustering researchers according to their research trajectories, defined as distributions of semantic topics over time. The approach has been evaluated through an empirical study involving 25 experts from the Semantic Web and Human-Computer Interaction areas. The evaluation shows that TST exhibits a performance comparable to the one achieved by human experts.".
• 154 hasAuthorList authorList.
• 154 isPartOf proceedings.
• 154 keyword "Clustering".
• 154 keyword "Community Detection".
• 154 keyword "Fuzzy C-Means".
• 154 keyword "Scholarly Data".
• 154 keyword "Scholarly Ontologies".
• 154 keyword "Semantic Technologies".
• 154 keyword "Similarity Metrics".
• 154 title "Identifying diachronic topic-based research communities by clustering shared research trajectories".
• 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 francesco-osborne.
• item-1 next item-2.
• item-2 type ListItem.
• item-2 hasContent giuseppe-scavo.
• item-2 next item-3.
• item-2 previous item-1.
• item-3 type ListItem.
• item-3 hasContent enrico-motta.
• item-3 previous item-2.
• 168 creator alessio-palmero-aprosio.
• 168 creator elena-cabrio.
• 168 creator serena-villata.
• 168 type InProceedings.
• 168 label "These are your rights: A Natural Language Processing Approach to Automated RDF Licenses Generation".
• 168 sameAs 168.
• 168 abstract "In the latest years, the Web has seen an increasing interest in legal issues, concerning the use and re-use of online published material. In particular, several open issues affect the terms and conditions under which the data published on the Web is released to the users, and the users rights over such data. Though the number of licensed material on the Web is considerably increasing, the problem of generating machine readable licenses information is still unsolved. In this paper, we propose to adopt Natural Language Processing techniques to extract in an auto-mated way the rights and conditions granted by a license, and we return the license in a machine readable format using RDF and adopting two well known vocabularies to model licenses. Experiments over a set of widely adopted licenses show the feasibility of the proposed approach.".
• 168 hasAuthorList authorList.
• 168 isPartOf proceedings.
• 168 keyword "Licenses".
• 168 keyword "Natural Language Processing".
• 168 keyword "Rights".
• 168 title "These are your rights: A Natural Language Processing Approach to Automated RDF Licenses Generation".
• 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 elena-cabrio.
• item-1 next item-2.
• item-2 type ListItem.
• item-2 hasContent alessio-palmero-aprosio.
• item-2 next item-3.
• item-2 previous item-1.

• first
• previous
• next