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• item-5 type ListItem.
• item-5 hasContent pascal-hitzler.
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• paper-63 type InProceedings.
• paper-63 label "ONTOMS2: an Efficient and Scalable ONTOlogy Management System with an Incremental Reasoning".
• paper-63 sameAs paper-63.
• paper-63 abstract "We present ONTOMS2, an efficient and scalable ONTOlogy Management System with an incremental reasoning. ONTOMS2 stores an OWL document and processes OWL-QL and SPARQL queries. Especially, ONTOMS2 supports SPARQL Update queries with an incremental instance reasoning of inverseOf, symmetric and transitive properties.".
• paper-63 hasAuthorList authorList.
• paper-63 isPartOf poster-demo-proceedings.
• paper-63 title "ONTOMS2: an Efficient and Scalable ONTOlogy Management System with an Incremental Reasoning".
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• item-1 type ListItem.
• item-1 hasContent min-joong-lee.
• item-1 next item-2.
• item-2 type ListItem.
• item-2 hasContent jong-ryul-lee.
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• item-3 type ListItem.
• item-3 hasContent sangyeon-kim.
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• item-4 type ListItem.
• item-4 hasContent myung-jae-park.
• item-4 next item-5.
• item-4 previous item-3.
• item-5 type ListItem.
• item-5 hasContent chin-wan-chung.
• item-5 previous item-4.
• paper-64 type InProceedings.
• paper-64 label "SPACE: SParql index for efficient Auto ComplEtion".
• paper-64 sameAs paper-64.
• paper-64 abstract "Querying Linked Data means to pose queries on various data sources without information about the data and the schema of the data. This demo shows SPACE, a tool to support autocompletion for SPARQL queries. It takes as input SPARQL query logs and builds an index structure for efficient and fast computation of query suggestions. To demonstrate SPACE, we use available query logs from the USEWOD Data Challenge 2013.".
• paper-64 hasAuthorList authorList.
• paper-64 isPartOf poster-demo-proceedings.
• paper-64 title "SPACE: SParql index for efficient Auto ComplEtion".
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• item-1 hasContent kasjen-kraemer.
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• item-2 type ListItem.
• item-2 hasContent renata-dividino.
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• item-3 type ListItem.
• item-3 hasContent gerd-groener.
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• paper-65 type InProceedings.
• paper-65 label "SexTant: Visualizing Time-Evolving Linked Geospatial Data".
• paper-65 sameAs paper-65.
• paper-65 abstract "The linked open data cloud is constantly evolving as datasets are continuously updated with newer versions. As a result, representing, querying, and visualizing the temporal dimension of linked data is crucial. This is especially important for geospatial datasets that form the backbone of large scale open data publication efforts in many sectors of the economy (the public sector, the Earth observation sector). Although there has been some work on the representation and querying of linked geospatial data that change over time, to the best of our knowledge, there is currently no tool that offers spatio-temporal visualization of such data. In this demo paper we present the system SexTant that addresses this issue. SexTant is a web-based tool that enables the exploration of time-evolving linked geospatial data as well as the creation, sharing, and collaborative editing of "temporally-enriched" thematic maps by combining different sources of geospatial and temporal information.".
• paper-65 hasAuthorList authorList.
• paper-65 isPartOf poster-demo-proceedings.
• paper-65 title "SexTant: Visualizing Time-Evolving Linked Geospatial Data".
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• item-1 type ListItem.
• item-1 hasContent konstantina-bereta.
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• item-2 hasContent charalampos-nikolaou.
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• item-3 type ListItem.
• item-3 hasContent manos-karpathiotakis.
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• item-4 type ListItem.
• item-4 hasContent kostis-kyzirakos.
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• item-5 type ListItem.
• item-5 hasContent manolis-koubarakis.
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• paper-66 type InProceedings.
• paper-66 label "The Empirical Robustness of Description Logic Classification".
• paper-66 sameAs paper-66.
• paper-66 abstract "In spite of the recent renaissance in lightweight description logics (DLs), many prominent DLs, such as that underlying the Web Ontology Language (OWL), have high worst case complexity for their key inference services. Modern reasoners have a large array of optimization, tuned calculi, and implementation tricks that allow them to perform very well in a variety of application scenarios, even though the complexity results ensure that they will perform poorly for some inputs. For users, the key question is how often they will encounter those pathological inputs in practice, that is, how robust are reasoners. We attempt to determine this question for classification of existing ontologies as they are found on the Web. It is a fairly common user task to examine ontologies published on the Web as part of their development process. Thus, the robustness of reasoners in this scenario is both directly interesting and provides some hints toward answering the broader question. From our experiments, we show that the current crop of OWL reasoners, in collaboration, is very robust against the Web.".
• paper-66 hasAuthorList authorList.
• paper-66 isPartOf poster-demo-proceedings.
• paper-66 title "The Empirical Robustness of Description Logic Classification".
• authorList type List.

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