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• item-1 hasContent gloria-re-calegari.
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• item-2 hasContent emanuela-carlino.
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• item-3 hasContent irene-celino.
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• item-4 hasContent diego-peroni.
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• 184 creator andreas-both.
• 184 creator christoph-lange.
• 184 creator dennis-diefenbach.
• 184 creator didier-cherix.
• 184 creator kuldeep-singh.
• 184 creator saeedeh-shekarpour.
• 184 type InProceedings.
• 184 label "Qanary -- An Extensible Vocabulary for Open Question Answering Systems".
• 184 sameAs 184.
• 184 abstract "It is very challenging to access the knowledge expressed within (big) data sets. Question answering (QA) aims at making sense out of data via a simple-to-use interface. However, QA systems are very complex and earlier approaches are mostly singular and monolithic implementations for QA in specific domains. Therefore, it is cumbersome and inefficient to design and implement new or improved approaches, in particular as many components are not reusable. Hence, there is a strong need for enabling best-of-breed QA systems, where the best performing components are combined, aiming at the best quality achievable in the given domain. Taking into account the high variety of functionality that might be of use within a QA system and therefore reused in new QA systems, we provide an approach driven by a core QA vocabulary that is aligned to existing, powerful ontologies provided by domain-specific communities. We achieve this by a methodology for binding existing vocabularies to our core QA vocabulary without re-creating the information provided by external components. We thus provide a practical approach for rapidly establishing new (domain-specific) QA systems, while the core QA vocabulary is re-usable across multiple domains. To the best of our knowledge, this is the first approach to open QA systems that is agnostic to implementation details and that inherently follows the linked data principles.".
• 184 hasAuthorList authorList.
• 184 isPartOf proceedings.
• 184 title "Qanary -- An Extensible Vocabulary for Open Question Answering Systems".
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• item-1 hasContent andreas-both.
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• item-2 hasContent dennis-diefenbach.
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• item-3 hasContent kuldeep-singh.
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• item-4 hasContent saeedeh-shekarpour.
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• item-5 hasContent didier-cherix.
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• item-6 hasContent christoph-lange.
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• 188 creator folz-pauline.
• 188 creator hala-skaf-molli.
• 188 creator pascal-molli.
• 188 type InProceedings.
• 188 label "cyclades: A Decentralized Cache for Linked Data Fragments".
• 188 sameAs 188.
• 188 abstract "Linked Data Fragment (LDF) approach promotes a new trade-off between performance and data availability for querying Linked Data. If data providers’ HTTP caches plays a crucial role in LDF performances, LDF clients are also caching data during SPARQL query processing. Unfortunately, as these clients do not collaborate, they cannot take advantage of this large decentralized cache hosted by clients. In this paper, we propose CyCLaDEs an overlay network based on LDF fragments similarity. For each LDF client, CyCLaDEs builds a neighborhood of LDF clients hosting related fragments in their cache. During query processing, LDF clients try to resolve the query in the neighborhood and eventually request the LDF server, if neighborhood cannot answer. Experimental results show that CyCLaDEs is able to handle a significant amount of LDF query processing and provide a more specialized cache on client-side.".
• 188 hasAuthorList authorList.
• 188 isPartOf proceedings.
• 188 title "cyclades: A Decentralized Cache for Linked Data Fragments".
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• item-1 type ListItem.
• item-1 hasContent folz-pauline.
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• item-2 type ListItem.
• item-2 hasContent hala-skaf-molli.
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• item-2 previous item-1.
• item-3 type ListItem.
• item-3 hasContent pascal-molli.
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• 191 creator achim-rettinger.
• 191 creator aditya-mogadala.
• 191 creator christian-paul.
• 191 creator craig-knoblock.
• 191 creator pedro-szekely.
• 191 type InProceedings.
• 191 label "Efficient Graph-based Document Similarity".
• 191 sameAs 191.
• 191 abstract "Assessing the relatedness of documents is at the core of many applications such as document retrieval and recommendation. Most similarity approaches operate on word-distribution based document representations - fast to compute, but problematic when documents differ in language, vocabulary or type and neglecting the rich relational knowledge available in Knowledge Graphs. In contrast, graph-based document models can leverage valuable knowledge about relations between entities - however, due to expensive graph operations, similarity assessments tend to become infeasible in many applications. This paper presents an efficient semantic similarity approach exploiting explicit hierarchical and transversal relations. We show in our experiments that (i) our similarity measure provides a significantly higher correlation with human notions of document similarity than comparable measures, (ii) this also holds for short documents with few annotations, (iii) document similarity can be calculated efficiently compared to other graph-traversal based approaches.".
• 191 hasAuthorList authorList.
• 191 isPartOf proceedings.
• 191 title "Efficient Graph-based Document Similarity".
• authorList type List.
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