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- 176 creator gerard-de-melo.
- 176 creator jacobo-rouces.
- 176 creator katja-hose.
- 176 type InProceedings.
- 176 label "FrameBase: Representing N-ary Relations using Semantic Frames".
- 176 sameAs 176.
- 176 abstract "Large-scale knowledge graphs such as those in the Linked Data cloud are typically represented as subject-predicate-object triples. However, many facts about the world involve more than two entities. While n-ary relations can be converted to triples in a number of ways, unfortunately, the structurally different choices made in different knowledge sources significantly impede our ability to connect them. They also make it impossible to query the data concisely and without prior knowledge of each individual source. We present FrameBase, a wide-coverage knowledge-base schema that uses linguistic frames to seamlessly represent and query n-ary relations from other knowledge bases, at different levels of granularity connected by logical entailment. It also opens possibilities to draw on natural language processing techniques for querying and data mining.".
- 176 hasAuthorList authorList.
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- 176 keyword "Data Integration".
- 176 keyword "Linked Data".
- 176 keyword "Semantic Web".
- 176 title "FrameBase: Representing N-ary Relations using Semantic Frames".
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- 178 creator freddy-lecue.
- 178 creator pascal-hitzler.
- 178 creator prabhaker-mateti.
- 178 creator raghava-mutharaju.
- 178 type InProceedings.
- 178 label "Distributed and Scalable OWL EL Reasoning".
- 178 sameAs 178.
- 178 abstract "OWL 2 EL is one of the tractable profiles of Web Ontology Language (OWL) which has been standardized by the W3C. OWL 2 EL provides sufficient expressivity to model large biomedical ontologies as well as streaming data such as traffic. Automated generation of ontologies from streaming data and text can lead to very large ontologies. Existing reasoners make use of only a single machine and are thus constrained by memory and computational power. There is a need to develop reasoning approaches which scale with the size of ontologies. We describe a distributed reasoning system that scales well using a cluster of commodity machines. We also apply our system to a use case on city traffic data and show that it can handle volumes which cannot be handled by current reasonably-sized single machine reasoners.".
- 178 hasAuthorList authorList.
- 178 isPartOf proceedings.
- 178 keyword "Distributed reasoning".
- 178 keyword "EL classification".
- 178 keyword "Load balancing".
- 178 keyword "Traffic data".
- 178 title "Distributed and Scalable OWL EL Reasoning".
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- item-1 hasContent raghava-mutharaju.
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- 184 creator claudia-damato.
- 184 creator floriana-esposito.
- 184 creator giuseppe-rizzo1.
- 184 creator nicola-fanizzi.
- 184 type InProceedings.
- 184 label "Inductive Classification through Evidence-based Models and their Ensembles".
- 184 sameAs 184.
- 184 abstract "In the context of Semantic Web, one of the most important issue related to the class-membership prediction task (by using inductive models) on ontological knowledge bases concerns with the class-imbalance of the training examples, mostly due to the heterogeneous nature and the incompleteness of the knowledge bases. An ensemble learning approach has been proposed to cope with this problem. However, the majority voting procedure, exploited for deciding the membership, does not consider explicitly the uncertainty and the conflict that may occur among the classifiers of an ensemble model. Moving from this observation, we propose to integrate the Dempster-Shafer (DS) Theory with ensemble learning by exploiting DS pooling operators for combining conflictual information. Specifically, we propose an algorithm for learning Evidential Terminological Random Forest models to be used for the class-membership prediction task. The algorithm extends Terminological Random Forest in the settings of the Dempster-Shafer Theory. An empirical evaluation showed that the resulting models performs better for datasets with a lot of positive and negative examples and have a less conservative behavior than the voting-based forests.".
- 184 hasAuthorList authorList.
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- 184 keyword "Dempster-Shafer Theory".
- 184 keyword "class-membership prediction".
- 184 keyword "uncertainty".
- 184 title "Inductive Classification through Evidence-based Models and their Ensembles".
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- item-1 hasContent giuseppe-rizzo1.
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