Matches in ScholarlyData for { ?s ?p ?o. }
- 42 creator sonja-zillner.
- 42 type InProceedings.
- 42 label "Reasoning-based Patient Classification for Enhanced Medical Image Annotations".
- 42 sameAs 42.
- 42 abstract "Medical imaging plays an important role in today''s clinical daily tasks, such as patient screening, diagnosis, treatment planning and follow up. But still a generic and flexible image understanding is missing. Although, there exist several approaches for semantic image annotation, those approaches do not make use of practical clinical knowledge, such as best practice solutions or clinical guidelines. We introduce a knowledge engineering approach aiming for reasoning-based enhancement of medical images annotation by integrating practical clinical knowledge. We will exemplify the reasoning steps of the methodology along a use case for automatic lymphoma patient staging.".
- 42 hasAuthorList authorList.
- 42 isPartOf proceedings.
- 42 keyword "Knowledge Engineering".
- 42 keyword "Medical Imaging".
- 42 keyword "Medical Ontologies".
- 42 keyword "Reasoning-based Ontology Enhancement".
- 42 title "Reasoning-based Patient Classification for Enhanced Medical Image Annotations".
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- 43 creator andreas-abecker.
- 43 creator joachim-kleb.
- 43 type InProceedings.
- 43 label "Entity Reference Resolution via Spreading Activation on RDF-Graphs".
- 43 sameAs 43.
- 43 abstract "The use of natural language identifiers as reference for ontology elements—in addition to the URIs required by the Semantic Web standards—is of utmost importance based on their predominance in the human everyday life, i.e. speech or print media. Depending on the context, different names can be chosen for one and the same element, and the same element can be referenced by different names. Here homonymy and synonymy are the main cause of the appearance of ambiguity in perceiving which concrete unique ontology element ought to be referenced by a specific natural language identifier describing an entity. We propose a novel method to resolve entity references under the aspect of ambiguity which explores only formal background knowledge represented in RDF graph structures. The key idea of our domain independent approach is to build an entity network with the most likely referenced ontology elements by constructing spanning graphs based on spreading activation (SA). Additional to the exploitation of complex graph structures we devise a new ranking technique that characterises the likelihood of entities in this network, i.e. interpretation contexts. Experiments in a highly polysemic domain show the ability of the algorithm to retrieve the correct ontology elements in almost all cases.".
- 43 hasAuthorList authorList.
- 43 isPartOf proceedings.
- 43 keyword "Entity Disambiguation".
- 43 keyword "Reference Resolution".
- 43 title "Entity Reference Resolution via Spreading Activation on RDF-Graphs".
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- item-1 hasContent joachim-kleb.
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- item-2 hasContent andreas-abecker.
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- 44 creator frank-van-harmelen.
- 44 creator henri-bal.
- 44 creator jacopo-urbani.
- 44 creator jason-maassen.
- 44 creator spyros-kotoulas.
- 44 type InProceedings.
- 44 label "OWL reasoning with MapReduce: calculating the closure of 100 billion triples".
- 44 sameAs 44.
- 44 abstract "In previous work we have shown that the MapReduce framework for distributed computation can be deployed for highly scalable inference over RDF graphs under the RDF Schema semantics. Unfortunately, several key optimizations that enabled the scalable RDFS inference do not generalize to the richer OWL semantics. In this paper we analyze these problems, and we propose solutions to overcome them. Our solutions allow distributed computation of the closure of an RDF graph under the OWL Horst semantics. We have implemented our approach on the Hadoop platform and deployed it on a compute cluster of 64 machines. We have evaluated our approach using a real-world dataset (UniProt, about 1.5 billion triples) and a synthetic benchmark (LUBM, up to 100 billion triples). Results show that our implementation is scalable and vastly outperforms current systems when comparing supported language expressivity, maximum data size and inference speed.".
- 44 hasAuthorList authorList.
- 44 isPartOf proceedings.
- 44 keyword "Distributed Reasoning".
- 44 keyword "Forward reasoning".
- 44 keyword "MapReduce".
- 44 keyword "OWL horst".
- 44 keyword "Scalability".
- 44 title "OWL reasoning with MapReduce: calculating the closure of 100 billion triples".
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- item-1 hasContent jacopo-urbani.
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- item-2 hasContent spyros-kotoulas.
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- item-3 hasContent jason-maassen.
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- item-4 hasContent frank-van-harmelen.
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- item-5 hasContent henri-bal.
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- 56 creator alan-rector.
- 56 creator ignazio-palmisano.
- 56 creator luigi-iannone.
- 56 creator robert-stevens.
- 56 type InProceedings.
- 56 label "Assessing the Safety of Knowledge Patterns in OWL Ontologies".
- 56 sameAs 56.
- 56 abstract "The availability of a concrete language for embedding knowledge patterns inside OWL ontologies makes it possible to analyze their impact on the semantics when applied to the ontologies themselves. Starting from recent results available in the literature, this work proposes a sufficient condition for identifying safe patterns encoded in OPPL. The resulting framework can be used to implement OWL ontology engineering tools that help knowledge engineers to understand the level of extensibility of their models as well as pattern users to determine what are the safe ways of utilizing a pattern in their ontologies.".
- 56 hasAuthorList authorList.
- 56 isPartOf proceedings.
- 56 keyword "Knowledge Patterns".
- 56 keyword "Ontology Engineering".
- 56 title "Assessing the Safety of Knowledge Patterns in OWL Ontologies".
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