Matches in ScholarlyData for { ?s ?p ?o. }
- 47 creator ronald-denaux.
- 47 type InProceedings.
- 47 label "Troubleshooting and Optimizing Named Entity Resolution Systems in the Industry".
- 47 sameAs 47.
- 47 abstract "Named Entity Resolution (NER) is an information extraction task that involves detecting mentions of named entities within texts and mapping them to their corresponding entities in a given knowledge source. Systems and frameworks for performing NER have been developed both by the academia and the industry with different features and capabilities. Nevertheless, what all approaches have in common is that their satisfactory performance in a given scenario does not constitute a trustworthy predictor of their performance in a different one, the reason being the scenario's different parameters (target entities, input texts, domain knowledge etc.). With that in mind, we describe in this paper a metric-based Diagnostic Framework that can be used to identify the causes behind the low performance of NER systems in industrial settings and take appropriate actions to increase it.".
- 47 hasAuthorList authorList.
- 47 isPartOf proceedings.
- 47 keyword "Diagnostic Metrics".
- 47 keyword "Effectiveness Optimization".
- 47 keyword "Named Entity Resolution".
- 47 title "Troubleshooting and Optimizing Named Entity Resolution Systems in the Industry".
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- item-1 hasContent panos-alexopoulos.
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- item-2 hasContent ronald-denaux.
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- item-3 hasContent jose-manuel-gomez-perez.
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- 56 creator alexey-andreev.
- 56 creator anna-spiridonova.
- 56 creator dmitry-muromtsev.
- 56 creator dmitry-pavlov.
- 56 creator eugene-cherny.
- 56 creator peter-haase.
- 56 type InProceedings.
- 56 label "Towards the Linked Russian Heritage Cloud: Data enrichment and Publishing".
- 56 sameAs 56.
- 56 abstract "In this paper we present an architecture and approach to publishing open linked data in cultural heritage domain. We demonstrate our approach for building a system both for data publishing and consumption and show how user’s benefits can be achieved with semantic technologies. For domain knowledge representation CIDOC-CRM ontology is used. As a main source of trusted data the web portal of the Russian Museum is crawled. For data enrichment we selected DBpedia and SPARQL-endpoint of the British Museum. The evaluation shows potentials of semantics applications for data publishing in contextual environment, semantic search and visualization and automated enrichment according to needs and expectations of art experts and regular museum visitors. ".
- 56 hasAuthorList authorList.
- 56 isPartOf proceedings.
- 56 keyword "CIDOC-CRM".
- 56 keyword "cultural heritage data".
- 56 keyword "open data".
- 56 keyword "semantic data publishing".
- 56 keyword "semantic web".
- 56 title "Towards the Linked Russian Heritage Cloud: Data enrichment and Publishing".
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- item-1 hasContent dmitry-muromtsev.
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- item-2 hasContent peter-haase.
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- item-3 hasContent dmitry-pavlov.
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- item-4 hasContent eugene-cherny.
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- item-5 hasContent alexey-andreev.
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- item-6 hasContent anna-spiridonova.
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- 68 creator dietlind-zuhlke.
- 68 creator heiner-oberkampf.
- 68 creator matthias-hammon.
- 68 creator soeren-auer.
- 68 creator sonja-zillner.
- 68 creator turan-gojayev.
- 68 type InProceedings.
- 68 label "From Symptoms to Diseases -- Creating the Missing Link".
- 68 sameAs 68.
- 68 abstract "A wealth of biomedical datasets is meanwhile published as Linked Open Data. Each of these datasets has a particular focus, such as providing information on diseases or symptoms of a certain kind. Hence, a comprehensive view can only be provided by integrating information from various datasets. Although, links between diseases and symptoms can be found, these links are far too sparse to enable practical applications such as a disease-centric access to clinical reports that are annotated with symptom information. For this purpose, we build a model of disease-symptom relations. Utilizing existing ontology mappings, we propagate semantic type information disease and symptom across ontologies. Then entities of the same semantic type from different ontologies are clustered and object properties between entities are mapped to cluster-level relations. The effectiveness of our approach is demonstrated by integrating all available disease-symptom relations from different biomedical ontologies resulting in a significantly increased linkage between datasets.".
- 68 hasAuthorList authorList.
- 68 isPartOf proceedings.
- 68 keyword "Linked Data".
- 68 keyword "biomedical ontologies".
- 68 keyword "disease symptom relations".
- 68 keyword "ontology mapping".
- 68 title "From Symptoms to Diseases -- Creating the Missing Link".
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