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- 223 creator daniel-sonntag.
- 223 creator jochen-setz.
- 223 creator maha-ahmed-baker.
- 223 creator sonja-zillner.
- 223 type InProceedings.
- 223 label "Clinical Trial and Disease Search With Ad Hoc Interactive Ontology Alignments".
- 223 sameAs 223.
- 223 abstract "We will explain how an Linked Open Drug Data (LODD) application based on diseases, drugs, and clinical trials can be used to improve the (ontology-based) clinical reporting process while, at the same time, improving the patient follow-up treatment process. Specific requirements of the radiology domain let us aggregate RDF results from several LODD sources such as DrugBank, Diseasome, DailyMed, and LinkedCT. The idea is to use state-of-the-art string matching algorithms which allow for a ranked list of candidates and confidences of the approximation of the distance between two diseases at query time. Context information must be provided by the clinician who decides on the “related”-mappings of patient context and links he wants to follow in order to retrieve disease and medication information. The industrial prototype is implemented as a facetted browsing tool which allows for all these aspects of interactive ontology alignment.".
- 223 hasAuthorList authorList.
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- 223 keyword "LODD for radiology".
- 223 keyword "ad-hoc ontology matching".
- 223 keyword "facetted browsing".
- 223 keyword "healthcare".
- 223 keyword "interactivity".
- 223 title "Clinical Trial and Disease Search With Ad Hoc Interactive Ontology Alignments".
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- item-1 hasContent daniel-sonntag.
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- 228 creator aidan-hogan.
- 228 creator dafni-stampouli.
- 228 creator gavin-powell.
- 228 creator marc-mellotte.
- 228 type InProceedings.
- 228 label "Towards Inductive Query-Relaxation for RDF".
- 228 sameAs 228.
- 228 abstract "In this paper, we argue that query relaxation over RDF data is an important but largely overlooked research topic: the Semantic Web standards allow for answering crisp queries over crisp data, but what of use-cases that require approximate answers for structured queries over RDF data? We introduce one such use-case extracted from an EADS project that aims to fuse together intelligence information for police post-incident analysis. The concrete application for query relaxation involves matching (possibly vague) descriptions of entities involved in crimes to structured descriptions thereof in the database. Here, the core research questions are: (i) how can we formalise potentially vague structured queries in a generic manner; (ii) how can we support approximate, structured query-answering over RDF? We first discuss the use-case, formalise the problem, and survey current literature for possible approaches. Next, we present a proof-of-concept framework for enabling relaxation of structured entity-lookup queries, evaluating different distance measures for performing relaxation. We argue that, beyond our specific use-case, query relaxation is important to many potential use-cases for Semantic Web techniques, and worthy of further attention.".
- 228 hasAuthorList authorList.
- 228 isPartOf proceedings.
- 228 keyword "cooperative systems".
- 228 keyword "query answering".
- 228 keyword "query relaxation".
- 228 title "Towards Inductive Query-Relaxation for RDF".
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- item-1 hasContent aidan-hogan.
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- 255 creator kentaro-oguchi.
- 255 creator rahul-parundekar.
- 255 type InProceedings.
- 255 label "Learning Driver Preferences of POIs using a Semantic Web Knowledge System".
- 255 sameAs 255.
- 255 abstract "In this paper, we present the architecture and implementation of a Semantic Web Knowledge System that is employed to learn driver preferences for Points of Interest (POIs) using a content based approach. Initially, implicit & explicit feedback is collected from drivers about the places that they like. Data about these places is then retrieved from web sources and a POI preference model is built using machine learning algorithms. At a future time, when the driver searches for places that he/she might want to go to, his/her learnt model is used to personalize the result. The data that is used to learn preferences is represented as Linked Data with the help of a POI ontology, and retrieved from multiple POI search services by `lifting' it into RDF. This structured data is then combined with driver context and fed into a machine learning algorithm that produces a statistical model of the driver's preferences. This data and model is hosted in the cloud and is accessible via intelligent services and an access control mechanism to a client device like an in-vehicle navigation system. We describe the design and implementation of such a system that is currently in-use to study how a driver's preferences can be modeled by the vehicle platform and utilized for POI recommendations.".
- 255 hasAuthorList authorList.
- 255 isPartOf proceedings.
- 255 keyword "Architecture".
- 255 keyword "Linked Data".
- 255 keyword "Preference Modeling".
- 255 keyword "Semantic Web Knowledge Base".
- 255 title "Learning Driver Preferences of POIs using a Semantic Web Knowledge System".
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