OWLIM – A Pragmatic Semantic Repository for OWL

Kiryakov, Atanas; Ognyanov, Damyan; Manov, Dimitar

doi:10.1007/11581116_19

Cited by 191 publications

(126 citation statements)

References 2 publications

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“…We derived the closure of SNOMED CT with CB [7], the fastest reasoner currently available for this nomenclature [4]. Then, we loaded the closure, our OWL schema and the patient data into BigOWLIM 3.5 [8], which is optimised for fast SPARQL evaluation and was allowed a maximum of 6GB memory. We employed openRDF Sesame 2.4 [2], which supports SPARQL 1.1 4 query features such as expressions, aggregates and negation.…”

Section: Resultsmentioning

confidence: 99%

Towards the Automated Calculation of Clinical Quality Indicators

Dentler

Teije

Cornet

et al. 2012

Knowledge Representation for Health-Care

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Abstract. To measure the quality of care in order to identify whether and how it can be improved is of increasing importance, and several organisations define quality indicators as tools for such measurement. The values of these quality indicators should ideally be calculated automatically based on data that is being collected during the care process. The central idea behind this paper is that quality indicators can be regarded as semantic queries that retrieve patients who fulfil certain constraints, and that indicators that are formalised as semantic queries can be calculated automatically by being run against patient data. We report our experiences in manually formalising exemplary quality indicators from natural language into SPARQL queries, and prove the concept by running the resulting queries against self-generated synthetic patient data. Both the queries and the patient data make use of SNOMED CT to represent relevant concepts. Our experimental results are promising: we ran eight queries against a dataset of 300,000 synthetically generated patients, and retrieved consistent results within acceptable time.

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Section: Resultsmentioning

confidence: 99%

Towards the Automated Calculation of Clinical Quality Indicators

Dentler

Teije

Cornet

et al. 2012

Knowledge Representation for Health-Care

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“…-In common with other rule based approaches [13,6], our approach to type inference is much more efficient than tableaux based reasoners [2], since we do not need to use a process of refutation to infer instances as being members of classes.…”

Section: Expressed In DLmentioning

confidence: 99%

“…The fact that the system is memory based provides fast load and query times, but means that it does not scale beyond tens of thousands of individuals. OWLIM [6] is similar in both features and problems, but supports a smaller subset of OWL-DL than O-DEVICE.…”

Section: Related Workmentioning

confidence: 99%

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SQOWL: Type Inference in an RDBMS

McBrien

Rizopoulos

Smith

2010

Conceptual Modeling – ER 2010

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Abstract. In this paper we describe a method to perform type inference over data stored in an RDBMS, where rules over the data are specified using OWL-DL. Since OWL-DL is an implementation of the Description Logic (DL) called SHOIN (D), we are in effect implementing a method for SHOIN (D) reasoning in relational databases. Reasoning may be broken down into two processes of classification and type inference. Classification may be performed efficiently by a number of existing reasoners, and since classification alters the schema, it need only be performed once for any given relational schema as a preprocessor of the schema before creation of a database schema. However, type inference needs to be performed for each data value added to the database, and hence needs to be more tightly coupled with the database system. We propose a technique to meet this requirement based on the use of triggers, which is the first technique to fully implement SHOIN (D) as part of normal transaction processing.

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“…Application developers thus often use scalable but incomplete reasoners-that is, reasoners that, for some query, ontology, and dataset, fail to compute all answers to the query. Examples of such incomplete reasoners include state of the art RDF management systems, such as Jena [8], OWLim [6], DLE-Jena [9], and Oracle's Semantic Store [17], which typically provide completeness guarantees only for ontologies expressed in the OWL 2 RL [11] profile of OWL 2 DL.…”

Section: Introductionmentioning

confidence: 99%

Repairing Ontologies for Incomplete Reasoners

Stoilos

Grau

Motik

et al. 2011

The Semantic Web – ISWC 2011

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Abstract. The need for scalable query answering often forces Semantic Web applications to use incomplete OWL 2 reasoners, which in some cases fail to derive all answers to a query. This is clearly undesirable, and in some applications may even be unacceptable. To address this problem, we investigate the problem of 'repairing' an ontology T -that is, computing an ontology R such that a reasoner that is incomplete for T becomes complete when used with T ∪R. We identify conditions on T and the reasoner that make this possible, present a practical algorithm for computing R, and present a preliminary evaluation which shows that, in some realistic cases, repairs are feasible to compute, reasonable in size, and do not significantly affect reasoner performance.

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OWLIM – A Pragmatic Semantic Repository for OWL

Cited by 191 publications

References 2 publications

Towards the Automated Calculation of Clinical Quality Indicators

Towards the Automated Calculation of Clinical Quality Indicators

SQOWL: Type Inference in an RDBMS

Repairing Ontologies for Incomplete Reasoners

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