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The modular structure of an ontology: atomic decomposition
, 2011
"... Extracting a subset of a given ontology that captures all the ontology’s knowledge about a specified set of terms is a well-understood task. This task can be based, for instance, on locality-based modules. However, a single module does not allow us to understand neither topicality, connectedness, st ..."
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Cited by 36 (3 self)
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Extracting a subset of a given ontology that captures all the ontology’s knowledge about a specified set of terms is a well-understood task. This task can be based, for instance, on locality-based modules. However, a single module does not allow us to understand neither topicality, connectedness, structure, or superfluous parts of an ontology, nor agreement between actual and intended modeling. The strong logical properties of locality-based modules suggest that the family of all such modules of an ontology can support comprehension of the ontology as a whole. However, extracting that family is not feasible, since the number of localitybased modules of an ontology can be exponential w.r.t. its size. In this paper we report on a new approach that enables us to efficiently extract a polynomial representation of the family of all locality-based modules of an ontology. We also describe the fundamental algorithm to pursue this task, and report on experiments carried out and results obtained. 1
Extracting Modules from Ontologies: A Logic-Based Approach
, 2009
"... The ability to extract meaningful fragments from an ontology is essential for ontology reuse. We propose a definition of a module that guarantees to completely capture the meaning of a given set of terms, i.e., to include all axioms relevant to the meaning of these terms. We show that the problem o ..."
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Cited by 26 (1 self)
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The ability to extract meaningful fragments from an ontology is essential for ontology reuse. We propose a definition of a module that guarantees to completely capture the meaning of a given set of terms, i.e., to include all axioms relevant to the meaning of these terms. We show that the problem of determining whether a subset of an ontology is a module for a given vocabulary is undecidable even for OWL DL. Given these negative results, we propose sufficient conditions for a for a fragment of an ontology to be a module. We propose an algorithm for computing modules based on those conditions and present our experimental results on a set of real-world ontologies of varying size and complexity.
Parallel TBox Classification in Description Logics – First Experimental Results
"... Abstract. One of the most frequently used inference services of description logic reasoners classifies all named classes of OWL ontologies into a subsumption hierarchy. Due to emerging OWL ontologies from the web community consisting of up to hundreds of thousand of named classes and the increasing ..."
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Cited by 19 (3 self)
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Abstract. One of the most frequently used inference services of description logic reasoners classifies all named classes of OWL ontologies into a subsumption hierarchy. Due to emerging OWL ontologies from the web community consisting of up to hundreds of thousand of named classes and the increasing availability of multi-processor and multi- or many-core computers, we extend our work on parallel TBox classification and propose a new algorithm that is sound and complete and demonstrates in a first experimental evaluation a low overhead w.r.t. subsumption tests (less than 3%) if compared with sequential classification. 1
Incremental Classification of Description Logics Ontologies
"... The development of ontologies involves continuous but relatively small modifications. However, existing ontology reasoners do not take advantage of the similarities between different versions of an ontology. In this paper, we propose a collection of techniques for incremental reasoning — that is, r ..."
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Cited by 10 (1 self)
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The development of ontologies involves continuous but relatively small modifications. However, existing ontology reasoners do not take advantage of the similarities between different versions of an ontology. In this paper, we propose a collection of techniques for incremental reasoning — that is, reasoning that reuses information obtained from previous versions of an ontology. We have applied our results to incremental classification of OWL ontologies and found significant improvement over regular classification time on a set of real-world ontologies.
Stream Reasoning: Where We Got So Far
"... Abstract. Data Streams- unbounded sequences of time-varying data elements- are pervasive. They occur in a variety of modern applications including the Web where blogs, feeds, and microblogs are increasingly adopted to distribute and present information in real-time streams. We foresee the need for l ..."
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Cited by 7 (0 self)
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Abstract. Data Streams- unbounded sequences of time-varying data elements- are pervasive. They occur in a variety of modern applications including the Web where blogs, feeds, and microblogs are increasingly adopted to distribute and present information in real-time streams. We foresee the need for languages, tools and methodologies for representing, managing and reasoning on data streams for the Semantic Web. We collectively name those research chapters Stream Reasoning. In this extended abstract, we motivate the need for investigating Steam Reasoning; we characterize the notion of Stream Reasoning; we report the results obtained by Politecnico di Milano in studying Stream Reasoning from 2008 to 2010; and we close the paper with a short review of the related works and some outlooks. 1
F.: Towards expressive stream reasoning
- Semantic Challenges in Sensor Networks. Number 10042 in Dagstuhl Seminar Proceedings, Dagstuhl, Germany, Schloss Dagstuhl - Leibniz-Zentrum fuer Informatik
, 2010
"... Stream Data processing has become a popular topic in database research addressing the challenge of efficiently answering queries over continuous data streams. Meanwhile data streams have become more and more important as a basis for higher level decision processes that require complex reasoning over ..."
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Cited by 6 (1 self)
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Stream Data processing has become a popular topic in database research addressing the challenge of efficiently answering queries over continuous data streams. Meanwhile data streams have become more and more important as a basis for higher level decision processes that require complex reasoning over data streams and rich background knowledge. In previous work the foundation for complex reasoning over streams and background knowledge was laid by introducing technologies for wrapping and querying streams in the RDF data format and by supporting simple forms of reasoning in terms of incremental view maintenance. In this paper, we discuss how this existing technologies should be extended toward richer forms of reasoning using Sensor Networks as a motivating example. 1
Towards Parallel Classification of TBoxes
"... Abstract. One of the most frequently used inference services of description logic reasoners is the classification of TBoxes with a subsumption hierarchy of all named concepts as the result. In response to (i) emerging TBoxes from the semantic web community consisting of up to hundreds of thousand of ..."
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Cited by 5 (0 self)
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Abstract. One of the most frequently used inference services of description logic reasoners is the classification of TBoxes with a subsumption hierarchy of all named concepts as the result. In response to (i) emerging TBoxes from the semantic web community consisting of up to hundreds of thousand of named concepts and (ii) the increasing availability of multi-processor and multi- or many-core computers, we propose a parallel approach for TBox classification. First experiments on parallelizing well-known algorithms for TBox classification were conducted to study the trade-off between incompleteness and speed improvement. 1
Streaming the Web: Reasoning over Dynamic Data
, 2014
"... In the last few years a new research area, called stream reasoning, emerged to bridge the gap between reasoning and stream processing. While current reasoning approaches are designed to work on mainly static data, the Web is, on the other hand, extremely dynamic: information is frequently changed an ..."
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Cited by 5 (0 self)
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In the last few years a new research area, called stream reasoning, emerged to bridge the gap between reasoning and stream processing. While current reasoning approaches are designed to work on mainly static data, the Web is, on the other hand, extremely dynamic: information is frequently changed and updated, and new data is continuously generated from a huge number of sources, often at high rate. In other words, fresh information is constantly made available in the form of streams of new data and updates. Despite some promising investigations in the area, stream reasoning is still in its infancy, both from the perspective of models and theories development, and from the perspective of systems and tools design and implementation. The aim of this paper is threefold: (i) we identify the requirements coming from different application scenarios, and we isolate the problems they pose; (ii) we survey existing approaches and proposals in the area of stream reasoning, highlighting their strengths and limitations; (iii) we draw a research agenda to guide the future research and development of stream reasoning. In doing so, we also analyze related research fields to extract algorithms, models, techniques, and solutions that could be useful in the area of stream reasoning.
