Efficient Query Answering for OWL 2

Perez-Urbina, Hector; Horrocks, Ian; Motik, Boris

doi:10.1007/978-3-642-04930-9_31

Cited by 67 publications

(115 citation statements)

References 6 publications

(14 reference statements)

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“…Also, it is worth to explore whether other techniques like memoization, other SIP strategies [11], or ad-hoc query-rewriting techniques [9] can be exploited to further improve the performance.…”

Section: Discussionmentioning

confidence: 99%

QueryPIE: Backward Reasoning for OWL Horst over Very Large Knowledge Bases

Urbani

Harmelen

Schlobach

et al. 2011

The Semantic Web – ISWC 2011

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Abstract. Both materialization and backward-chaining as different modes of performing inference have complementary advantages and disadvantages.Materialization enables very efficient responses at query time, but at the cost of an expensive up front closure computation, which needs to be redone every time the knowledge base changes. Backward-chaining does not need such an expensive and change-sensitive precomputation, and is therefore suitable for more frequently changing knowledge bases, but has to perform more computation at query time.Materialization has been studied extensively in the recent semantic web literature, and is now available in industrial-strength systems. In this work, we focus instead on backward-chaining, and we present an hybrid algorithm to perform efficient backward-chaining reasoning on very large datasets expressed in the OWL Horst (pD * ) fragment.As a proof of concept, we have implemented a prototype called QueryPIE (Query Parallel Inference Engine), and we have tested its performance on different datasets of up to 1 billion triples. Our parallel implementation greatly reduces the reasoning complexity of a naive backward-chaining approach and returns results for single query-patterns in the order of milliseconds when running on a modest 8 machine cluster.To the best of our knowledge, QueryPIE is the first reported backward-chaining reasoner for OWL Horst that efficiently scales to a billion triples.

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Section: Discussionmentioning

confidence: 99%

QueryPIE: Backward Reasoning for OWL Horst over Very Large Knowledge Bases

Urbani

Harmelen

Schlobach

et al. 2011

The Semantic Web – ISWC 2011

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“…This technique has been used in the past in different scenarios, most notably Ontology-based Data Access (OBDA). In OBDA, data from one (or more) data sources can be queried in terms of a high-level ontological model, hiding from the users the internal schema and storage details of the data sources [21].…”

Section: Query Rewritingmentioning

confidence: 99%

“…A logic with special relevance in our case is ELHIO, one of the most expressive logics currently used for query rewriting. ELHIO is not FOL-reducible (in the presence of certain cyclic axioms the rewriting produces a recursive datalog program) but remains tractable (PTimecomplete [21]) for the rewriting process. For the description of our TBoxes we will use acyclic ELHIO.…”

Section: Query Rewritingmentioning

confidence: 99%

“…The details of the inference steps for the rewriting can be found in [18]. The transformation of the ontology to first order logic is performed according to the rules described in [21]. Afterwards, the UCQ is then converted into a union of basic graph patterns (BGPs).…”

Section: Query Answering Semanticsmentioning

confidence: 99%

“…The problem of answering queries over an ontology can be solved in different ways, and an important technique to do so is query rewriting. It is based on the idea of transforming the original query into an expanded query that captures the information of the ontology TBox [21]. Then this expanded query is evaluated over the ABox, providing answers that extract implicit knowledge of the data.…”

Section: Introductionmentioning

confidence: 99%

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Query Rewriting in RDF Stream Processing

Calbimonte

Mora

Corcho

2016

The Semantic Web. Latest Advances and New Domains

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Abstract. Querying and reasoning over RDF streams are two increasingly relevant areas in the broader scope of processing structured data on the Web. While RDF Stream Processing (RSP) has focused so far on extending SPARQL for continuous query and event processing, stream reasoning has concentrated on ontology evolution and incremental materialization. In this paper we propose a different approach for querying RDF streams over ontologies, based on the combination of query rewriting and stream processing. We show that it is possible to rewrite continuous queries over streams of RDF data, while maintaining efficiency for a wide range of scenarios. We provide a detailed description of our approach, as well as an implementation, StreamQR, which is based on the kyrie rewriter, and can be coupled with a native RSP engine, namely CQELS. Finally, we show empirical evidence of the performance of StreamQR in a series of experiments based on the SRBench query set.

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Ontology population for open‐source intelligence: A GATE‐based solution

et al. 2018

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Open-Source INTelligence is intelligence based on publicly available sources such as news sites, blogs, forums, etc. The Web is the primary source of information, but once data are crawled, they need to be interpreted and structured.Ontologies may play a crucial role in this process, but because of the vast amount of documents available, automatic mechanisms for their population are needed, starting from the crawled text. This paper presents an approach for the automatic population of predefined ontologies with data extracted from text and discusses the design and realization of a pipeline based on the General Architecture for Text Engineering system, which is interesting for both researchers and practitioners in the field. Some experimental results that are encouraging in terms of extracted correct instances of the ontology are also reported. Furthermore, the paper also describes an alternative approach and provides additional experiments for one of the phases of our pipeline, which requires the use of predefined dictionaries for relevant entities. Through such a variant, the manual workload required in this phase was reduced, still obtaining promising results. KEYWORDSgeneral architecture for text engineering (GATE), information extraction, internet as a data source, ontology population 2302 in an automatic way was not here taken into consideration. Domain ontologies usually have a very complex intentional structure to satisfy all the needs of the specific application domain at hand, and typically their design cannot be completely automatized. 3,5 In this work, we, instead, have pursued an approach for information extraction that assumed that a domain ontology was already available and that text should be mined to extract instances of ontology predicates.In order to solve this task, Named Entity Recognition (NER) 6 can be initially adopted. However, this approach only focuses on recognizing specific types of concepts (eg, persons, places, or organizations), without considering relationships among them. The Relational Information Extraction attempts, in addition, to identify the relationships that exist among concepts, as allowed by, eg, SystemT, * an IBM-developed commercial tool that is used to derive rich information from documents and emails. Our approach, thus, adopted both NER and Relational Extraction techniques, using open-source technologies.Among various existing open-source tools for information extraction (eg, LingPipe † or OpenNLP ‡ ), we decided to use General Architecture for Text Engineering (GATE), § given the flexibility it allows to customize its underlying architecture and to incorporate other external components developed by third parties. The extraction activity in GATE is typically carried out through different stages, each depending on the contingent needs of the user, who can, for instance, adopt existing dictionaries (aka Gazetteers) for NER or create new ones and/or specify tailored extraction rule sets through the use of the Java Annotation Pattern Language (JAPE). 7 In addition, GATE...

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Efficient Query Answering for OWL 2

Cited by 67 publications

References 6 publications

QueryPIE: Backward Reasoning for OWL Horst over Very Large Knowledge Bases

QueryPIE: Backward Reasoning for OWL Horst over Very Large Knowledge Bases

Query Rewriting in RDF Stream Processing

Ontology population for open‐source intelligence: A GATE‐based solution

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