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Bijan Parsia - One of the best experts on this subject based on the ideXlab platform.

  • The OWL Reasoner Evaluation (ORE) 2015 Competition Report
    Journal of Automated Reasoning, 2017
    Co-Authors: Bijan Parsia, Rafael S. Gonçalves, Birte Glimm, Nicolas Matentzoglu, Andreas Steigmiller
    Abstract:

    The OWL Reasoner Evaluation competition is an annual competition (with an associated workshop) that pits OWL 2 compliant Reasoners against each other on various standard reasoning tasks over naturally occurring problems. The 2015 competition was the third of its sort and had 14 Reasoners competing in six tracks comprising three tasks (consistency, classification, and realisation) over two profiles (OWL 2 DL and EL). In this paper, we discuss the design, execution and results of the 2015 competition with particular attention to lessons learned for benchmarking, comparative experiments, and future competitions.

  • the owl Reasoner evaluation ore 2015 resources
    International Semantic Web Conference, 2016
    Co-Authors: Bijan Parsia, Rafael S. Gonçalves, Birte Glimm, Nicolas Matentzoglu, Andreas Steigmiller
    Abstract:

    The OWL Reasoner Evaluation (ORE) Competition is an annual competition (with an associated workshop) which pits OWL 2 compliant Reasoners against each other on various standard reasoning tasks over naturally occurring problems. The 2015 competition was the third of its sort and had 14 Reasoners competing in six tracks comprising three tasks (consistency, classification, and realisation) over two profiles (OWL 2 DL and EL). In this paper, we outline the design of the competition and present the infrastructure used for its execution: the corpora of ontologies, the competition framework, and the submitted systems. All resources are publicly available on the Web, allowing users to easily re-run the 2015 competition, or reuse any of the ORE infrastructure for Reasoner experiments or ontology analysis.

  • a multi Reasoner justification based approach to Reasoner correctness
    International Semantic Web Conference, 2015
    Co-Authors: Michael J Lee, Bijan Parsia, Nicolas Matentzoglu, Uli Sattler
    Abstract:

    OWL 2 DL is a complex logic with reasoning problems that have a high worst case complexity. Modern Reasoners perform mostly very well on naturally occurring ontologies of varying sizes and complexity. This performance is achieved through a suite of complex optimisations (with complex interactions) and elaborate engineering. While the formal basis of the core Reasoner procedures are well understood, many optimisations are less so, and most of the engineering details (and their possible effect on Reasoner correctness) are unreviewed by anyone but the Reasoner developer. Thus, it is unclear how much confidence should be placed in the correctness of implemented Reasoners. To date, there is no principled, correctness unit test-like suite for simple language features and, even if there were, it is unclear that passing such a suite would say much about correctness on naturally occurring ontologies. This problem is not merely theoretical: Divergence in behaviour (thus known bugginess of implementations) has been observed in the OWL Reasoner Evaluation (ORE) contests to the point where a simple, majority voting procedure has been put in place to resolve disagreements.

  • verifying Reasoner correctness a justication based method
    International Workshop Description Logics, 2015
    Co-Authors: Michael J Lee, Nicolas Matentzoglu, Uli Sattler, Bijan Parsia
    Abstract:

    DL Reasoners are complex pieces of software that work on even more complex input which makes manual verification difficult. A single ontology can have hundreds or thousands of classes and thus its classification involve an unsurveyable number of subsumption tests. We propose a new method for debugging classification across multiple Reasoners which employs justifications generated from the set of entailments that Reasoners disagree upon to determine the cause of the disagreement.

  • OWL Reasoner Evaluation (ORE) Workshop 2013 Results: Short Report
    2014
    Co-Authors: Rafael S. Gonçalves, Bijan Parsia, Samantha Bail, Ernesto Jimenez-ruiz, Birte Glimm, Yevgeny Kazakov
    Abstract:

    Abstract. The OWL Reasoner evaluation (ORE) workshop brings together Reasoner developers and ontology engineers in order to discuss and evaluate the performance and robustness of modern Reasoners on OWL ontologies. In addition to paper submissions, the workshop featured a live and offline Reasoner competition where standard reasoning tasks were tested: classification, consistency, and concept satisfiability. The Reasoner competition is performed on several large corpora of reallife OWL ontologies obtained from the web, as well as user-submitted ontologies which were foundtobe challenging for Reasoners. Overall there were 14 Reasoner submissions for the competition, some of which dedicated to certain subsets or profiles of OWL 2, and implementing different algorithms and optimisations. In this report, we give an overview of the competition methodology and present a summary of its results, divided into the respective categories based on OWL 2 profiles and test corpora.

Ian Horrocks - One of the best experts on this subject based on the ideXlab platform.

  • pagoda pay as you go ontology query answering using a datalog Reasoner
    Journal of Artificial Intelligence Research, 2015
    Co-Authors: Yujiao Zhou, Bernardo Cuenca Grau, Yavor Nenov, Mark Kaminski, Ian Horrocks
    Abstract:

    Answering conjunctive queries over ontology-enriched datasets is a core reasoning task for many applications. Query answering is, however, computationally very expensive, which has led to the development of query answering procedures that sacrifice either expressive power of the ontology language, or the completeness of query answers in order to improve scalability. In this paper, we describe a hybrid approach to query answering over OWL 2 ontologies that combines a datalog Reasoner with a fully-fledged OWL 2 Reasoner in order to provide scalable 'pay-as-you-go' performance. The key feature of our approach is that it delegates the bulk of the computation to the datalog Reasoner and resorts to expensive OWL 2 reasoning only as necessary to fully answer the query. Furthermore, although our main goal is to efficiently answer queries over OWL 2 ontologies and data, our technical results are very general and our approach is applicable to first-order knowledge representation languages that can be captured by rules allowing for existential quantification and disjunction in the head; our only assumption is the availability of a datalog Reasoner and a fully-fledged Reasoner for the language of interest, both of which are used as 'black boxes'. We have implemented our techniques in the PAGOdA system, which combines the datalog Reasoner RDFox and the OWL 2 Reasoner HermiT. Our extensive evaluation shows that PAGOdA succeeds in providing scalable pay-as-you-go query answering for a wide range of OWL 2 ontologies, datasets and queries.

  • hermit an owl 2 Reasoner
    Journal of Automated Reasoning, 2014
    Co-Authors: Birte Glimm, Ian Horrocks, Boris Motik, Giorgos Stoilos, Zhe Wang
    Abstract:

    This system description paper introduces the OWL 2 Reasoner HermiT. The Reasoner is fully compliant with the OWL 2 Direct Semantics as standardised by the World Wide Web Consortium (W3C). HermiT is based on the hypertableau calculus, and it supports a wide range of standard and novel optimisations that improve the performance of reasoning on real-world ontologies. Apart from the standard OWL 2 reasoning task of entailment checking, HermiT supports several specialised reasoning services such as class and property classification, as well as a range of features outside the OWL 2 standard such as DL-safe rules, SPARQL queries, and description graphs. We discuss the system's architecture, and we present an overview of the techniques used to support the mentioned reasoning tasks. We further compare the performance of reasoning in HermiT with that of FaCT++ and Pellet--two other popular and widely used OWL 2 Reasoners.

  • pay as you go ontology query answering using a datalog Reasoner
    Description Logics, 2014
    Co-Authors: Yujiao Zhou, Bernardo Cuenca Grau, Yavor Nenov, Ian Horrocks
    Abstract:

    We describe a hybrid approach to conjunctive query answering over OWL 2 ontologies that combines a datalog Reasoner with a fully-fledged OWL 2 Reasoner in order to provide scalable “pay as you go” performance. Our approach delegates the bulk of the computation to the highly scalable datalog engine and resorts to expensive OWL 2 reasoning only as necessary to fully answer the query. We have implemented a prototype system that uses RDFox as a datalog Reasoner, and HermiT as an OWL 2 Reasoner. Our evaluation over both benchmark and realistic ontologies and datasets suggests the feasibility of our approach.

  • complete query answering over horn ontologies using a triple store
    International Semantic Web Conference, 2013
    Co-Authors: Yujiao Zhou, Bernardo Cuenca Grau, Yavor Nenov, Ian Horrocks
    Abstract:

    In our previous work, we showed how a scalable OWL 2 RL Reasoner can be used to compute both lower and upper bound query answers over very large datasets and arbitrary OWL 2 ontologies. However, when these bounds do not coincide, there still remain a number of possible answer tuples whose status is not determined. In this paper, we show how in the case of Horn ontologies one can exploit the lower and upper bounds computed by the RL Reasoner to efficiently identify a subset of the data and ontology that is large enough to resolve the status of these tuples, yet small enough so that the status can be computed using a fully-fledged OWL 2 Reasoner. The resulting hybrid approach has enabled us to compute exact answers to queries over datasets and ontologies where previously only approximate query answering was possible.

  • making the most of your triple store query answering in owl 2 using an rl Reasoner
    The Web Conference, 2013
    Co-Authors: Yujiao Zhou, Bernardo Cuenca Grau, Ian Horrocks, Zhe Wu, Jay Banerjee
    Abstract:

    Triple stores implementing the RL profile of OWL 2 are becoming increasingly popular. In contrast to unrestricted OWL 2, the RL profile is known to enjoy favourable computational properties for query answering, and state-of-the-art RL Reasoners such as OWLim and Oracle's native inference engine of Oracle Spatial and Graph have proved extremely successful in industry-scale applications. The expressive restrictions imposed by OWL 2 RL may, however, be problematical for some applications. In this paper, we propose novel techniques that allow us (in many cases) to compute exact query answers using an off-the-shelf RL Reasoner, even when the ontology is outside the RL profile. Furthermore, in the cases where exact query answers cannot be computed, we can still compute both lower and upper bounds on the exact answers. These bounds allow us to estimate the degree of incompleteness of the RL Reasoner on the given query, and to optimise the computation of exact answers using a fully-fledged OWL 2 Reasoner. A preliminary evaluation using the RDF Semantic Graph feature in Oracle Database has shown very promising results with respect to both scalability and tightness of the bounds.

Boontawee Suntisrivaraporn - One of the best experts on this subject based on the ideXlab platform.

  • cel a polynomial time Reasoner for life science ontologies
    International Joint Conference on Automated Reasoning, 2006
    Co-Authors: Franz Baader, Carsten Lutz, Boontawee Suntisrivaraporn
    Abstract:

    CEL (Classifier for ${\mathcal{E}{L}}$) is a Reasoner for the small description logic ${\mathcal{E}{L}}^+$ which can be used to compute the subsumption hierarchy induced by ${\mathcal{E}{L}}^+$ ontologies. The most distinguishing feature of CEL is that, unlike all other modern DL Reasoners, it is based on a polynomial-time subsumption algorithm, which allows it to process very large ontologies in reasonable time. In spite of its restricted expressive power, ${\mathcal{E}{L}}^+$ is well-suited for formulating life science ontologies.

  • cel a polynomial time Reasoner for life science ontologies
    Lecture Notes in Computer Science, 2006
    Co-Authors: Franz Baader, Carsten Lutz, Boontawee Suntisrivaraporn
    Abstract:

    CEL (Classifier for eL) is a Reasoner for the small description logic ∈L + which can be used to compute the subsuinption hierarchy induced by eL + ontologies. The most distinguishing feature of CEL is that, unlike all other modern DL Reasoners, it is based on a polynomial-time subsumption algorithm, which allows it to process very large ontologies in reasonable time. In spite of its restricted expressive power, eL+ is well-suited for formulating life science ontologies.

Franz Baader - One of the best experts on this subject based on the ideXlab platform.

  • cel a polynomial time Reasoner for life science ontologies
    International Joint Conference on Automated Reasoning, 2006
    Co-Authors: Franz Baader, Carsten Lutz, Boontawee Suntisrivaraporn
    Abstract:

    CEL (Classifier for ${\mathcal{E}{L}}$) is a Reasoner for the small description logic ${\mathcal{E}{L}}^+$ which can be used to compute the subsumption hierarchy induced by ${\mathcal{E}{L}}^+$ ontologies. The most distinguishing feature of CEL is that, unlike all other modern DL Reasoners, it is based on a polynomial-time subsumption algorithm, which allows it to process very large ontologies in reasonable time. In spite of its restricted expressive power, ${\mathcal{E}{L}}^+$ is well-suited for formulating life science ontologies.

  • cel a polynomial time Reasoner for life science ontologies
    Lecture Notes in Computer Science, 2006
    Co-Authors: Franz Baader, Carsten Lutz, Boontawee Suntisrivaraporn
    Abstract:

    CEL (Classifier for eL) is a Reasoner for the small description logic ∈L + which can be used to compute the subsuinption hierarchy induced by eL + ontologies. The most distinguishing feature of CEL is that, unlike all other modern DL Reasoners, it is based on a polynomial-time subsumption algorithm, which allows it to process very large ontologies in reasonable time. In spite of its restricted expressive power, eL+ is well-suited for formulating life science ontologies.

Evren Sirin - One of the best experts on this subject based on the ideXlab platform.

  • pelletspatial a hybrid rcc 8 and rdf owl reasoning and query engine
    OWL: Experiences and Directions, 2009
    Co-Authors: Markus Stocker, Evren Sirin
    Abstract:

    In this paper, we present PelletSpatial, a qualitative spatial reasoning engine implemented on top of Pellet. PelletSpatial provides consistency checking and query answering over spatial data represented with the Region Connection Calculus (RCC). It supports all RCC-8 relations as well as standard RDF/OWL semantic relations, both represented in RDF/OWL. As such, it can answer mixed SPARQL queries over both relation types. PelletSpatial implements two RCC Reasoners: (a) A Reasoner based on the semantics preserving translation of RCC relations to OWL-DL class axioms and (b) a Reasoner based on the RCC composition table that implements a path-consistency algorithm. We discuss the details of two implementation approaches and focus on some of their respective advantages and disadvantages.

  • pellet a practical owl dl Reasoner
    Journal of Web Semantics, 2007
    Co-Authors: Evren Sirin, Bernardo Cuenca Grau, Bijan Parsia, Aditya Kalyanpur, Yarden Katz
    Abstract:

    In this paper, we present a brief overview of Pellet: a complete OWL-DL Reasoner with acceptable to very good performance, extensive middleware, and a number of unique features. Pellet is the first sound and complete OWL-DL Reasoner with extensive support for reasoning with individuals (including nominal support and conjunctive query), user-defined datatypes, and debugging support for ontologies. It implements several extensions to OWL-DL including a combination formalism for OWL-DL ontologies, a non-monotonic operator, and preliminary support for OWL/Rule hybrid reasoning. Pellet is written in Java and is open source.

  • sparql dl sparql query for owl dl
    OWL: Experiences and Directions, 2007
    Co-Authors: Evren Sirin, Bijan Parsia
    Abstract:

    There are many query languages (QLs) that can be used to query RDF and OWL ontologies but neither type is satisfactory for querying OWL-DL ontologies. RDF-based QLs (RDQL, SeRQL, SPARQL) are harder to give a semantics w.r.t. OWL-DL and are more powerful than what OWL-DL Reasoners can provide. DL-based QLs (DIG ask queries, nRQL) have clear semantics but are not powerful enough in the general case. In this paper we describe SPARQL-DL, a substantial subset of SPARQL for which we provide a clear OWL-DL based semantics. SPARQL-DL is significantly more expressive than existing DL QLs (by allowing mixed TBox/RBox/ABox queries) and can still be implemented without too much effort on top of existing OWL-DL Reasoners. We discuss design decisions and practical issues that arise for defining SPARQL-DL and report about our preliminary prototype implemented on top of OWL-DL Reasoner Pellet.

  • pellet an owl dl Reasoner
    International Workshop Description Logics, 2004
    Co-Authors: Evren Sirin, Bijan Parsia
    Abstract:

    In order to gain experience with description logic Reasoner, and to contribute to the OWL Candidate Recommendation process, a small team at MINDSWAP set out to implement a tableau Reasoner for the Lite and DL dialects of OWL (corresponding roughly to the description logics SHIF(D) and SHION(D)). Our group found existing, available DL Reasoners lacking for our purposes, because we needed an open-source tool that provides ABox reasoning, that does not make Unique Name assumption, supports entailment checks and works with XML Schema datatypes. Pellet has been developed to addresses these issues and has become both our test bed for experiments with DL and Semantic Web reasoning, as well as our standard reasoning component. While not (yet) in the performance range of Racer or Fact, it has many usability features that makes it a good choice for various lighter weight situations. Technically, Pellet is a sound and complete tableau Reasoners for SHIN(D) and SHON(D) (with ABoxes), and a sound but incomplete tableau Reasoner for SHION(D) (with ABoxes). Pellet has the usual suite of optimizations including lazy unfolding, absorption, dependency directed backjumping, and semantic branching. It incorporates datatype reasoning for the built-in primitive XML Schema datatypes. Pellet is implemented in pure Java and available as open source software.