304 research outputs found
A survey of current, stand-alone OWL Reasoners
Abstract. We present a survey of the current OWL reasoner landscape. Through literature and web search we have identified 35 OWL reasoners that are, at least to some degree, actively maintained. We conducted a survey directly addressing the respective developers, and collected 33 responses. We present an analysis of the survey, characterising all reasoners across a wide range of categories such as supported expressiveness and reasoning services. We will also provide some insight about ongoing research efforts and a rough categorisation of reasoner calculi
OWL Reasoners still useable in 2023
In a systematic literature and software review over 100 OWL reasoners/systems
were analyzed to see if they would still be usable in 2023. This has never been
done in this capacity. OWL reasoners still play an important role in knowledge
organisation and management, but the last comprehensive surveys/studies are
more than 8 years old. The result of this work is a comprehensive list of 95
standalone OWL reasoners and systems using an OWL reasoner. For each item,
information on project pages, source code repositories and related
documentation was gathered. The raw research data is provided in a Github
repository for anyone to use
Web Reasoning and Rule Systems: 7th International Conference, RR 2013, Mannheim, Germany, July 27-29, 2013, Proceedings
This book constitutes the refereed proceedings of the 7th International Conference on Web Reasoning and Rule Systems, RR 2013, held in Manheim, Germany in July 2013. The 19 revised research papers and 4 technical communications presented together with 2 invited talks and 1 tutorial talk were carefully reviewed and selected from 34 submissions. The scope of conference is decision making, planning, and intelligent agents, reasoning, machine learning, knowledge extraction and IR technologies, large-scale data management and reasoning on the web of data, data integration, dataspaces and ontology-based data access, non-standard reasoning, algorithms for distributed, parallelized, and scalable reasoning, and system descriptions and experimentation
Description Logics Go Second-Order -- Extending EL with Universally Quantified Concepts
The study of Description Logics have been historically mostly focused on
features that can be translated to decidable fragments of first-order logic. In
this paper, we leave this restriction behind and look for useful and decidable
extensions outside first-order logic. We introduce universally quantified
concepts, which take the form of variables that can be replaced with arbitrary
concepts, and define two semantics of this extension. A schema semantics allows
replacements of concept variables only by concepts from a particular language,
giving us axiom schemata similar to modal logics. A second-order semantics
allows replacement of concept variables with arbitrary subsets of the domain,
which is similar to quantified predicates in second-order logic.
To study the proposed semantics, we focus on the extension of the description
logic . We show that for a useful fragment of the extension, the
conclusions entailed by the different semantics coincide, allowing us to use
classical reasoning algorithms even for the second-order
semantics. For a slightly smaller, but still useful, fragment, we were also
able to show polynomial decidability of the extension. This fragment, in
particular, can express a generalized form of role chain axioms, positive self
restrictions, and some forms of (local) role-value-maps from KL-ONE, without
requiring any additional constructors
Efficient Maximum A-Posteriori Inference in Markov Logic and Application in Description Logics
Maximum a-posteriori (MAP) query in statistical relational models computes the most probable world given evidence and further knowledge about the domain. It is arguably one of the most important types of computational problems, since it is also used as a subroutine in weight learning algorithms. In this thesis, we discuss an improved inference algorithm and an application for MAP queries. We focus on Markov logic (ML) as statistical relational formalism. Markov logic combines Markov networks with first-order logic by attaching weights to first-order formulas.
For inference, we improve existing work which translates MAP queries to integer linear programs (ILP). The motivation is that existing ILP solvers are very stable and fast and are able to precisely estimate the quality of an intermediate solution. In our work, we focus on improving the translation process such that we result in ILPs having fewer variables and fewer constraints. Our main contribution is the Cutting Plane Aggregation (CPA) approach which leverages symmetries in ML networks and parallelizes MAP inference. Additionally, we integrate the cutting plane inference (Riedel 2008) algorithm which significantly reduces the number of groundings by solving multiple smaller ILPs instead of one large ILP. We present the new Markov logic engine RockIt which outperforms state-of-the-art engines in standard Markov logic benchmarks.
Afterwards, we apply the MAP query to description logics. Description logics (DL) are knowledge representation formalisms whose expressivity is higher than propositional logic but lower than first-order logic. The most popular DLs have been standardized in the ontology language OWL and are an elementary component in the Semantic Web. We combine Markov logic, which essentially follows the semantic of a log-linear model, with description logics to log-linear description logics. In log-linear description logic weights can be attached to any description logic axiom. Furthermore, we introduce a new query type which computes the most-probable 'coherent' world. Possible applications of log-linear description logics are mainly located in the area of ontology learning and data integration. With our novel log-linear description logic reasoner ELog, we experimentally show that more expressivity increases quality and that the solutions of optimal solving strategies have higher quality than the solutions of approximate solving strategies
Forschungsbericht Universität Mannheim 2008 / 2009
Die Universität Mannheim hat seit ihrer Entstehung ein spezifisches Forschungsprofil,
welches sich in ihrer Entwicklung und derz
eitigen Struktur deutlich widerspiegelt. Es ist geprägt von national und international
sehr anerkannten Wirtschafts- und
Sozialwissenschaften und deren Vernetzung mit leistungsstarken Geisteswissenschaften, Rechtswissenschaft sowie Mathematik und Informatik.
Die Universität Mannheim wird auch in Zukunft
einerseits die Forschungsschwerpunkte in den Wirtschafts- und Sozialwissenschaften fördern und andererseits eine interdisziplinäre Kultur im
Zusammenspiel aller Fächer der Universität
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Proceedings of The Tenth International Workshop on Ontology Matching (OM-2015)
shvaiko2016aInternational audienceno abstrac
Neural and Symbolic AI - mind the gap! Aligning Artificial Neural Networks and Ontologies
Artificial neural networks have been the key to solve a variety of different problems.
However, neural network models are still essentially regarded as black boxes, since they
do not provide any human-interpretable evidence as to why they output a certain re sult. In this dissertation, we address this issue by leveraging on ontologies and building
small classifiers that map a neural network’s internal representations to concepts from
an ontology, enabling the generation of symbolic justifications for the output of neural
networks. Using two image classification problems as testing ground, we discuss how to
map the internal representations of a neural network to the concepts of an ontology, exam ine whether the results obtained by the established mappings match our understanding
of the mapped concepts, and analyze the justifications obtained through this method
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