36 research outputs found
Modular Materialisation of Datalog Programs
The semina\"ive algorithm can materialise all consequences of arbitrary
datalog rules, and it also forms the basis for incremental algorithms that
update a materialisation as the input facts change. Certain (combinations of)
rules, however, can be handled much more efficiently using custom algorithms.
To integrate such algorithms into a general reasoning approach that can handle
arbitrary rules, we propose a modular framework for materialisation computation
and its maintenance. We split a datalog program into modules that can be
handled using specialised algorithms, and handle the remaining rules using the
semina\"ive algorithm. We also present two algorithms for computing the
transitive and the symmetric-transitive closure of a relation that can be used
within our framework. Finally, we show empirically that our framework can
handle arbitrary datalog programs while outperforming existing approaches,
often by orders of magnitude.Comment: Accepted at AAAI 201
Ontology Module Extraction via Datalog Reasoning
Module extraction - the task of computing a (preferably small) fragment M of
an ontology T that preserves entailments over a signature S - has found many
applications in recent years. Extracting modules of minimal size is, however,
computationally hard, and often algorithmically infeasible. Thus, practical
techniques are based on approximations, where M provably captures the relevant
entailments, but is not guaranteed to be minimal. Existing approximations,
however, ensure that M preserves all second-order entailments of T w.r.t. S,
which is stronger than is required in many applications, and may lead to large
modules in practice. In this paper we propose a novel approach in which module
extraction is reduced to a reasoning problem in datalog. Our approach not only
generalises existing approximations in an elegant way, but it can also be
tailored to preserve only specific kinds of entailments, which allows us to
extract significantly smaller modules. An evaluation on widely-used ontologies
has shown very encouraging results.Comment: 13 pages. To appear in AAAI-1
Approximate Assertional Reasoning Over Expressive Ontologies
In this thesis, approximate reasoning methods for scalable assertional reasoning are provided whose computational properties can be established in a well-understood way, namely in terms of soundness and completeness, and whose quality can be analyzed in terms of statistical measurements, namely recall and precision. The basic idea of these approximate reasoning methods is to speed up reasoning by trading off the quality of reasoning results against increased speed
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
KLM-Style Defeasible Reasoning for Datalog
In many problem domains, particularly those related to mathematics and philosophy, classical logic has enjoyed great success as a model of valid reasoning and discourse. For real-world reasoning tasks, however, an agent typically only has partial knowledge of its domain, and at most a statistical understanding of relationships between properties. In this context, classical inference is considered overly restrictive, and many systems for non-monotonic reasoning have been proposed in the literature to deal with these tasks. A notable example is the Klm framework, which describes an agent's defeasible knowledge qualitatively in terms of conditionals of the form “if A, then typically B”. The goal of this research project is to investigate Klm-style semantics for defeasible reasoning over Datalog knowledge bases. Datalog is a declarative logic programming language, designed for querying large deductive databases. Syntactically, it can be viewed as a computationally feasible fragment of firstorder logic, so this continues a recent line of work in which the Klm framework is lifted to more expressive languages
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
Tractable approximate deduction for OWL
Acknowledgements This work has been partially supported by the European project Marrying Ontologies and Software Technologies (EU ICT2008-216691), the European project Knowledge Driven Data Exploitation (EU FP7/IAPP2011-286348), the UK EPSRC project WhatIf (EP/J014354/1). The authors thank Prof. Ian Horrocks and Dr. Giorgos Stoilos for their helpful discussion on role subsumptions. The authors thank Rafael S. Gonçalves et al. for providing their hotspots ontologies. The authors also thank BoC-group for providing their ADOxx Metamodelling ontologies.Peer reviewedPostprin