Parallel computation techniques for ontology reasoning

2007

Abstract This report summarizes the reasoning techniques and algorithms developed in the TONES project that realize the reasoning services identified as fundamental for ontology-based access, processing, and usage. In addition to a detailed presentation of the algorithms, we report on their computational properties and investigate solutions for the expressivity and data scalability problems. c 2007/TONES–July, 2007 2/143 TONES-D18–v. 1.1

ABSTRACT Ontologies, in the OWL language, form the basis of the Semantic Web: they define concepts and relationships, and are thus the fundamental model for encoding information. As the Semantic Web has been more widely adopted, extremely large ontologies have begun to emerge, particularly in the life sciences. Reasoning about such ontologies requires scalability beyond that of current Description Logic-based reasoning tools.

Lecture Notes in Computer Science, 2012

In order to support the vision of the Semantic Web, ontology reasoning needs to be highly scalable and efficient. A natural way to achieve scalability and efficiency is to develop parallel ABox reasoning algorithms for tractable OWL 2 profiles to distribute the load between different computation units within a reasoning system. So far there have been some work on parallel ABox reasoning algorithms for the pD* fragment of OWL 2 RL. However, there is still no work on parallel ABox reasoning algorithm for OWL 2 EL, which is the language for many influential ontologies (such as the SNOMED CT ontology). In this paper, we extend a parallel TBox reasoning algorithm [5] for ELHR+ to parallel ABox reasoning algorithms for ELH ⊥,R+ , which also supports the bottom concept so as to model disjointness and inconsistency. In design of algorithms, we exploit the characteristic of ABox reasonings to improve parallelisation and reduce unnecessary resource cost. Our evaluation shows that a naive implementation of our approach can compute all ABox entailments of a Not-Galen − ontology with about 1 million individuals and 9 million axioms in about 3 minutes.

2005

The number of semantic web tools assumed to operate on multiple ontologies from the same domain is growing far ahead of availability of suitable testing material. We suggest a domain and principles for building a widely-usable collection of parallel ontologies. An initial fragment of the collection has been created and processed with two among available tools.

2005

EU-IST Network of Excellence (NoE) IST-2004-507482 KWEB Deliverable D2.1.3.1 (WP2.1)

Lecture Notes in Computer Science, 2012

Theory and practice in ontology management are drifting away from monolithic ontologies towards ontology networks. Processing interconnected knowledge models, e.g. through reasoning, can provide greater amounts of explicit knowledge, but at a high computational cost if the whole knowledge base has to be handled by concurrent processes. Our research tackles this problem via a software engineering approach. We devised a framework that combines privileged containers for ontology models with volatile containers for instance data that vary frequently. A reference implementation was developed in an Apache project for content management, and its adoption is providing data and use cases for validating it with objects from Fedora Commons repositories.

2011

2007

OWL is a recommended language for publishing and sharing ontologies on the Semantic Web. To manage the ontologies, several OWL data management systems have been proposed. However, the existing systems have limitations of the scalability and the reasoning. In this paper, we propose an OWL data management system, ONTOMS, which stores OWL data into class based relations, performs complete inverseOf, symmetric, and transitive reasoning for instances, and efficiently evaluates OWL-QL queries against ontologies in a relational database.

Proceedings of the International conference on …, 2002

Lecture Notes in Computer Science, 2013

Ontologies are becoming increasingly important in largescale information systems such as healthcare systems. Ontologies can represent knowledge from clinical guidelines, standards, and practices used in the healthcare sector and may be used to drive decision support systems for healthcare, as well as store data (facts) about patients. Reallife ontologies may get very large (with millions of facts or instances). The effective use of ontologies requires not only a well-designed and well-defined ontology language, but also adequate support from reasoning tools. Main memory-based reasoners are not suitable for reasoning over large ontologies due to the high time and space complexity of their reasoning algorithms. In this paper, we present OwlOntDB, a scalable reasoning system for OWL 2 RL ontologies with a large number of instances, i.e., large ABoxes. We use a logic-based approach to develop the reasoning system by extending the Description Logic Programs (DLP) mapping between OWL 1 ontologies and datalog rules, to accommodate the new features of OWL 2 RL. We first use a standard DL reasoner to create a complete class hierarchy from an OWL 2 RL ontology, and translate each axiom and fact from the ontology to its equivalent datalog rule(s) using the extended DLP mapping. We materialize the ontology to infer implicit knowledge using a novel database-driven forward chaining method, storing asserted and inferred knowledge in a relational database. We evaluate queries using a modified SPARQL-DL API over the relational database. We show our system performs favourably with respect to query evaluation when compared to two main-memory based reasoners on several ontologies with large datasets including a healthcare ontology.