OWLET: An Object-Oriented Environment for OWL Ontology Management

2000

Rule-based OWL Reasoning 2 ABSTRACT This chapter is focused on the basic principles behind the utilization of rules in order to perform reasoning about the Web Ontology Language (OWL), a Description Logic-based language that is the W3C recommendation for creating and sharing ontologies in the Semantic Web. More precisely, we elaborate on the entailment-based OWL reasoning (EBOR) paradigm, which is based on the utilization of RDF/RDFS and OWL entailment rules that run on a rule engine, applying the formal semantics of the ontology language. To this end, seven EBOR systems are described and compared, analyzing the different approaches. Despite the closed rule environment, which comes in contrast with the open nature of the Semantic Web, and the fact that OWL semantics are partially mapped into rules, the rule-based OWL reasoning paradigm can give great potentials in the Semantic Web, enabling the utilization of rule engines on top of ontology information.

Due to the enormous growth of the volume of data and different structures, information systems are becoming increasingly complex. The Semantic Web aims to find, share and refine the information of current web more easily. The Semantic Web is a solution for representing the data in a machine understandable manner. Ontology is a powerful technology for representing the data in semantic web. Web Ontology Language (OWL) has been accepted as the knowledge representation standard by W3C. This paper proposes a unified and integrated framework, i.e., an Extended Hierarchical Censored Production Rules (EHCPRs) Framework for representing ontologies in Semantic Web, maintaining the knowledge treasures and doing reasoning over it. In addition, assuming that the framework is in place and is adopted, an algorithm is provided to convert the existing OWL ontologies into the EHCPRs Ontologies.The Protégé tool is used to create a trivial Vehicle Ontology and using the developed algorithm, it is converted into EHCPRs Ontology with no loss of information, i.e., preserving all the classes, their attributes and instances.

Journal of Biomedical Semantics, 2015

Background: The increasing use of ontologies highlights the need for a library for working with ontologies that is efficient, accessible from various programming languages, and compatible with common computational platforms. Results: We developed owlcpp, a library for storing and searching RDF triples, parsing RDF/XML documents, converting triples into OWL axioms, and reasoning. The library is written in ISO-compliant C++ to facilitate efficiency, portability, and accessibility from other programming languages. Internally, owlcpp uses the Raptor RDF Syntax library for parsing RDF/XML and the FaCT++ library for reasoning. The current version of owlcpp is supported under Linux, OSX, and Windows platforms and provides an API for Python. Conclusions: The results of our evaluation show that, compared to other commonly used libraries, owlcpp is significantly more efficient in terms of memory usage and searching RDF triple stores. owlcpp performs strict parsing and detects errors ignored by other libraries, thus reducing the possibility of incorrect semantic interpretation of ontologies. owlcpp is available at http://owl-cpp.sf.net/ under the Boost Software License, Version 1.0.

2009

Abstract. This paper presents the OWL API a high level Application Programming Interface (API) for working with OWL 2 ontologies. The API is closely aligned with the OWL 2 structural specification. It supports parsing and rendering in the syntaxes defined in the W3C specification, namely, the Functional Syntax, RDF/XML, OWL/XML and the Manchester OWL Syntax. Finally, the reference implementation of the API, which is written in Java, includes validators for the various OWL 2 profiles-OWL 2 QL, OWL 2 EL and OWL 2 RL.

2003

This paper discusses issues that surround the provision of application support using OWL ontologies. It presents the OWL API, a high-level programmatic interface for accessing and manipulating OWL ontologies. We discuss the underlying design issues and illustrate possible solutions to technical issues occurring in systems that intend to support the OWL standard.

1977

: The motivation and overall organization of the OWL language for knowledge representation is described. OWL consists of a memory of concepts in terms of which all English phrases and all knowledge of an application domain are represented, a theory of English grammar which tells how to map English phrases into concepts, a parser to perform that mapping for individual sentences, and an interpreter to carry out procedures which are written in the same representational formalism. The system has been applied to the study of interactive dialogs, explanations of its own reasoning, and question answering.

Knowledge and Data …, 2008

In this paper we describe O-DEVICE, a memory-based knowledge base system for reasoning and querying OWL ontologies by implementing RDF/OWL entailments in the form of production rules in order to apply the formal semantics of the language. Our approach is based on a transformation procedure of OWL ontologies into an Object-Oriented schema and the application of inference production rules over the generated objects in order to implement the various semantics of OWL. In order to enhance the performance of the system, we introduce a dynamic approach of generating production rules for ABOX reasoning and an incremental approach of loading ontologies. O-DEVICE is built over the CLIPS production rule system, using the object-oriented language COOL to model and handle ontology concepts and RDF resources. One of the contributions of our work is that we enable a well-known and efficient production rule system to handle OWL ontologies. We argue that although native OWL rule reasoners may process ontology information faster, they lack some of the key features that rule systems offer, such as the efficient manipulation of the information through complex rule programs. We present a comparison of our system with other OWL reasoners, showing that O-DEVICE can constitute a practical rule environment for ontology manipulation.

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.

Lecture Notes in Computer Science, 2005

OWLIM is a high-performance Storage and Inference Layer (SAIL) for Sesame, which performs OWL DLP reasoning, based on forward-chaining of entilement rules. The reasoning and query evaluation are performed inmemory, while in the same time OWLIM provides a reliable persistence, based on N-Triples files. This paper presents OWLIM, together with an evaluation of its scalability over synthetic, but realistic, dataset encoded with respect to PROTON ontology. The experiment demonstrates that OWLIM can scale to millions of statements even on commodity desktop hardware. On an almostentry-level server, OWLIM can manage a knowledge base of 10 million explicit statements, which are extended to about 19 millions after forward chaining. The upload and storage speed is about 3,000 statement/sec. at the maximal size of the repository, but it starts at more than 18,000 (for a small repository) and slows down smoothly. As it can be expected for such an inference strategy, delete operations are expensive, taking as much as few minutes. In the same time, a variety of queries can be evaluated within milliseconds. The experiment shows that such reasoners can be efficient for very big knowledge bases, in scenarios when delete operations should not be handled in real-time.