OWL SAIQL - An OWL DL Query Language for Ontology Extraction

Handbook on Ontologies, 2004

With the deep research of semantic Web, people are more and more concerned with the problem of representing and retrieving information content on the Web. The ontology language layer building on top of RDF schema is used to formally describe the meaning of ...

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.

2007

In this paper, an object-oriented model and a software environment for the management of OWL ontologies is presented. The object-oriented model allows a simple and complete representation of ontologies defined by using OWL DL profile. The software environment, called OWLET, implements this object-oriented model and provides a complete set of reasoning functions together with a graphical editor for the creation and modification of ontologies. OWLET can be very useful for realizing heterogeneous and distributed semantic systems where nodes differ for their capabilities (i.e., CPU power, memory size, …); in fact, it offers a layered reasoning API that allows to deploy a system where high power nodes take advantages of all the OWLET reasoning capabilities, medium power nodes take advantages of a limited set of OWLET reasoning capabilities (e.g., reasoning about individuals) and low power nodes delegate reasoning tasks to the other nodes of the system.

IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans, 2011

Most Semantic Web technology-based applications need users to have a deep background on the formal underpinnings of ontology languages and some basic skills in these technologies. Generally, only experts in the field meet these requirements. In this paper, we present OWLPath, a natural language-query editor guided by multilanguage OWL-formatted ontologies. This application allows nonexpert users to easily create SPARQL queries that can be issued over most existing ontology storage systems. Our approach is a fully fledged solution backed with a proof-of-concept implementation and the empirical results of two challenging use cases: one in the domain of e-finance and the other in e-tourism.

2004

Abstract The OWL Web Ontology Language is designed for use by applications that need to process the content of information instead of just presenting information to humans. OWL facilitates greater machine interpretability of Web content than that supported by XML, RDF, and RDF Schema (RDF-S) by providing additional vocabulary along with a formal semantics. OWL has three increasingly-expressive sublanguages: OWL Lite, OWL DL, and OWL Full.

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.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2013

Tool development for and empirical experimentation in OWL ontology engineering require a wide variety of suitable ontologies as input for testing and evaluation purposes and detailed characterisations of real ontologies. Empirical activities often resort to (somewhat arbitrarily) hand curated corpora available on the web, such as the NCBO BioPortal and the TONES Repository, or manually selected sets of well-known ontologies. Findings of surveys and results of benchmarking activities may be biased, even heavily, towards these datasets. Sampling from a large corpus of ontologies, on the other hand, may lead to more representative results. Current large scale repositories and web crawls are mostly uncurated and suffer from duplication, small and (for many purposes) uninteresting ontology files, and contain large numbers of ontology versions, variants, and facets, and therefore do not lend themselves to random sampling. In this paper, we survey ontologies as they exist on the web and describe the creation of a corpus of OWL DL ontologies using strategies such as web crawling, various forms of de-duplications and manual cleaning, which allows random sampling of ontologies for a variety of empirical applications.

2003

The OWL Web Ontology Language is a new formal language for representing ontologies in the Semantic Web. OWL has features from several families of representation languages, including primarily Description Logics and frames. OWL also shares many characteristics with RDF, the W3C base of the Semantic Web. In this paper, we discuss how the philosophy and features of OWL can be traced back to these older formalisms, with modifications driven by several other constraints on OWL.

Proceedings of the 10th IEEE International Conference on Collaborative Computing: Networking, Applications and Worksharing, 2014

Extracting Ontop mapping rules and OWL ontology manually from a relational schema is a tedious task. We present an automatic approach for extracting Ontop mappings and OWL ontology from an existing database schema. The end users can access the underlying data source through SPARQL queries. A SPARQL query is written according to the extracted ontology and the end user does not need to know about the underlying data source and its schema. The proposed approach takes into consideration the different relationships between entities of the database schema. Instead of extracting a flat ontology that is an exact copy of the database schema, it extracts a rich ontology. The extracted ontology can also be used as an intermediate between a domain ontology and the underlying database schema. The experiment results indicate that the extracted mappings and ontology are accurate. i.e., end users can query all data (using SPARQL) from the underlying database source in the same way as if they have written SQL queries.