Georgios Meditskos

A Rule-Based Object-Oriented OWL Reasoner

In this paper, we describe O-DEVICE, a memory-based knowledge-based 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.

DLEJena: A practical forward-chaining OWL 2 RL reasoner combining Jena and Pellet

This paper describes DLEJena, a practical reasoner for the OWL 2 RL profile that combines the forward-chaining rule engine of Jena and the Pellet DL reasoner. This combination is based on rule templates, instantiating at run-time a set of ABox OWL 2 RL/RDF Jena rules dedicated to a particular TBox that is handled by Pellet. The goal of DLEJena is to handle efficiently, through instantiated rules, the OWL 2 RL ontologies under direct semantics, where classes and properties cannot be at the same time individuals. The TBox semantics are treated by Pellet, reusing in that way efficient and sophisticated TBox DL reasoning algorithms. The experimental evaluation shows that DLEJena achieves more scalable ABox reasoning than the direct implementation of the OWL 2 RL/RDF rule set in the Jenas production rule engine, which is the main target of the system. DLEJena can be also used as a generic framework for applying an arbitrary number of entailments beyond the OWL 2 RL profile.

Semantic web technologies in pervasive computing

Pervasive and sensor-driven systems are by nature open and extensible, both in terms of input and tasks they are required to perform. Data streams coming from sensors are inherently noisy, imprecise and inaccurate, with differing sampling rates and complex correlations with each other. These characteristics pose a significant challenge for traditional approaches to storing, representing, exchanging, manipulating and programming with sensor data. Semantic Web technologies provide a uniform framework for capturing these properties. Offering powerful representation facilities and reasoning techniques, these technologies are rapidly gaining attention towards facing a range of issues such as data and knowledge modelling, querying, reasoning, service discovery, privacy and provenance. This article reviews the application of the Semantic Web to pervasive and sensor-driven systems with a focus on information modelling and reasoning along with streaming data and uncertainty handling. The strengths and weaknesses of current and projected approaches are analysed and a roadmap is derived for using the Semantic Web as a platform, on which open, standard-based, pervasive, adaptive and sensor-driven systems can be deployed.

A combinatory framework of Web 2.0 mashup tools, OWL-S and UDDI

Research highlights? We propose a social-oriented extension of OWL-S for mashup descriptions. ? Mashup descriptions are mapped on UDDI. ? We define a semantic mashup matchmaking algorithm. ? Framework implementation in Yahoo Pipes. The increasing number of Web 2.0 applications, such as wikis or social networking sites, indicates a movement to large-scale collaborative and social Web activities. Users can share information, add value to Web applications by using them or aggregate data from different sources creating Web applications (mashups) using specialized tools (mashup tools). However, Web 2.0 is not a new technology, but it rather embraces a new philosophy, treating the Internet as a platform. Several issues related to the Semantic Web vision, such as interoperability or machine understandable data semantics, are not tackled by Web 2.0. In this paper, we present our effort to combine semantic Web services (SWS) discovery frameworks, UDDI repositories and existing mashup tools in order to enhance the procedure of developing mashups with semantic mashup discovery capabilities. Towards this end, we introduce a social-oriented extension of OWL-S advertisements, their mapping algorithm on UDDI repositories and a semantic mashup discovery algorithm. Finally, we elaborate on the way our framework has been realized using the Yahoo Pipes mashup tool.

Combining a DL Reasoner and a Rule Engine for Improving Entailment-Based OWL Reasoning

We introduce the notion of the mixed DL and entailment-based (DLE) OWL reasoning, defining a framework inspired from the hybrid and homogeneous paradigms for integration of rules and ontologies. The idea is to combine the TBox inferencing capabilities of the DL algorithms and the scalability of the rule paradigm over large ABoxes. Towards this end, we define a framework that uses a DL reasoner to reason over the TBox of the ontology (hybrid-like) and a rule engine to apply a domain-specific version of ABox-related entailments (homogeneous-like) that are generated by TBox queries to the DL reasoner. The DLE framework enhances the entailment-based OWL reasoning paradigm in two directions. Firstly, it disengages the manipulation of the TBox semantics from any incomplete entailment-based approach, using the efficient DL algorithms. Secondly, it achieves faster application of the ABox-related entailments and efficient memory usage, comparing it to the conventional entailment-based approaches, due to the low complexity and the domain-specific nature of the entailments.

SP-ACT: A hybrid framework for complex activity recognition combining OWL and SPARQL rules

In this paper we describe SP-ACT, a hybrid framework for the derivation of high-level activity interpretations in context-aware environments, by defining a combination of OWL ontologies and SPARQL CONSTRUCT graph patterns. More specifically, the native semantics of OWL is used to formally represent and integrate activity-related information originated from different data sources, whereas SPARQL (SPIN) rules further aggregate activities so as to derive highlevel activity abstractions. The goal of the hybrid framework is to address the limitations of the ontology-based context modelling paradigm in domains that require the recognition of complex context elements, namely, the lack of support for (i) temporal reasoning and (ii) new named individual assertions.

On the Combination of Textual and Semantic Descriptions for Automated Semantic Web Service Classification

Semantic Web services have emerged as the solution to the need for automating several aspects related to service-oriented architectures, such as service discovery and composition, and they are realized by combining Semantic Web technologies and Web service standards. In the present paper, we tackle the problem of automated classification of Web services according to their application domain taking into account both the textual description and the semantic annotations of OWL-S advertisements. We present results that we obtained by applying machine learning algorithms on textual and semantic descriptions separately and we propose methods for increasing the overall classification accuracy through an extended feature vector and an ensemble of classifiers.

MetaQ: A knowledge-driven framework for context-aware activity recognition combining SPARQL and OWL 2 activity patterns

Abstract In this paper we describe MetaQ, an ontology-based hybrid framework for activity recognition in Ambient Assisted Living (AAL) environments that combines SPARQL queries and OWL 2 activity patterns. SPARQL is used as a standardised declarative language for aggregating, interpreting and enriching low-level contextual RDF knowledge bases with higher level derivations. The proposed SPARQL-based reasoning framework supports key inferencing tasks that are important in activity interpretation domains, but not supported by the standard semantics of OWL 2, such as temporal reasoning and dynamic assertion of structured individuals. In order to promote the extensibility and reuse of the underlying interpretation semantics, the reasoning framework is further enhanced with a conceptual layer that allows the formal representation of activity meta-knowledge by means of DOLCE+DnS Ultralite (DUL) ontology patterns. We illustrate the capabilities of the proposed framework through its deployment in a hospital for monitoring activities of Alzheimer’s disease patients.

SPARSE: A symptom-based antipattern retrieval knowledge-based system using Semantic Web technologies

Abstract Antipatterns provide information on commonly occurring solutions to problems that generate negative consequences. The number of software project management antipatterns that appears in the literature and the Web increases to the extent that makes using antipatterns problematic. Furthermore, antipatterns are usually inter-related and rarely appear in isolation. As a result, detecting which antipatterns exist in a software project is a challenging task which requires expert knowledge. This paper proposes SPARSE, an OWL ontology based knowledge-based system that aims to assist software project managers in the antipattern detection process. The antipattern ontology documents antipatterns and how they are related with other antipatterns through their causes, symptoms and consequences. The semantic relationships that derive from the antipattern definitions are determined using the Pellet DL reasoner and they are transformed into the COOL language of the CLIPS production rule engine. The purpose of this transformation is to create a compact representation of the antipattern knowledge, enabling a set of object-oriented CLIPS production rules to run and retrieve antipatterns relevant to some initial symptoms. SPARSE is exemplified through 31 OWL ontology antipattern instances of software development antipatterns that appear on the Web.

Object-Oriented Similarity Measures for Semantic Web Service Matchmaking

The semantic annotation of Web services capabilities with ontological information aims at providing the necessary infrastructure for facilitating efficient and accurate service discovery. The main idea is to apply reasoning techniques over semantically enhanced Web service requests and advertisements in order to determine Web services that meet certain requirements. In this paper we present our work for introducing similarity measures inspired from the domain of Object-Oriented paradigm for ontology concept matching. Our work focuses on the utilization of such measures over an Object-Oriented schema that is created through mapping rules of OWL constructs and semantics into the Object-Oriented model. The goal of the approach is to combine the Object-Oriented representation of the information and the reasoning over OWL semantics in order to enhance the retrieval of semantically relevant, to some criteria, Web services.