Sotirios Batsakis

Qualitative Spatial Reasoning Using Topological and Directional Information in OWL

We investigate on potential improvements to reasoning approaches designed for spatial information in OWL. First, we introduce CHOROS, a qualitative spatial reasoning engine for ontologies in OWL. Building upon Pellet Spatial, CHOROS supports consistency checking and query answering for spatial information using Region-Connection Calculus (RCC), but also using the Cone-Shaped Directional (CSD) logic formalism. It works with all RCC and CSD relations in combination with standard RDF/OWL semantic relations in an OWL ontology and can answer SPARQL queries with spatial and non-spatial relations. We also present SOWL, a spatial reasoner for both relation calculi implemented in SWRL and runs under Pellet. We discuss and evaluate possible optimizations of CHOROS and compare its performance with that of SOWL. The experimental results demonstrate that CHOROS runs significantly faster than its respective SWRL implementation in most cases.

CHRONOS: A Tool for Handling Temporal Ontologies in Protégé

Representing information evolving in time in ontologies is a difficult problem to deal with. Temporal relations are in fact ternary (i.e., properties of objects that change in time involve also a temporal value in addition to the object and the subject) and cannot be handled directly by OWL. The standard solution to this problem is to map all temporal relations to a set of binary ones with new (intermediate) classes introduced by the temporal model applied. Nevertheless, ontologies then become complicated and difficult to handle by standard editors such as Protege (e.g., property restrictions of temporal classes might refer to the new classes rather than to the classes on which they were meant to be defined). It also requires that the user be familiar with the peculiarities of the temporal representation. This is exactly the problem this work is dealing with. We introduce CHRONOS, a plug-in for Protege that enables handling of temporal ontologies in Protege the same way static ontologies are handled. It is implemented as a Tab plug-in for Protege and can be downloaded from the Web.

CHRONOS Ed: A Tool for Handling Temporal Ontologies in Protégé

Representing information evolving in time in ontologies is a difficult problem to deal with. Temporal relations are in fact ternary (i.e., properties of objects that change in time involve also a temporal value in addition to the object and the subject) and cannot be handled directly by OWL. The standard solution to this problem is to map all temporal relations to a set of binary ones with new (intermediate) classes introduced by the temporal model applied. Nevertheless, ontologies then become complicated and difficult to handle by standard editors such as Protege (e.g., property restrictions of temporal classes might refer to the new classes rather than to the classes on which they were meant to be defined). It also requires that the user be familiar with the peculiarities of the temporal representation. This is exactly the problem this work is dealing with. We introduce CHRONOS Ed, a plug-in for Protege that enables handling of temporal ontologies in Protege the same way static ontologies are handled. It is implemented as a Tab plug-in for Protege and can be downloaded from the Web.

CHRONOS: Improving the Performance of Qualitative Temporal Reasoning in OWL

We investigate on potential improvements to reasoning about qualitative temporal information in OWL. Building upon path consistency, the new reasoner design, referred to as CHRONOS, computes a minimal set of relation compositions at run-time (based on a tractable subset of Allen relations) while being sound and complete. The experimental results demonstrate that the two implementation variants of CHRONOS discussed in this work run up to two orders of magnitude faster than SOWL, a temporal reasoner implemented in SWRL.

SOWL QL: Querying Spatio-Temporal Ontologies in OWL

We introduce SOWL QL, a query language for spatio-temporal information in ontologies. Building-upon SOWL (Spatio-Temporal OWL), an ontology for handling spatio-temporal information in OWL, SOWL QL supports querying over qualitative spatio-temporal information (expressed using natural language expressions such as “before”, “after”, “north of”, “south of”) rather than merely quantitative information (exact dates, times, locations). SOWL QL extends SPARQL with a powerful set of temporal and spatial operators, including temporal Allen topological, spatial directional and topological operations or combinations of the above. SOWL QL maintains simplicity of expression, and also upward and downward compatibility with SPARQL. Query translation in SOWL QL yields SPARQL queries, implying that querying spatio-temporal ontologies using SPARQL is still feasible but suffers from several drawbacks, the most important of them being that, queries in SPARQL become particularly complicated and users must be familiar with the underlying spatio-temporal representation (the “N-ary relations” or the “4D-fluents” approach in this work). Finally, querying in SOWL QL is supported by the SOWL reasoner which is not part of the standard SPARQL translation. The run-time performance of SOWL QL has been assessed experimentally in a real data setting. A critical analysis of its performance is also presented.

Large scale distributed spatio-temporal reasoning using real-world knowledge graphs

Abstract Most of the existing work in the field of Qualitative Spatial Temporal Reasoning (QSTR) has focussed on comparatively small constraint networks that consist of hundreds or at most thousands of relations. Recently we have seen the emergence of much larger qualitative spatial knowledge graphs that feature hundreds of thousands and millions of relations. Traditional approaches to QSTR are unable to reason over networks of such size. In this article we describe ParQR, a parallel, distributed implementation of QSTR techniques that addresses the challenge of reasoning over large-scale qualitative spatial and temporal datasets. We have implemented ParQR using the Apache Spark framework, and evaluated our approach using both large scale synthetic datasets and real-world knowledge graphs. We show that our approach scales effectively, is able to handle constraint networks consisting of millions of relations, and outperforms current distributed implementations of QSTR.

A Hypercat-Enabled Semantic Internet of Things Data Hub

An increasing amount of information is generated from the rapidly increasing number of sensor networks and smart devices. A wide variety of sources generate and publish information in different formats, thus highlighting interoperability as one of the key prerequisites for the success of Internet of Things (IoT). The BT Hypercat Data Hub provides a focal point for the sharing and consumption of available datasets from a wide range of sources. In this work, we propose a semantic enrichment of the BT Hypercat Data Hub, using well-accepted Semantic Web standards and tools. We propose an ontology that captures the semantics of the imported data and present the BT SPARQL Endpoint by means of a mapping between SPARQL and SQL queries. Furthermore, federated SPARQL queries allow queries over multiple hub-based and external data sources. Finally, we provide two use cases in order to illustrate the advantages afforded by our semantic approach.

Large Scale Reasoning Using Allen’s Interval Algebra

This paper proposes and evaluates a distributed, parallel ap- proach for reasoning over large scale datasets using Allen’s Interval Alge- bra (IA). We have developed and implemented algorithms that reason over IA networks using the Spark distributed processing framework. Experiments have been conducted by deploying the algorithms on computer clusters using synthetic datasets with various characteristics. We show that reasoning over datasets consisting of millions of interval relations is feasible and that our implementation scales effectively. The size of the IA networks we are able to reason over is far greater than those found in previously published works.