Proceedings ICPW'07: 2nd International Conference on the Pragmatic Web, 22-23 Oct. 2007, Tilburg: NL

Proceedings ICPW'07: 2nd International Conference on the Pragmatic Web, 22-23 Oct. 2007, Tilburg: N

Geospatial Semantics: Why, of What, and How?

Abstract. Why are notions like semantics and ontologies suddenly getting so much attention, within and outside geospatial information communities? The main reason lies in the componentization of Geographic Information Systems (GIS) into services, which are supposed to interoperate within and across these communities. Consequently, I look at geospatial semantics in the context of semantic interoperability. The paper clarifies the relevant notion of semantics and shows what parts of geospatial information need to receive semantic speci-fications in order to achieve interoperability. No attempt at a survey of ap-proaches to provide semantics is made, but a framework for solving interopera-bility problems is proposed in the form of semantic reference systems. Particular emphasis is put on the need and possible ways to ground geospatial semantics in physical processes and measurements. 1. Introduction: Wh

Ontological interpretation of network monitoring data

Interpreting measurement and monitoring data from networks in general and the Internet in particular is a challenge. The motivation for this work has been to in- vestigate new ways to bridge the gap between the kind of data which are available and the more developed information which is needed by network stakeholders to support decision making and network management. Specific problems of syntax, semantics, conflicting data and modeling domain-specific knowledge have been identified. The methods developed and tested have used the Resource Descrip- tion Framework (rdf) and the ontology languages of the Semantic Web to bring together data from disparate sources into unified knowledgebases in two discrete case studies, both using real network data. Those knowledgebases have then been demonstrated to be usable and valuable sources of information about the networks concerned. Some success has been achieved in overcoming each of the identified problems using these techniques, proving the thesis that taking an ontological ap- proach to the processing of network monitoring data can be a very useful technique for overcoming problems of interpretation and for making information available to those who need it

Exploring multi-granular documentation strategies for the representation, discovery and use of geographic information

This thesis explores how digital representations of geography and Geographic Information (GI) may be described, and how these descriptions facilitate the use of the resources they depict. More specifically, it critically examines existing geospatial documentation practices and aims to identify opportunities for refinement therein, whether when used to signpost those data assets documented, for managing and maintaining information assets, or to assist in resource interpretation and discrimination. Documentation of GI can therefore facilitate its utilisation; it can be reasonably expected that by refining documentation practices, GI hold the potential for being better exploited. The underpinning theme connecting the individual papers of the thesis is one of multi-granular documentation. GI may be recorded at varying degrees of granularity, and yet traditional documentation efforts have predominantly focussed on a solitary level (that of the geospatial data layer). Developing documentation practices to account for other granularities permits the description of GI at different levels of detail and can further assist in realising its potential through better discovery, interpretation and use. One of the aims of the current work is to establish the merit of such multi-granular practices. Over the course of four research papers and a short research article, proprietary as well as open source software approaches are accordingly presented and provide proof-of-concept and conceptual solutions that aim to enhance GI utilisation through improved documentation practices. Presented in the context of an existing body of research, the proposed approaches focus on the technological infrastructure supporting data discovery, the automation of documentation processes and the implications of describing geospatial information resources of varying granularity. Each paper successively contributes to the notion that geospatial resources are potentially better exploited when documentation practices account for the multi-granular aspects of GI, and the varying ways in which such documentation may be used. In establishing the merit of multi-granular documentation, it is nevertheless recognised in the current work that instituting a comprehensive documentation strategy at several granularities may be unrealistic for some geospatial applications. Pragmatically, the level of effort required would be excessive, making universal adoption impractical. Considering however the ever-expanding volumes of geospatial data gathered and the demand for ways of managing and maintaining the usefulness of potentially unwieldy repositories, improved documentation practices are required. A system of hierarchical documentation, of self-documenting information, would provide for information discovery and retrieval from such expanding resource pools at multiple granularities, improve the accessibility of GI and ultimately, its utilisation

Ontology-based knowledge representation and semantic search information retrieval: case study of the underutilized crops domain

The aim of using semantic technologies in domain knowledge modeling is to introduce the semantic meaning of concepts in knowledge bases, such that they are both human-readable as well as machine-understandable. Due to their powerful knowledge representation formalism and associated inference mechanisms, ontology-based approaches have been increasingly adopted to formally represent domain knowledge. The primary objective of this thesis work has been to use semantic technologies in advancing knowledge-sharing of Underutilized crops as a domain and investigate the integration of underlying ontologies developed in OWL (Web Ontology Language) with augmented SWRL (Semantic Web Rule Language) rules for added expressiveness. The work further investigated generating ontologies from existing data sources and proposed the reverse-engineering approach of generating domain specific conceptualization through competency questions posed from possible ontology users and domain experts. For utilization, a semantic search engine (the Onto-CropBase) has been developed to serve as a Web-based access point for the Underutilized crops ontology model. Relevant linked-data in Resource Description Framework Schema (RDFS) were added for comprehensiveness in generating federated queries. While the OWL/SWRL combination offers a highly expressive ontology language for modeling knowledge domains, the combination is found to be lacking supplementary descriptive constructs to model complex real-life scenarios, a necessary requirement for a successful Semantic Web application. To this end, the common logic programming formalisms for extending Description Logic (DL)-based ontologies were explored and the state of the art in SWRL expressiveness extensions determined with a view to extending the SWRL formalism. Subsequently, a novel fuzzy temporal extension to the Semantic Web Rule Language (FT-SWRL), which combines SWRL with fuzzy logic theories based on the valid-time temporal model, has been proposed to allow modeling imprecise temporal expressions in domain ontologies

Thinking outside the TBox multiparty service matchmaking as information retrieval

Service oriented computing is crucial to a large and growing number of computational undertakings. Central to its approach are the open and network-accessible services provided by many different organisations, and which in turn enable the easy creation of composite workflows. This leads to an environment containing many thousands of services, in which a programmer or automated composition system must discover and select services appropriate for the task at hand. This discovery and selection process is known as matchmaking. Prior work in the field has conceived the problem as one of sufficiently describing individual services using formal, symbolic knowledge representation languages. We review the prior work, and present arguments for why it is optimistic to assume that this approach will be adequate by itself. With these issues in mind, we examine how, by reformulating the task and giving the matchmaker a record of prior service performance, we can alleviate some of the problems. Using two formalisms—the incidence calculus and the lightweight coordination calculus—along with algorithms inspired by information retrieval techniques, we evolve a series of simple matchmaking agents that learn from experience how to select those services which performed well in the past, while making minimal demands on the service users. We extend this mechanism to the overlooked case of matchmaking in workflows using multiple services, selecting groups of services known to inter-operate well. We examine the performance of such matchmakers in possible future services environments, and discuss issues in applying such techniques in large-scale deployments