Knowledge Representation with Ontologies: The Present and Future

Recently, we have seen an explosion of interest in ontologies as artifacts to represent human knowledge and as critical components in knowledge management, the semantic Web, business-to-business applications, and several other application areas. Various research communities commonly assume that ontologies are the appropriate modeling structure for representing knowledge. However, little discussion has occurred regarding the actual range of knowledge an ontology can successfully represent

Pengines: Web Logic Programming Made Easy

When developing a (web) interface for a deductive database, functionality required by the client is provided by means of HTTP handlers that wrap the logical data access predicates. These handlers are responsible for converting between client and server data representations and typically include options for paginating results. Designing the web accessible API is difficult because it is hard to predict the exact requirements of clients. Pengines changes this picture. The client provides a Prolog program that selects the required data by accessing the logical API of the server. The pengine infrastructure provides general mechanisms for converting Prolog data and handling Prolog non-determinism. The Pengines library is small (2000 lines Prolog, 150 lines JavaScript). It greatly simplifies defining an AJAX based client for a Prolog program and provides non-deterministic RPC between Prolog processes as well as interaction with Prolog engines similar to Paul Tarau's engines. Pengines are available as a standard package for SWI-Prolog 7.Comment: To appear in Theory and Practice of Logic Programmin

Automated software quality visualisation using fuzzy logic techniques

In the past decade there has been a concerted effort by the software industry to improve the quality of its products. This has led to the inception of various techniques with which to control and measure the process involved in software development. Methods like the Capability Maturity Model have introduced processes and strategies that require measurement in the form of software metrics. With the ever increasing number of software metrics being introduced by capability based processes, software development organisations are finding it more difficult to understand and interpret metric scores. This is particularly problematic for senior management and project managers where analysis of the actual data is not feasible. This paper proposes a method with which to visually represent metric scores so that managers can easily see how their organisation is performing relative to quality goals set for each type of metric. Acting primarily as a proof of concept and prototype, we suggest ways in which real customer needs can be translated into a feasible technical solution. The solution itself visualises metric scores in the form of a tree structure and utilises Fuzzy Logic techniques, XGMML, Web Services and the .NET Framework. Future work is proposed to extend the system from the prototype stage and to overcome a problem with the masking of poor scores

A commentary on standardization in the Semantic Web, Common Logic and MultiAgent Systems

Given the ubiquity of the Web, the Semantic Web (SW) offers MultiAgent Systems (MAS) a most wide-ranging platform by which they could intercommunicate. It can be argued however that MAS require levels of logic that the current Semantic Web has yet to provide. As ISO Common Logic (CL) ISO/IEC IS 24707:2007 provides a firstorder logic capability for MAS in an interoperable way, it seems natural to investigate how CL may itself integrate with the SW thus providing a more expressive means by which MAS can interoperate effectively across the SW. A commentary is accordingly presented on how this may be achieved. Whilst it notes that certain limitations remain to be addressed, the commentary proposes that standardising the SW with CL provides the vehicle by which MAS can achieve their potential.</p