Visualisation in intelligent planning systems [Ghallab et al., 2004] is a subject that has not been given much attention by researchers. Among the existing planning systems, some well known planners do not propose a solution for visualisation at all, while others only consider a single approach when this solution sometimes is not appropriate for every situation. Thus, users cannot make the most of planning systems because they do not have appropriate support for interaction with them. This problem is more enhanced when considering mixed-initiative planning systems, where agents that are collaborating in the process have different backgrounds, are playing different roles in the process, have different capabilities and responsibilities, or are using different devices to interact and collaborate in the process. To address this problem, we propose a general framework for visualisation in planning systems that will give support for a more appropriate visualisation mechanism. This framework is divided into two main parts: a knowledge representation aspect and a reasoning mechanism for multi-modality visualisation. The knowledge representation uses the concept of ontology to organise and model complex domain problems. The reasoning mechanism gives support to reasoning about the visualisation problem based on the knowledge bases available for a realistic collaborative planning environment, including agent preferences, device features, planning information, visualisation modalities, etc. The main result of the reasoning mechanism is an appropriate visualisation modality for each specific situation, which provides a better interaction among agents (software and human) in a collaborative planning environment. The main contributions of this approach are: (1) it is a general and extensible framework for the problem of visualisation in planning systems, which enables the modelling of the domain from an information visualisation perspective; (2) it allows a tailored approach for visualisation of information in an AI collaborative planning environment; (3) its models can be used separately in other problems and domains; (4) it is based on real standards that enable easy communication and interoperability with other systems and services; and (5) it has a broad potential for its application on the Semantic Web.

No description available.

In recent years there has been a proliferation of scientific resources available through the Internet including, for example, datasets and computational modelling services.  Scientists are becoming increasingly dependent upon these resources, which are changing the way they conduct their research activities with increasing emphasis on conducting ‘in silico’ experiments as a way to test hypotheses.  Scientific workflow technologies provide researchers with a flexible problem-solving environment by facilitating the creation and execution of experiments from a pool of available services.  This thesis investigates the use of workflow tools enhanced with semantics to facilitate the design, execution, analysis and interpretation of workflow experiments and exploratory studies.  It is argued that in order to better characterise such experiments we need to go beyond low-level service composition and execution details by capturing higher-level descriptions of the scientific process.  Current workflow technologies do not incorporate any representation of such experimental constraints and goals, which is referred to in this thesis as scientist’s intent.  This thesis proposes an abstract model of scientific intent based on the concept of an Agent in the Open Provenance Model (OPM) specification.  To realise this model a framework based upon a number of Semantic Web technologies has been developed, including the OWL ontology language and the Semantic Web Rule Language (SWRL).  Through the use of social simulation case studies the thesis illustrates the benefits of using this framework in terms of workflow monitoring, workflow provenance and annotation of experimental results.

Ontologies are widely used as data representations for knowledge bases and marking up data on the emerging Semantic Web. Hence, techniques for managing ontol- ogy come to the centre of any practical and general solution of knowledge-based systems. Challenges arise when we look a step further in order to achieve flexibility and scalability of the ontology management. Previous works in ontology management, primarily for ontology mapping, ontology integration and ontology evolution, have exploited only one form or another of ontology management in restrictive settings. However, a distributed and heterogeneous environment makes it necessary for re- searchers in this field to consider ontology interoperability in order to achieve the vision of the Semantic Web. Several challenges arise when we set our goal to achieve ontology interoperability on the Web. The first one is to decide which soft- ware engineering paradigm to employ. The issue of such a paradigm is the core of ontology management when dynamic property is involved. It should make it easy to model complex systems and significantly improve current practice in software engineering. Moreover, it allows the extension of the range of applications that can feasibly be tackled. The second challenge is to exploit frameworks based on the pro- posed paradigm. Such a framework should make possible flexibility, interactivity, reusability and reliability for systems which are built on it. The third challenge is to investigate suitable mechanisms to cope with ontology mapping, integration and evolution based on the framework. It is known that predefined rules or hypotheses may not apply given that the environment hosting an ontology is changing over time. Fortunately, agents are being advocated as a next generation model for en- gineering complex and distributed systems. Also some researchers in this field have given a qualitative analysis to provide a justification for precisely why the agent-based approach is well suited to engineer complex software systems. From a multi-agent perspective, agent technology fits well in developing applications in uncontrolled and distributed environments which require substantial support for change. Agents in multi-agent systems (MAS) are autonomous and can engage in interactions which are essential for any ongoing agents� actions. A MAS approach is thus regarded as an intuitive and suitable way of modelling dynamic systems. Following the above discussion, an agent-based framework for managing ontology in a dynamic environment is developed. The framework has several key characteris- tics such as flexibility and extensibility that differentiate this research from others. Three important issues of the ontology management are also investigated. It is be- lieved that inter-ontology processes like ontology mapping with logical semantics are foundations of ontology-based applications. Hence, firstly, ontology mapping is discussed. Several types of semantic relations are proposed. Following these, the mapping mechanisms are developed. Secondly, based on the previous mapping results, ontology integration is developed to provide abstract views for participating organisations in the presence of a variety of ontologies. Thirdly, as an ontology is subject to evolution in its life cycle, there must be some kind of mechanisms to reflect their changes in corresponding interrelated ontologies. Ontology refinement is investigated to take ontology evolution into consideration. Process algebra is employed to catch and model information exchanges between ontologies. Agent negotiation strategy is applied to guide corresponding ontologies to react properly. A prototype is built to demonstrate the above design and functionalities. It is applied to ontologies dealing with the subject of beer (type). This prototype con- sists of four major types of agents, ranging from user agent, interface agent, ontology agent, and functionary agent. Evaluations such as query, consistency checking are conducted on the prototype. This shows that the framework is not only flexible but also completely workable. All agents derived from the framework exhibit their behaviours appropriately as expected.

published_or_final_version / Computer Science / Doctoral / Doctor of Philosophy

An early vision in Computer Science was to create intelligent systems ca- pable of reasoning on large amounts of data. Independent results in the areas of Description Logic and Relational Databases have advanced us towards this vision. Description Logic research has advanced the understanding of the tradeoff between the computational complexity of reasoning and the expressiveness of logic languages, and now underpins the Semantic Web. The Semantic Web comprises a graph data model (RDF), an ontology language for knowledge representation and reasoning (OWL) and a graph query language (SPARQL). Database research has advanced the theory and practice of management of data, embodying features such as views and recursion which are capable of representing reasoning. Despite the independent advances, the interface between Relational Databases and Semantic Web is poorly understood. This dissertation revisits this vision with respect to current technology and addresses the following question: How and to what extent can Relational Databases be integrated with the Semantic Web? The thesis is that much of the existing Relational Database infrastructure can be reused to support the Semantic Web. Two problems are studied. Can a Relational Database be automatically virtualized as a Semantic Web data source? This paradigm comprises a single Relational Database. The first contribution is an automatic direct mapping from a Relational Database schema and data to RDF and OWL. The second contribution is a method capable of evalu- ating SPARQL queries against the Relational Database, per the direct mapping, by exploiting two existing relational query optimizations. These contributions are embodied in a system called Ultrawrap. Empirical analysis consistently yield that SPARQL query execution performance on Ultrawrap is comparable to that of SQL queries written directly for the relational representation of the data. Such results have not been previously achieved. Can a Relational Database be mapped to existing Semantic Web ontologies and act as a reasoner? This paradigm comprises an OWL ontology including inheritance and transitivity, a Relational Database and mappings between the two. A third contribution is a method for Relational Databases to support inheritance and transitivity by compiling the ontology as mappings, implementing the mappings as SQL views, using SQL recursion and optimizing by materializing a subset of views. This contribution is implemented in an extension of Ultrawrap. Empirical analysis reveals that Relational Databases are able to effectively act as reasoners. / text

Univ., Diss.--Karlsruhe, 2005. / Literaturverz. S. [255] - 266.

Thesis (M.F.A.)--Kent State University, 2009. / Title from PDF t.p. (viewed April 22, 2010). Advisor: Sanda Katila. Keywords: microformats; semantic web; labels. Includes bibliographical references (p. 52-53).

This thesis provides a detailed description of the research undertaken into the creation of a framework that uses Semantic Web languages to implement a recently developed commonsense reasoning formalism called Discrete Event Calculus (DEC). It aims to show to what extent DEC reasoning can be applied to Semantic Web data, using the Semantic Web standards and supporting development environments available for the purpose in 2008, when the research programme commenced. The research aims to provide an accurate and reusable DEC ontology using the languages defined in Semantic Web Standards. To this end, an ontology describing the DEC entities and axioms is defined in OWL and SWRL; this represents the core elements of the DEC formalism, namely its set of logical types and predicates and the relations between them. The ontology is used together with a proof-of-concept DEC resolver software that applies the ontology to an existing rules engine, so that new inferences can be created from a DEC domain. The design and implementation of the combined ontology and software framework are described in detail. The methodological issues involved in reconciling a software model with an ontology model are also discussed and the capabilities of the framework are validated by a series of tests modelled on established AI benchmark scenarios that can be resolved correctly using DEC. The results confirm that the framework will create the appropriate inferences with reference to the benchmark problems, though they also highlight some of current limitations in the framework, notably to do with how it represents changing fluent values. A detailed sample domain ontology is provided, which is based on the domain of turn-based multiplayer online games; this illustrates how the DEC ontology defined in this research could be extended for use with other domains. A further extension of the DEC ontology is proposed, which enables the resolver to represent real-world time values independently of the timepoints defined as part of the formalism. Finally, the strengths and extant boundaries of the chosen approach are discussed and suggestions are provided for improvements that could form the basis of future work.

The web of data has continued to expand thanks to the principles of Linked Data outlined by Tim Berners-Lee, increasing its impact on the semantic web both in its depth and range of data sources. Meanwhile traditional web applications and technologies, with a strong focus on user interaction, such as blogs, wikis, folksonomies-based systems, and content management systems have become an integral part of the World Wide Web. However the semantic web has not yet managed to fully harness these technologies, resulting in a lack of linked data coming from user-generated content. The high level aim of this thesis is to answer the question of whether semantic web applications can be configured to use existing technologies that encourage usergenerated content on the Web. This thesis proposes an approach to reusing user-generated content from folksonomybased systems in semantic web applications, allowing these applications to be configured to make use of the structure and associated reasoning power of the semantic web, but while being able to reuse the vast amount of data already existing in these folksonomy-based systems. It proposes two new methods of semantic web application development: (i) a reusable infrastructure for building semantic mashup applications that can be configured to make use of the proposed approach; and (ii) a approach to configuring traditional web content management systems (CMS) to maintain repositories of Linked Data. The proposed approach allows semantic web applications to make use of tagged resources, while also addressing some limitations of the folksonomy approach by using ontology reasoning to exploit the structured information held in domain ontologies. The reusable infrastructure provides a set of components to allow semantic web applications to be configured to reuse content from folksonomy-based systems, while also allowing the users of these systems to contribute to the semantic web indirectly via the proposed approach. The proposed Linked Data CMS approach provides a configurable tools for semantic web application developers to develop an entire website based on linked data, while allowing ordinary web users to contribute directly to the semantic web using familiar CMS tools. The approaches proposed in this thesis make use of lightweight ontology reasoning, which is both efficient and scalable, to provide a basis for the development of practical semantic web applications. The research presented in this thesis shows how the semantic web can reuse both folksonomies and content management systems from Web 2.0 to help narrow the gap between these two key areas of the web.