Dickson Lukose

Ontology Alignment—A Survey with Focus on Visually Supported Semi-Automatic Techniques

Semantic technologies are of paramount importance to the future Internet. The reuse and integration of semantically described resources, such as data or services, necessitates the bringing of ontologies into mutual agreement. Ontology alignment deals with the discovery of correspondences between concepts and relations from different ontologies. Alignment provides the key ingredient to semantic interoperability. This paper gives an overview on the state of the art in the field of visually supported semi-automatic alignment techniques and presents recent trends and developments. Particular attention is given to user interfaces and visualization techniques supporting involvement of humans in the alignment process. We derive and summarize requirements for visual semi-automatic alignment systems, provide an overview of existing approaches, and discuss the possibilities for further improvements and future research.

An Affective Decision Making Agent Architecture Using Emotion Appraisals

Interacting intelligently within a temporally dynamic environment calls for adaptive performances from artificial beings. One mechanism currently being explored to produce an intuitive-like behaviour in AI applications is that of emotion. Several popular psychological theories of emotion have been the basis for a number of agent models that are capable of synthesizing emotions. However, while these models have demonstrated limited success, they lack the dynamic scalability to form complex emotional dimensions that are necessary for producing adaptive behaviour. This paper examines how an affective appraisal made with discrete evaluations of an event can overcomes contemporary limitations. An artificial agent that has had this theory integrated into its architecture is demonstrated. A formal model of the agents Ontology and its Multidimensional Affective Decision Making process is outlined, with examples demonstrating an affective decision making process.

Artificial Intelligence in Industry

The Symposium on the Application of Artificial Intelligence in Industry was held in conjunction with the Sixth Pacific Rim International Conference on Artificial Intelligence (PRICAI-2000), Melbourne Australia, August 2000. It was the second of the Symposium series aiming to highlight actual applications of Artificial Intelligence in industry and to share and compare experiences in doing so. The Symposium brought together researchers and developers of applied Artificial Intelligence systems. The symposium is the leading forum in the Pacific Rim for the presentation of innovative applications of AI in industry.

Ontology based graphical query language supporting recursion

Text based queries often lead tend to be complex, and may result in non user friendly query structures. However, querying information systems using visual means, even for complex queries has proven to be more efficient and effective as compared to text based queries. This is owing to the fact that visual systems make way for better human-computer communication. This paper introduces an improved query system using a Visual Query Language. The system allows the users to construct query graphs by interacting with the ontology in a user friendly manner. The main purpose of the system is to enable efficient querying on ontologies even by novice users who do not have an in-depth knowledge of internal query structures. The system also supports graphical recursive queries and methods to interpret recursive programs from these visual query graphs. Additionally, we have performed some preliminary usability experiments to test the efficiency and effectiveness of the system.

Building detection with loosely-coupled hybrid feature descriptors

The paper presents a hybrid approach that ultilizes multiple low-level feature descriptors for performing building detection in 2D images. The proposed method is a symbiosis of two feature descriptors, namely Color and Edge Directivity Descriptor (CEDD) and Fuzzy Color and Texture Histrogram (FCTH). The use of edge detection, texture and color combined features using fuzzy technique in encoding low-level visual information from images are embedded in the hybridization. First, multiple locations from a target image are chosen in the feature extraction process. Then, a hybridized vector index is proposed for measuring the low-level visual features distance between the target natural images with the training images, allowing a building content to be detected. Size and resolution of the source of images are not restricted in the proposed model and thus it can enhance the computational effectiveness. The empirical assessment, in term of the accuracy in detecting building objects in a set of images, validates the feasibility and potentiality of the proposed techniques.

Modeling Islamic finance knowledge for contract compliance in Islamic banking

Online access to an evolving Islamic finance knowledge repository is crucial to all the stake holders in the Islamic finance domain, including Information Systems that utilize this knowledge for compliance verification and other type of risk management activities. This paper attempts to describe what knowledge of Islamic finance need to be modeled, and how they are modeled using the vocabulary and standards endorsed by the World Wide Web Consortium (W3C). Special attention is focused on the modeling of Islamic Finance compliance heuristics. Competing rule formalisms are considered for this purpose, and this paper reports on the strengths and weakness of each of these formalisms.