A connectionist inferencing network, based on a fuzzy version of Kohonen’s model already developed by the authors, is proposed. It is capable of handling uncertainty and/or impreciseness in the input representation provided in quantitative, linguistic and/or set forms. The output class membership value of an input pattern is inferred by the trained network. A measure of certainty expressing con...

The purpose of this paper is to provide an introduction to the OWL Web ontology language, a survey focused on the current state of the art in OWL inferencing capabilities, and a historical perspective on procedural attachments. The perspective is aligned with current OWL research. Several limitations of the OWL language and proposed extensions to overcome these limitations are discussed. A fram...

Ontology-based context models are widely used in a ubiquitous computing environment. Among many benefits such as acquisition of conceptual context through inference, context sharing, and context reusing, the ontology-based context model enables context-aware applications to use conceptual contexts which cannot be acquired by sensors. However, inferencing causes processing delay and it becomes a...

interpretation is the name given to the generic process of "executing" a program on a lattice which is much simpler than the standard execution lattice. This process produces a kind of "homomorphic image" of the real computation, and is often used for various kinds of static analysis [Cousot77, Mycroft81, Burn87]. Most "forward" type inference, including that performed by Kaplan-Ullman, Beer an...

Flood water in Lake Manapouri is released according to strictly formulated flood rules. B e Realtime Flood Assistant is an expert system which incorporates Lake Manapouri flood rules and the txperience of the control room operators at Transpower NZ Ltd. to assist them in the release of flood water. The expert system is being developed in Level5 Object. The program is mainly an event-driven syst...

Serverless computing has emerged as a compelling paradigm for the development and deployment of a wide range of event based cloud applications. At the same time, cloud providers and enterprise companies are heavily adopting machine learning and Artificial Intelligence to either differentiate themselves, or provide their customers with value added services. In this work we evaluate the suitabili...

With a huge amount of RDF data available on the web, the ability to find and access relevant information is crucial. Traditional approaches to storing, querying, and inferencing fall short when faced with web-scale data. We present a system that combines the computational power of large clusters for enabling large-scale inferencing and data access with an efficient data structure for storing an...

This paper describes NAIVE, a low-level knowledge representation language and inferencing process. NAIVE has been designed for reasoning about nondeterministic dynamic systems like those found in medicine. Knowledge is represented in a graph structure consisting of nodes, which correspond to the variables describing the system of interest, and arcs, which correspond to the procedures used to in...

We propose a novel scheme for designing fuzzy rule based classifier. An SOFM based method is used for generating a set of prototypes which is used to generate a set of fuzzy rules. Each rule represents a region in the feature space that we call the context of the rule. The rules are tuned with respect to their context. We justified that the reasoning scheme may be different in different context...

The aim of the experiment is to put the domainand implementationindependent theory of granularity to the test with the subject domain of human infectious diseases. After determining the data sources, defining the model, and data manipulation operators, the granular perspectives and their levels were defined and contents added. Subsequently, granular information retrieval is tested for cholera a...