Christopher J. Matheus

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The notions of ''situation'' and ''situation awareness'' have been formulated by many authors in various contexts. In this paper, we present a formalization of situations that is compatible with the interpretation of situation awareness in terms of human awareness as well as the situation theory of Barwise and Devlin. The purpose of this paper is to capture(More)
— One of the promises of the Semantic Web is to support applications that easily and seamlessly deal with heterogeneous data. Most data on the Web, however, is in the Extensible Markup Language (XML) format, but using XML requires applications to understand the format of each data source that they access. To achieve the benefits of the Semantic Web involves(More)
Situation awareness involves the identification and monitoring of relationships among objects participating in an evolving situation. This problem in general is intractable (i.e., there is a potentially infinite number of relations that could be tracked) and thus requires additional constraints and guidance defined by the user if there is to be any hope of(More)
We present an iterative algorithm for nonlinear regression based on construction of sparse polynomials. Polynomials are built sequentially from lower to higher order. Selection of new terms is accomplished using a novel look-ahead approach that predicts whether a variable contributes to the remaining error. The algorithm is based on the tree-growing(More)
BaseVISor is a forward-chaining inference engine based on a Rete network optimized for the processing of RDF triples. A clause within the body and head of a rule either represents an RDF triple or invokes a procedural attachment (either built-in or user defined). This paper describes how BaseVISor has been outfitted to process RuleML and R-Entailment rules.(More)
Integrating and relating heterogeneous data using inference is one of the cornerstones of semantic technologies and there are a variety of ways in which this may be achieved. Cross source relationships can be automatically translated or inferred using the axioms of RDFS/OWL, via user generated rules, or as the result of SPARQL query result transformations.(More)
The automated discovery of knowledge in databases is becoming increasingly important as the world's wealth of data continues to grow exponentially. Knowledge-discovery systems face challenging problems from real-world databases which tend to be dynamic, incomplete, redundant , noisy, sparse, and very large. This paper addresses these problems and describes(More)