Mark T. Elmore

TF-ICF: A New Term Weighting Scheme for Clustering Dynamic Data Streams

In this paper, we propose a new term weighting scheme called term frequency-inverse corpus frequency (TF-ICF). It does not require term frequency information from other documents within the document collection and thus, it enables us to generate the document vectors of N streaming documents in linear time. In the context of a machine learning application, unsupervised document clustering, we evaluated the effectiveness of the proposed approach in comparison to five widely used term weighting schemes through extensive experimentation. Our results show that TF-ICF can produce document clusters that are of comparable quality as those generated by the widely recognized term weighting schemes and it is significantly faster than those methods

An ontology-based HTML to XML conversion using intelligent agents

How to organize and classify large amounts of heterogeneous information accessible over the Internet is a major problem faced by industry, government, and military organizations. XML is clearly a potential solution to this problem, however, a significant challenge is how to automatically convert information currently expressed in a standard HTML format to an XML format. Within the Virtual Information Processing Agent Research (VIPAR) project, we have developed a process using Internet ontologies and intelligent software agents to perform automatic HTML to XML conversion for Internet newspapers. The VIPAR software is based on a number of significant research breakthroughs. Most notably, the ability for intelligent agents to use a flexible RDF ontology to transform HTML documents to XML tagged documents. The VIPAR system is currently deployed at the USA Pacific Command, Camp Smith, HI, traversing up to 17 Internet newspapers daily.

An ontology-based software agent system case study

This case study demonstrates a flexible and dynamic approach for fusing data across combinations of participating heterogeneous sources to maximize knowledge sharing. Software agents are used to generate the largest intersection of shared data across any selected data source subset. This ontology-based agent approach maximizes knowledge sharing by dynamically generating common ontologies for the data sources of interest. We validated our approach using (disparate) data sets provided by five national laboratories. A local ontology was defined for each laboratory data source. The ontologies specify how to format the data using XML to make it suitable for query. Consequently, software agents are empowered to provide the ability to dynamically form local ontologies from the data sources. In this way, the cost of developing these ontologies is reduced while providing the broadest possible access to available data sources.

The Semantic Web: The Goal of Web Intelligence

Before a ubiquitous Semantic Web can be realized, there are a number of challenging issues in a number of divergent disciplines that must be solved. How can we better gather, fuse, interpret, analyze, and visualize information stored on the Web? How can we provide a rapid and profound understanding of the information that is available on the Web? This minitrack seeks to explore novel, multidisciplinary research in these and other broad issues related to the Semantic Web.

Minitack: Software Agents and Semantic Web Technologies

The Semantic Web seeks to extend the World Wide Web from a web for human users into a web for Software Agents as well, where Software Agents provide a powerful assist to the human users. Before a ubiquitous and Software Agent-enabled Semantic Web can be realized, there are a number of challenging issues in a number of divergent disciplines that must be solved. How can we better gather, fuse, interpret, analyze, and visualize information stored on the Web? How can we provide a rapid and profound understanding of the information that is available on the Web?

Knowledge Discovery and Data Mining (KDDM) survey report.

The large number of government and industry activities supporting the Unit of Action (UA), with attendant documents, reports and briefings, can overwhelm decision-makers with an overabundance of information that hampers the ability to make quick decisions often resulting in a form of gridlock. In particular, the large and rapidly increasing amounts of data and data formats stored on UA Advanced Collaborative Environment (ACE) servers has led to the realization that it has become impractical and even impossible to perform manual analysis leading to timely decisions. UA Program Management (PM UA) has recognized the need to implement a Decision Support System (DSS) on UA ACE. The objective of this document is to research the commercial Knowledge Discovery and Data Mining (KDDM) market and publish the results in a survey. Furthermore, a ranking mechanism based on UA ACE-specific criteria has been developed and applied to a representative set of commercially available KDDM solutions. In addition, an overview of four R&D areas identified as critical to the implementation of DSS on ACE is provided. Finally, a comprehensive database containing detailed information on surveyed KDDM tools has been developed and is available upon customer request.

The semantic web: structure & critical information issues minitrack

The focus of this minitrack is to explore a broad range of issues involving the Semantic Web and related technologies. Incredible volumes of information, much of it in the form of digital documents, are readily available from virtually any computer in the world. Unfortunately, this increase in information often overwhelms the people it is intending to help. This phenomenon can be seen in business, science, military, and government. The Semantic Web is one proposed solution to this problem.

XML and the semantic web: implications and applications

The semantic web concept extends the idea of XML by providing not only the structure to a Web document, but by providing a way of describing the machine understandable meaning of a document. If this concept can be realised, the power of the Internet and computing in general will be enormously increased. However, many significant hurdles exist to achieving this goal, ranging from semantic representation, to natural language processing, to agent interaction.