Abstract:
A prototype tool classifies output from an electronic meeting system into a manageable list of concepts, topics, or issues that a group can further evaluate. In an experiment with output from the GroupSystems electronic meeting system, the tool's recall ability was comparable to that of a human facilitator, but took roughly a sixth of the time.
Abstract:
In crime analysis, law enforcement officials have to process a large amount of criminal data and figure out their relationships. It is important to identify different associations among criminal entities. In this paper, we propose the use of a hyperbolic tree view and a hierarchical list view to visualize criminal relationships. A prototype system called COPLINK Criminal Relationship Visualizer was developed. An experiment was conducted to test the effectiveness and the efficiency of the two views. The results show that the hyperbolic tree view is more effective for an "identify" task and more efficient for an "associate" task. The participants generally thought it was easier to use the hierarchical list, with which they were more familiar. When asked about the usefulness of the two views, about half of the participants thought that the hyperbolic tree was more useful, while the other half thought otherwise. Our results indicate that both views can help in criminal relationship visualization. While the hyperbolic tree view performs better in some tasks, the users' experiences and preferences will impact the decision on choosing the visualization technique. © 2004 Elsevier B.V. All rights reserved.
Abstract:
The public safety community in the United States consists of thousands of local, state, and federal agencies, each with its own information system. In the past few years, there has been a thrust on the seamless interoperability of systems in these agencies. Ontology-based interoperability approaches in the public safety domain need to rely on mapping between ontologies as each agency has its own representation of information. However, there has been little study of ontology mapping techniques in this domain. We evaluate current mapping techniques with real-world data representations from law-enforcement and public safety data sources. In addition, we implement an information theory based tool called MIMapper that uses WordNet and mutual information between data instances to map ontologies. We find that three tools: PROMPT, Chimaera, and LOM, have average F-measures of 0.46, 0.49, and 0.68 when matching pairs of ontologies with the number of classes ranging from 13-73. MIMapper performs better with an average F-measure of 0.84 in performing the same task. We conclude that the tools that use secondary sources (like WordNet) and data instances to establish mappings between ontologies are likely to perform better in this application domain. © 2007 Elsevier B.V. All rights reserved.
