Semantic Interoperability in the Fixed Income Securities Industry: A Knowledge Representation Architecture for Dynamic Integration of Web-Based Information

We examine a knowledge representation architecture to support context interchange mediation. For autonomous receivers and sources sharing a common subject domain, the mediator's reasoning engine can devise query plans integrating multiple sources and resolving semantic heterogeneity. Receiver applications obtain the data they need in the form they need it without imposing changes on sources. The KR architecture includes: 1) data models for each source and receiver, 2) subject domain ontologies, containing abstract subject matter conceptualizations that would be known to experienced practitioners in the industry, and 3) context models for each source and receiver that explain how each source or receiver data model implements the abstract concepts from a subject domain ontology. Examples drawn from the fixed income securities industry illustrate problems and solutions enabled by the proposed architecture

Context Mediation Demonstration of Counter-Terrorism Intelligence (CTI) Integration

Examination of intelligence failures prior to the 9/11/01 attacks made clear it that lack of effective information exchange among government agencies hindered the capability of identifying potential threats and preventing terrorist actions. A 2002 National Research Council study noted that “Although there are many private and public databases that contain information potentially relevant to counterterrorism programs, they lack the necessary context definitions (i.e., metadata) and access tools to enable interoperation with other databases and the extraction of meaningful and timely information.”[14] This report clearly recognized the importance of problems that the semantic data integration research community has been studying

Context Mediation Demonstration of Counter-Terrorism Intelligence Integration

In this report, we demonstrate the applicability and value of the context mediation approach in facilitating the effective and correct use of counter-terrorism intelligence information coming from diverse heterogeneous sources

UNSWIRF: A Tunable Imaging Spectrometer for the Near-Infrared

We describe the specifications, characteristics, calibration, and analysis of data from the University of New South Wales Infrared Fabry-Perot (UNSWIRF) etalon. UNSWIRF is a near-infrared tunable imaging spectrometer, used primarily in conjunction with IRIS on the AAT, but suitable for use as a visitor instrument at other telescopes. The etalon delivers a resolving power in excess of 4000 (corresponding to a velocity resolution ~75 km/s), and allows imaging of fields up to 100" in diameter on the AAT at any wavelength between 1.5 and 2.4 microns for which suitable blocking filters are available.Comment: 16 pages, 10 figures, uses psfig.sty and html.sty (included). To appear in Publications of the Astronomical Society of Australi

Information Integration for Counter Terrorism Activities: The Requirement for Context Mediation

The National Research Council has noted that although there are many private and public databases that contain information potentially relevant to counterterrorism programs, they lack the necessary context definitions (i.e., metadata) and access tools to enable interoperation with other databases and the extraction of meaningful and timely information. In this paper we present examples of these problems and a technology developed at MIT, called context mediation, which provides a novel approach for addressing these problems

Framework for the Analysis of the Adaptability, Extensibility, and Scalability of Semantic Information Integration and the Context Mediation Approach

Technological advances such as Service Oriented Architecture (SOA) have increased the feasibility and importance of effectively integrating information from an ever widening number of systems within and across enterprises. A key difficulty of achieving this goal comes from the pervasive heterogeneity in all levels of information systems. A robust solution to this problem needs to be adaptable, extensible, and scalable. In this paper, we identify the deficiencies of traditional semantic integration approaches. The COntext INterchange (COIN) approach overcomes these deficiencies by declaratively representing data semantics and using a mediator to create the necessary conversion programs from a small number of conversion rules. The capabilities of COIN is demonstrated using an example with 150 data sources, where COIN can automatically generate the over 22,000 conversion programs needed to enable semantic interoperability using only six parametizable conversion rules. This paper presents a framework for evaluating adaptability, extensibility, and scalability of semantic integration approaches. The application of the framework is demonstrated with a systematic evaluation of COIN and other commonly practiced approaches.This work has been supported, in part, by MITRE Corp., the MIT-MUST project, the Singapore-MIT Alliance, and Suruga Bank

Semantic Information Integration in the Large: Adaptability, Extensibility, and Scalability of the Context Mediation Approach

There is pressing need for effectively integrating information from an ever increasing number of available sources both on the web and in other existing systems. A key difficulty of achieving this goal comes from the pervasive heterogeneities in all levels of information systems. Existing and emerging technologies, such as the Web, ODBC, XML, and Web Services, provide essential capabilities in resolving heterogeneities in the hardware and software platforms, but they do not address the semantic heterogeneity of the data itself. A robust solution to this problem needs to be adaptable, extensible, and scalable. In this paper, we identify the deficiencies of traditional approaches that address this problem using hand-coded programs or require complete data standardization. The COntext INterchange (COIN) approach overcomes these deficiencies by declaratively representing data semantics and using a mediator to create the necessary conversion programs using a small number of conversion rules. The capabilities of COIN is demonstrated using an intelligence information integration example consisting of 150 data sources, where COIN can automatically generate the over 22,000 conversion programs needed to enable semantic integration using only six parametizable conversion rules. This paper makes a unique contribution by providing a systematic evaluation of COIN and other commonly practiced approaches