Information space models for data integration, and entity resolution

Reid Porter; Linn Collins; James Powell; Reid Rivenburgh

doi:10.1117/12.923055

21 May 2012 Information space models for data integration, and entity resolution

Reid Porter, Linn Collins, James Powell, Reid Rivenburgh

Proceedings Volume 8396, Geospatial InfoFusion II; 83960B (2012) https://doi.org/10.1117/12.923055
Event: SPIE Defense, Security, and Sensing, 2012, Baltimore, Maryland, United States

Abstract

Geospatial information systems provide a unique frame of reference to bring together a large and diverse set of data from a variety of sources. However, automating this process remains a challenge since: 1) data (particularly from sensors) is error prone and ambiguous, 2) analysis and visualization tools typically expect clean (or exact) data, and 3) it is difficult to describe how different data types and modalities relate to each other. In this paper we describe a data integration approach that can help address some of these challenges. Specifically we propose a light weight ontology for an Information Space Model (ISM). The ISM is designed to support functionality that lies between data catalogues and domain ontologies. Similar to data catalogues, the ISM provides metadata for data discovery across multiple, heterogeneous (often legacy) data sources e.g. maps servers, satellite images, social networks, geospatial blogs. Similar to domain ontologies, the ISM describes the functional relationship between these systems with respect to entities relevant to an application e.g. venues, actors and activities. We suggest a minimal set of ISM objects, and attributes for describing data sources and sensors relevant to data integration. We present a number of statistical relational learning techniques to represent and leverage the combination of deterministic and probabilistic dependencies found within the ISM. We demonstrate how the ISM provides a flexible language for data integration where unknown or ambiguous relationships can be mitigated.

Citation Download Citation

Reid Porter, Linn Collins, James Powell, and Reid Rivenburgh "Information space models for data integration, and entity resolution", Proc. SPIE 8396, Geospatial InfoFusion II, 83960B (21 May 2012); https://doi.org/10.1117/12.923055

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available