Information Space Models for Data Integration, and Entity Resolution

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.