William Sunna

Structural Alignment Methods with Applications to Geospatial Ontologies

We consider the problem of enabling interoperability and information sharing among geospatial applications that use ontologies to describe their concepts and the relationships among them. We present two fully automatic alignment methods that use the graph structures of a pair of ontologies to establish their alignment, that is, the semantic correspondences between their concepts. We have tested our methods on geospatial ontologies pertaining to wetlands and four other pairs that belong to a repository that has been used in the Ontology Alignment Evaluation Initiative (OAEI). Using these ontologies, we have compared the effectiveness (precision and recall) of our methods against the Similarity Flooding Algorithm that was proposed by others and show that for each of the tested ontologies one of our methods is at least as effective as their method. We have tuned the performance of our methods by introducing a greedy approach that reduces the number of concepts that get compared. This approach reduces runtime by approximately 30% with a minor compromise to the effectiveness of the results. To further validate our approach, we participated in the OAEI competition to align a pair of ontologies, each with a few thousand concepts.

A visual tool for ontology alignment to enable geospatial interoperability

In distributed geospatial applications with heterogeneous databases, an ontology-driven approach to data integration relies on the alignment of the concepts of a global ontology that describe the domain, with the concepts of the ontologies that describe the data in the distributed databases. Once the alignment between the global ontology and each distributed ontology is established, agreements that encode a variety of mappings between concepts are derived. In this way, users can potentially query hundreds of geospatial databases using a single query. Using our approach, querying can be easily extended to new data sources and, therefore, to new regions. In this paper, we describe the AgreementMaker, a tool that displays the ontologies, supports several mapping layers visually, presents automatically generated mappings, and finally produces the agreements.

Handling semantic heterogeneities using declarative agreements

The focus of this paper is on interoperability issues to achieve data integration in distributed databases for geographic applications. Our concrete application is in the context of the State of Wisconsin Land Information System (WLIS). In WLIS, data is stored in XML using independently maintained local databases. However, answers to many queries must span several databases. Our approach is based on the existence of a central ontology and on declarative transformations, called \it agreements, between the schemas of the local databases and the central ontology. Using our approach, end users can seamlessly query and aggregate semantically related data that is available throughout the state using a visual interface. An expert at the site of the local database uses another visual interface to specify the agreement between that database and the ontology. Our approach has been fully developed and tested.

Semi-automatic ontology alignment for geospatial data integration

In geospatial applications with heterogeneous databases, an ontology-driven approach to data integration relies on the alignment of the concepts of a global ontology that describe the domain, with the concepts of the ontologies that describe the data in the local databases. Once the alignment between the global ontology and each local ontology is established, users can potentially query hundreds of databases using a single query that hides the underlying heterogeneities. Using our approach, querying can be easily extended to a new data source by aligning a local ontology with the global one. For this purpose, we have designed and implemented a tool to align ontologies. The output of this tool is a set of mappings between concepts, which will be used to produce the queries to the local databases once a query is formulated on the global ontology. To facilitate the user’s task, we propose semi-automatic methods for propagating such mappings along the ontologies. In this paper, we present the principles behind our propagation method, the implementation of the tool, and we conclude with a discussion of interesting cases and proposed solutions.

Structure-based methods to enhance geospatial ontology alignment

In geospatial applications with heterogeneous classification schemes that describe related domains, an ontology-driven approach to data sharing and interoperability relies on the alignment of concepts across different ontologies. To enable scalability both in the size and the number of the ontologies involved, the alignment method should be automatic. In this paper, we propose two fully automatic alignment methods that use the structure of the ontology graphs for contextual information, thus providing the matching process with more semantics. We have tested our methods on a set of geospatial ontologies pertaining to the domain of wetlands and on four sets that belong to an ontology repository that is becoming the standard for testing ontology alignment techniques. We have compared the effectiveness and efficiency of the proposed methods against two previous approaches. The effectiveness results that we have obtained with at least one of the new methods are as good or better than the results obtained with the previously proposed methods.

A Web Query System for Heterogeneous Government Data

This paper describes a Web-based query system for semantically heterogeneous government-produced data. Geospatial Web-based information systems and portals currently are being developed by various levels of government along with the GIS community. Typically, these sites provide data discovery and download capabilities but do not include the ability to pose DBMS-type queries. One of the main problems in querying distributed government data sources is the difference in semantics used by various jurisdictions. We extend work in schema integration by focusing on resolving semantics at the value level in addition to the schema or attribute level. We illustrate our method using land use data, but the method can be used to query across other heterogeneous sets of values. Our work starts from an XML Web-based DBMS and adds functionality to accommodate heterogeneous data between jurisdictions. Our ontology and query rewrite systems use mappings to enable querying across distributed heterogeneous data.