Feiyu Lin

A Survey of Exploiting WordNet in Ontology Matching

Nowadays, many ontologies are used in industry, public adminstration and academia. Although these ontologies are developed for various purposes and domains, they often contain overlapping information. To build a collaborative semantic web, which allows data to be shared and reused across applications, enterprises, and community boundaries, it is necessary to find ways to compare, match and integrate various ontologies. Different strategies (e.g., string similarity, synonyms, structure similarity and based on instances) for determining similarity between entities are used in current ontology matching systems. Synonyms can help to solve the problem of using different terms in the ontologies for the same concept. The WordNet thesauri can support improving similarity measures. This paper provides an overview of how to apply WordNet in the ontology matching research area.

Context-based Ontology Matching: Concept and Application Cases

Work presented in this paper addresses the challenge of bringing together concepts and experiences from two different areas of computer science: context modeling and ontology matching. Current work in the field of automatic ontology matching does not take into account the context of the user during the matching process. This paper is organized as follows, (1) we introduce of the concept of “context” in the ontology matching process, (2) we introduce the use cases and motivation along with an example of the use of context and (3) an approach for context-based semantic matching, which builds on different (weighted) levels of overlap for a better ranking of alignment elements depending on user’s demand.

Semantic matching based on enterprise ontologies

Semantic Web technologies have in recent years started to also find their way into the world of commercial enterprises. Enterprise ontologies can be used as a basis for determining the relevance of information with respect to the enterprise. The interests of individuals can be expressed by means of the enterprise ontology. The main contribution of our approach is the integration of point set distance measures with a modified semantic distance measure for pair-wise concept distance calculation. Our combined measure can be used to determine the intra-ontological distance between sub-ontologies.

Information Reuse and Interoperability with Ontology Patterns and Linked Data : First Experiences from the ExpertFinder Project

Semantic web technologies show great promise in usage scenarios that involve information logistics. This paper is an experience report on improving the semantic web ontology underlying an application used in expert finding. We use ontology design patterns to find and correct poor design choices, and align the application ontology to commonly used semantic web ontologies in order to increase the interoperability of the ontology and application. Lessons learned and problems faced are discussed, and possible future developments of the project mapped out.

A new expanding tree ontology matching method

Currently, many ontologies are available even through they are created by different organizations. Although these ontologies are developed for various application purpores and areas, they often contain overlapping information. In order to achieve interoperability between ontologies, we need to find a way to integrate various ontologies. For example, ontology engineers would like to create new ontologies based on existing ontologies, adapt or extend existing ontologies for the same or different domains. In this context, it is important to find the corresponding entities in different ontologies. The main ideas we contribute to the research field are introducing the expanding ontology tree to determine the semantic similarity of ontologies.

Polygon-Based Similarity Aggregation for Ontology Matching

Due to an increased awareness of potential ontology applications in industry, public administration and academia, a growing number of ontologies are created by different organizations and individuals. Although these ontologies are developed for various application purposes and areas, they often contain overlapping information. In this context, it is necessary to find ways to integrate various ontologies and enable use of multiple ontologies. In this paper, we extend previous work on ontology matching using polygon-based similarity aggregation. The main ideas we contribute to the research field are (1) an improved approach to aggregate the results of distance calculations between concepts in different ontologies by creating polygons for each ontology and (2) to compare the area of these polygons for deciding on similarity.

User-Based Constraint Strategy in Ontology Matching

Ontologies, as essential elements of the semantic web, have been developed for various purposes and in many different domains. Many ontologies are quite extensive and sophisticated, capturing essential knowledge in a domain. However, ontology users or engineers do not only use their own ontologies, but also want to integrate or adapt other ontologies, or even apply multiple ontologies to solve a problem. As ontologies themselves can be heterogenous, it is necessary to find ways to integrate various ontologies and enable cooperation between them. Ontology matching for finding similar parts in the source ontologies or finding translation rules between ontologies is an important first step. Different strategies (e.g., string similarity, synonyms, structure similarity and based on instances) for determining similarity between entities are used in current ontology matching systems. A lot of existing research work in automatic matching does not sufficiently take into account the users requirements in the ontology matching process. In real world, the users or domain experts requirements play an important role. In this paper, we propose to integrate user-based constraint strategy in ontology matching. The proposed approach includes excluding rules, dominant key and extended key definition and extended key aggregation.

HyperCircle: An Efficient Broadcast Protocol for Super-Peer P2P Networks

The challenge of peer-to-peer (P2P) applications is how toefficiently broadcast in large-scale P2P networks. This paper presents the 8-point HyperCircle protocol for efficient broadcasting and routing of messages in P2P networks. The HyperCircle topology consists of n-dimensional 8-point circles where each point in eachdimension can consist of an 8-point circle. A HyperCircle node requires 2*k steps to spread the message to allnodes. The construction and maintenance of the topology including the broadcast algorithm with HyperCircle are introduced. A simulation framework with the HyperCircle protocol is implemented based on OverSim. Initial experimental results comparing protocolslike Chord, Kademlia and HyperCircle are reported. HyperCircle provides the most efficient broadcasting messages.