Anca-Andreea Ivan

Searching service repositories by combining semantic and ontological matching

In this paper, we explore the use of domain-independent and domain-specific ontologies to find matching service descriptions. The domain-independent relationships are derived using an English thesaurus after tokenization and part-of-speech tagging. The domain-specific ontological similarity is derived by an inference on the semantic annotations associated with Web service descriptions. Matches due to the two cues are combined to determine an overall semantic similarity score. By combining multiple cues, we show that better relevancy results can be obtained for service matches from a large repository, than could be obtained using any one cue alone.

SEMAPLAN: Combining Planning with Semantic Matching to Achieve Web Service Composition

In this paper, we present a novel algorithm to compose Web services in the presence of semantic ambiguity by combining semantic matching and AI planning algorithms. Specifically, we use cues from domain-independent and domain-specific ontologies to compute an overall semantic similarity score between ambiguous terms. This semantic similarity score is used by AI planning algorithms to guide the searching process when composing services. Experimental results indicate that planning with semantic matching produces better results than planning or semantic matching alone. The solution is suitable for semi-automated composition tools or directory browsers

User Collaborative Semantic Web Service Matching for Legacy Application Integration A Case Study

In this paper we present the results of a case study on the application of our semantic Web service matching system to a real-world legacy application integration problem. Experimental results on our initial data sets indicate that the system on its own is able to achieve close to an average of 50% recall and 54% precision measures in matching the parameters that define the interfaces of business applications. Additional performance enhancements of up to 95% recall and 72% precision measures were achieved with the usage of domain ontologies created by an expert. Therefore, we believe a user working with a semantics-based intelligence tool could gain significant productivity enhancements in IT projects rather than working alone. These productivity enhancements can be quickly translated into savings in time and money in business process integration projects.

Deep Visibility in Enterprise Value Networks: Knowledge Models, Real-Time Monitoring and E-Commerce

Value networks represent a novel approach to model complex enterprise relationships from the perspective of value creation, propagation, and evolution. The success of a value network relies on timely sensing of business insights from high volume process and product performance information. This paper addresses three major challenges of value network-driven enterprise e-commerce analysis: (1) unifying the business knowledge of multiple enterprises and their diverse and conflicting objectives in the value network, (2) sensing the value network and processes through a real-time system, and (3) analyzing the quantifiable value each enterprise contributes to the value network. The solution presented in this paper consists of a semantic knowledge model capturing multi-enterprise objectives and relationships between performance metrics, and a system to monitor, integrate, and analyze enterprise performance information