Searching service repositories by combining semantic and ontological matching

Improving Performance of Web Services Query Matchmaking with Automated Knowledge Acquisition

“…A combination of the matches due to the two cues is done to determine an overall semantic similarity score. Our work extends the work by Syeda-Mahmood et al [10], but dynamically learning from previous matchmaking results, extending the ontological vocabulary, and applying the knowledge to subsequent queries.…”

“…Another related effort is Racer [9], which focuses solely on service capability-based semantic matches for application in e-commerce systems. Syeda-Mahmood et al [10] explore the use of domain-independent and domain-specific ontologies for finding matching service descriptions. Domain-independent relationships are derived using an English thesaurus after tokenization and part-of-speech tagging, while domainspecific ontological similarities are derived by inferring semantic annotations associated with Web service descriptions.…”

An Architecture for Web Service Similarity Evaluation Based on their Functional and QoS Aspects

“…In the first group, there exists a group of previously chosen similar services; when a service fails to work at runtime, it is replaced by another based on user context or QoS [8,9]. In the second group, similar services are selected dynamically [3,4,5,6]. In the third group, the external behavior of a Web service like execution paths or its conversations with other services is considered.…”

“…For this purpose, there is a need to seek a way to know the user's preferences about QoS. In most studies such as [3,4,5,6], finding similar services is based on functional similarity in which a number of the best services (k) are selected and introduced to the user. The user then has to select one of them based on his/her preferences about QoS.…”

Leveraging Web Services Discovery with Customizable Hybrid Matching

“…Then, the structural similarity between sub-trees of the concepts and elements is determined, after which the overall similarity is computed as the geometric mean of the structural and linguistic similarities. Syeda-Mahmood et al [16] use domainindependent and domain-specific ontologies to match service descriptions. They combine both cues to determine an overall semantic similarity score and argue that the hybrid matching algorithm gives the better relevancy results than can be achieved using any one cue alone.…”