In this paper, we present new algorithms for matching Web services described in YASA4WSDL (YASA for short). We have already defined YASA that overcomes some issues missing in WSDL or SAWSDL. In this paper, we continue on our contribution and show how YASA Web services are matched based on the specificities of YASA descriptions. Our matching algorithm consists of three variants based on three different semantic matching degree aggregations. This algorithm was implemented in YASA-M, a new Web service matchmaker. YASA-M is evaluated and compared to well known approaches for service matching. Experiments show that YASA-M provides better results, in terms of precision, response time, and scalability, than a well known matchmaker.
Nowadays, real-time systems and intelligent systems offer more and more control interface based on voice recognition or human language recognition. Robots and drones will soon be mainly controlled by voice. Other robots will integrate bots to interact with their users, this can be useful both in industry and entertainment. At first, researchers were digging on the side of "ontology reasoning". Given all the technical constraints brought by the treatment of ontologies, an interesting solution has emerged in last years: the construction of a model based on machine learning to connect a human language to a knowledge base (based for example on RDF). We present in this paper our contribution to build a bot that could be used on real-time systems and drones/robots, using recent machine learning technologies.
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