Hybrid Semantic Service Matchmaking Method Based on a Random Forest

Semantic Service Matchmaking(SSM)can be leveraged for mining the most suitable service to accommodate a diversity of user demands.However,existing research on SSM mostly involves logical or non-logical matching,leading to unavoidable false-positive and false-negative problems.Combining different types of SSM methods is an effective way to improve this situation,but the adaptive combination of different service matching methods is still a difficult issue.To conquer this difficulty,a hybrid SSM method,which is based on a random forest and combines the advantages of existing SSM methods,is proposed in this paper.The result of each SSM method is treated as a multi-dimensional feature vector input for the random forest,converting the service matching into a two classification problem.Therefore,our method avoids the flaws found in manual threshold setting.Experimental results show that the proposed method achieves an outstanding performance.

World-Wide Semantic Web of Agriculture Knowledge

The rapid increase in the publication of knowledge bases as linked open data （LOD） warrants serious consideration from all concerned, as this phenomenon will potentially scale exponentially. This paper will briefly describe the evolution of the LOD, the emerging world-wide semantic web （WWSW）, and explore the scalability and performance features Of the service oriented architecture that forms the foundation of the semantic technology platform developed at MIMOS Bhd., for addressing the challenges posed by the intelligent future internet. This paper＂ concludes with a review of the current status of the agriculture linked open data.

Key technologies of artificial intelligence in electric power customer service

As the demand for customer service continues to increase,more companies are attempting to apply artificial intelligence technology in the field of customer service,enabling intelligent customer service,reducing customer service pressure,and reducing operating costs.Currently,the existing intelligent customer service has a limited degree of intelligence and can only answer simple user questions,and complex user expressions are difficult to understand.To solve the problem of low accuracy of multi-round dialogue semantic understanding,this paper proposes a semantic understanding model based on the fusion of a convolutional neural network(CNN)and attention.The model builds an“intention-slot”joint model based on the“encoding–decoding”framework and uses hidden semantic information that combines intent recognition and slot filling,avoiding the problem of information loss in traditional isolated tasks,and achieving end-to-end semantic understanding.Additionally,an improved attention mechanism based on CNNs is introduced in the decoding process to reduce the interference of redundant information in the original text,thereby increasing the accuracy of semantic understanding.Finally,by applying the model to electric power intelligent customer service,we verified through an experimental comparison that the proposed fusion model improves the performance of intent recognition and slot filling and can improve the user experience of electric power intelligent customer services.

Hybrid QoS-aware semantic web service composition strategies

Hybrid QoS model which consists of certain and uncertain expressions has strong power of semantic QoS description. For solving the hybrid QoS-aware semantic web service composition problem, this paper presents an Uncertain Multi-attribute decision making based Composition algorithm （UMC）. The UMC includes two parts. First, UMC-Core can be used to synthetically evaluate the hybrid service quality information. Second, UMC-DH （Distributed and Heuristic framework for UMC） aims at enhancing the run-time performance of UMC when the problem space increases. The simulation results show that the UMC has lower execution cost, higher approximation ratio and success ratio than other similar approaches.

Quality Model and Metrics of Ontology for Semantic Descriptions of Web Services

An ontology is a conceptualisation of domain knowledge. It is employed in semantic web services technologies to describe the meanings of services so that they can be dynamically searched for and composed according to their meanings. It is essential for dynamic service discovery, composition, and invocation. Whether an ontology is well constructed has a tremendous impact on the accuracy of the semantic description of a web service, the complexity of the semantic definitions, the efficiency of processing messages passed between services, and the precision and recall rates of service retrieval from service registrations. However, measuring the quality of an ontology remains an open problem. Works on the evaluation of ontologies do exist, but they are not in the context of semantic web services. This paper addresses this problem by proposing a quality model of ontology and defining a set of metrics that enables the quality of an ontology to be measured objectively and quantitatively in the context of semantic descriptions of web services. These metrics cover the contents, presentation, and usage aspects of ontologies. The paper also presents a tool that implements these metrics and reports a case study on five real-life examples of web services.

Efficient Composition of Semantic Web Services with End-to-End QoS Optimization

The efficiency of QoS-aware service composition is important since most service composition problems are known to be NP-hard. With the growing number of web services, service composition is like a decision problem on selecting services or/and execution plans to satisfy the users＇ end-to-end QoS requirements （e.g. response time, throughput）. Composite services with the same functionality may have dif-ferent execution plans, which may cause different end-to-end QoS. This paper presents a model combining semantic data-links and QoS, which leads to an efficient approach to automatic construction of a composite service with optimal end-to-end QoS. The approach is based on a greedy algorithm to select both services and execution plans for composite services. Empirical and theoretical analyses of the approach show that its time complexity is O（mn2） for a repository with n services and an ontology with m concepts. Moreover, the approach increases linearly in time when using an index to search services in the repository. Tests with a repository with 20 000 services and an ontology with 300 000 concepts show that the algorithm significantly outperforms current existing algorithms in terms of composition efficiency while achieving optimal end-to-end QoS.

A Semantic Matchmaker for Ranking Web Services

This paper is concerned with the matchmaker for ranking web services by using semantics. So far several methods of semantic matchmaker have been proposed. Most of them, however, focus on classifying the services into predefined categories rather than providing a ranking result. In this paper, a new method of semantic matchmaker is proposed for ranking web services. It is proposed to use the semantic distance for estimating the matching degree between a service and a user request. Four types of semantic distances are defined and four algorithms are implemented respectively to calculate them. Experimental results show that the proposed semantic matchmaker significantly outperforms the keywordbased baseline method.

An automatic subdigraph renovation plan for failure recovery of composite semantic Web services

A Web service-based system never fulfills a user＇s goal unless a failure recovery approach exists. It is inevitable that several Web services may either perish or fail before or during transactions. The completion of a composite process relies on the smooth execution of all constituent Web services. A mediator acts as an intermediary between providers and consumers to monitor the execution of these services. If a service fails, the mediator has to recover the whole composite process or else jeopardize achieving the intended goals. The atomic replacement of a perished Web service usually does not apply because the process of locating a matched Web service is unreliable. Even the system cannot depend on the replacement of the dead service with a com- posite service. In this paper, we propose an automatic renova- tion plan for failure recovery of composite semantic services based on an approach of subdigraph replacement. A replacement subdigraph is posed in lieu of an original subdigraph, which includes the failed service. The replacement is done in two separate phases, ofltine and online, to make the recovery faster. The ofitine phase foresees all possible subdigraphs, pre-calculates them, and ranks several possible replacements. The online phase compensates the unwanted effects and executes the replacement subdigraph in lieu of the original subdigraph. We have evaluated our approach during an experiment and have found that we could recover more than half of the simulated failures. These achievements show a significant improvement compared to current approaches

AUTOMATIC PATENT DOCUMFNT SUMMARIZATION FOR COLLABORATIVE KNOWLEDGE SYSTEMS AND SERVICES

Engineering and research teams often develop new products and technologies by referring to inventions described in patent databases. Efficient patent analysis builds R＆D knowledge, reduces new product development time, increases market success, and reduces potential patent infringement. Thus, it is beneficial to automatically and systematically extract information from patent documents in order to improve knowledge sharing and collaboration among R＆D team members. In this research, patents are summarized using a combined ontology based and TF-IDF concept clustering approach. The ontology captures the general knowledge and core meaning of patents in a given domain. Then, the proposed methodology extracts, clusters, and integrates the content of a patent to derive a summary and a cluster tree diagram of key terms. Patents from the International Patent Classification （IPC） codes B25C, B25D, B25F （categories for power hand tools） and B24B, C09G and H011 （categories for chemical mechanical polishing） are used as case studies to evaluate the compression ratio, retention ratio, and classification accuracy of the summarization results. The evaluation uses statistics to represent the summary generation and its compression ratio, the ontology based keyword extraction retention ratio, and the summary classification accuracy. The results show that the ontology based approach yields about the same compression ratio as previous non-ontology based research but yields on average an 11% improvement for the retention ratio and a 14% improvement for classification accuracy.