Semantic Discovery of Composite GIS Services

With an increasing number of Geographical Information System (GIS) services publicly available on the Web, the discovery of composite GIS services is promising when novel requirements are to be satisfied. GIS services in the repository like ArcGIS software are organized in a tree hierarchy, where a parent node represents a categorial GIS service with a coarser-granularity than its child GIS services, while leaf nodes correspond to atomic and exercisable GIS services. In this setting, discovering appropriate atomic GIS services is challenging. To remedy this issue, this paper proposes a composite GIS service discovery mechanism. Specifically, for the given requirement, select the parent nodes that take the given input parameters as input and remove their inactivated children. Use remaining children to build the network and repeat the previous operation until finding the services that contain the required output. Then record the semantic similarity degree, calculated by services functional description, in this network. By using the simulated annealing algorithm, a composite GIS services solution will be recommended from this semantic network. Evaluation results demonstrate that our approach could give more significant solution compared with the state-of-the-art techniques.

QoS-Based Collaborative Filtering for Web Service Mining

Stored information in the databases is heterogeneous, from various sources, and of large volumes. The web service selection becomes nontrivial, as the users are easily overloaded by vast amount candidates. Using the keyword-based search method, users are struggling to choose the best web services among those having similar features. In the traditional methods, the users set different constraints and QoS parameters of a web service from what's claimed by the provider. Moreover, different researches challenge this problem, introducing semantic discovery process to enable relevant and desired search results. These approaches don't give importance to users' opinions and the selection history. The classical development of the ontology is typically entirely based on high human participation. In this paper, the authors use ontology-based querying, user profile to know the history, new collaborative filtering to calculate user, and query similarity and QoS as the final step for web service selection. The approach combines the syntactic and semantic methods to increase the selection precision.

Knowledge management practice of medical cloud logistics industry: transportation resource semantic discovery based on ontology modelling

PurposeThe booming of the Internet of things (IoT) and artificial intelligence (AI) techniques contributes to knowledge adoption and management innovation for the healthcare industry. It is of great significance to transport the medical resources to required places in an efficient way. However, it is difficult to exactly discover matched transportation resources and deliver to its destination due to the heterogeneity. This paper studies the medical transportation resource discovery mechanism, leading to efficiency improvement and operational innovation.Design/methodology/approachTo solve the transportation resource semantic discovery problem under the novel cloud environment, the ontology modelling approach is used for both transportation resources and tasks information modes. Besides, medical transportation resource discovery mechanism is proposed, and resource matching rules are designed including three stages: filtering reasoning, QoS-based matching and user preferences-based rank to satisfy personalized demands of users. Furthermore, description logic rules are built to express the developed matching rules.FindingsAn organizational transportation case is taken as an example to describe the medical transportation logistics resource semantic discovery process under cloud medical service scenario. Results derived from the proposed semantic discovery mechanism could assist operators to find the most suitable resources.Research limitations/implicationsThe case study validates the effectiveness of the developed transportation resource semantic discovery mechanism, contributing to knowledge management innovation for the medical logistics industry.Originality/valueTo improve task-resource matching accuracy under cloud scenario, this study develops a transportation resource semantic discovery procedure from the viewpoint of knowledge management. The novel knowledge management practice contributes to operational management of the cloud medical logistics service by introducing ontology modelling and creative management.

Multi-Intentional Description of Learning Semantic Web Services

E-learning systems use web service technology to develop distributed applications. Therefore, with the tremendous growth in the number of web services, finding the proper services while ensuring the independence and reusability of the learning objects in a different context has become an important issue and has attracted much interest. This article first proposes an extension of the Ontology Web Language for Services Learning Object (OWLS-LO) model to describe a multi-intentional learning object. This description ensures accessibility to learning objects. This research then presents a service discovery mechanism that uses the new semantic model for service matching. Experimental results show that the proposed semantic discovery mechanism using multi-intention model performs better than discovery mechanism based on single intention.