Semantic Platform for building coherent net of smart services

Information infrastrucrure of modern cities has been developping incredibly fast over last decades. Improvement of all kinds of services has been impatiently demanded by end users in all domains who where forced to keep up-to-date not to lose ground in their spheres of interest. As a result, the majority of services used now are high-quality services that meet the extended requirements of end users. They can be definitely called smart services. The proplem is that there are a lot of services, but they are not compatible with each other. They can hardly be considered as elements of complicated business processes. It leads to creation of new services with duplicating functionality. Observed dynamics of service market development and its short term prediction clearly shows that in near future it will be impossible to satisfy all requests for new services and service infrastructure will become overheated. At the level of enterprises the problem is commonly solved by means of enterprise service bus, at the level of WWW – due to building and overall application of semantic web services. For the level of cities still there are no solutions that allow building complex logical structures based on existing services. The most obvious way for services integration is their unification. Even this simple solution is unimplementable for two reasons. First, it requires huge resources that depend on the total number of services. Second, it can affect the functionality of the services that is inadmissible for end users. So, one can say that at the level of the city integration solutions based on enterprise service bus are too light but Internet oriented solutions such as semantic web servises are too heavy. In this paper we propose a platform for agile service integration that allows linking services using semantic technologies. The platform does not generate additional requirements to services or imposes any restrictions. It supports linking services and, thus, building a net of services. Furthermore, it can reveal possible links between services that can enrich the service infrastructure. Sematic technologies form the base for integration platform. The services and their peculiar features are described in the platform ontology using OWL language. The OWL description of the services clarifies reasonable cases and ways for services usage. Similar approach is used for describing logic of complex services application. The processes of services interaction are defined in ontologies as well. For logic description BPEL is used

VODRE: Visualisation of drools rules execution

Knowledge-based Systems and Expert Systems, in particular, are expensive to build and difficult to validate and debug because of their complexity and dynamism. Therefore, it is not easy for knowledge engineer and domain expert to identify the gaps and mistakes in knowledge base. Unit testing is unable to cover validation process at all stages, in many cases manual thorough review of decision process is needed. In this paper we spot main approaches to validation and verification issue and describe a component that helps to debug a knowledge base by visualising execution of rules that derive a particular result. This component is developed for Knowledge-based Systems built on Drools Platform1 and we demonstrate application of this component in a knowledge-based engineering system for structural optical design

Automated extraction of concept matcher thesaurus from semi-structured catalogue-like sources of data on the web

Ontology design and the process of populating a data-set with knowledge following the chosen or developed ontology to fit the principles of Semantic Web and Linked Open Data is a time-consuming and iterative process, requiring either expert knowledge or a set of tools for data scraping from web. A valid and consistent ontology and knowledge withing the data-set require unification of concepts which means overcoming ambiguity and synonymy of terms which become individuals of ontology. In this paper we spot on techniques used for organising a Russian food product data-set under a light-weight FOOD Ontology and concept matching in particular. Main approaches to data-set concept unification, synonymic term matching and ways to collect dictionaries for matcher are mentioned. The tool for catalogue-like semi-structured resources parsing and thesaurus extraction is developed and introduced for the task of on-the-fly concept matching