Kostyantyn M. Shchekotykhin

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The effective debugging of ontologies is an important prerequisite for their successful application and impact on the semantic web. The heart of this debugging process is the diagnosis of faulty knowledge bases. In this paper we define general concepts for the diagnosis of ontologies. Based on these concepts, we provide correct and complete algorithms for(More)
The complexity of product assortments offered by e-Commerce platforms requires intelligent sales assistance systems alleviating the retrieval of solutions fitting to the wishes and needs of a customer. Knowledge-based recommender applications meet these requirements by allowing the calculation of personalized solutions based on an explicit representation of(More)
The process of populating an ontology-based system with high-quality and upto-date instance information can be both time consuming and prone to error. In many domains, however, one possible solution to this problem is to automate the instantiation process for a given ontology by searching (mining) the web for the required instance information. The primary(More)
Debugging of ontologies is an important prerequisite for their wide-spread application, especially in areas that rely upon everyday users to create and maintain knowledge bases, as in the case of the Semantic Web. Recent approaches use diagnosis methods to identify causes of inconsistent or incoherent ontologies. However, in most debugging scenarios these(More)
Debugging of ontologies is an important prerequisite for their wide-spread application, especially in areas that rely upon everyday users to create and maintain knowledge bases, as in the case of the Semantic Web. Recent approaches use diagnosis methods to identify causes of inconsistent or incoherent ontologies. However, in most debugging scenarios these(More)
Customers interacting with online selling platforms require the assistance of sales support systems in the product and service selection process. Knowledge-based recommenders are specific sales support systems which involve online customers in dialogs with the goal to support preference forming processes. These systems have been successfully deployed in(More)
Efficient acquisition of constraint networks is a key factor for the applicability of constraint problem solving methods. Current techniques learn constraint networks from sets of training examples, where each example is classified as either a solution or non-solution of a target network. However, in addition to this classification, an expert can usually(More)