Successful construction of intelligent systems to automate human reasoning processes often requires the use of a variety of techniques, including symbolic rule-based representations of reasoning and data-driven reenement of the knowledge. Connicts may arise between knowledge derived from the data and expert rules, as evidenced by misdiagnoses which are not correctable using neural network reene...

Service discovery and selection approaches are often done using a centralized registry-based technique, which only captures common Quality of Service criteria. With more and more services offered via social networks, these approaches are not able to evaluate trust in service providers and often fail to comply with new requester’s expectations. This is because theses approaches are not able (i) ...

structural repetitive subsequences are most important portion of biological sequences, which play crucial roles on corresponding sequence’s fold and functionality. biggest class of the repetitive subsequences is “transposable elements” which has its own sub-classes upon contexts’ structures. many researches have been performed to criticality determine the structure and function of repetitive su...

This paper investigates the role of outliers in literature-based knowledge discovery. It shows that detecting interesting outliers which appear in the literature on a given phenomenon can help the expert to find implicit relationships among concepts of different domains. The underlying assumption is that while the majority of articles in the given scientific domain describe matters related to a...

In this position paper, we claim that the need for time consuming data preparation and result interpretation tasks in knowledge discovery, as well as for costly expert consultation and consensus building activities required for ontology building can be reduced through exploiting the interplay of data mining and ontology engineering. The aim is to obtain in a semi-automatic way new knowledge fro...

Over the last decade, several algorithms for process discovery and process conformance have been proposed. Still, it is well-accepted that there is no dominant algorithm in any of these two disciplines, and then it is often difficult to apply them successfully. Most of these algorithms need a close-to expert knowledge in order to be applied satisfactorily. In this paper, we present a recommende...

E-discovery projects typically start with an assessment of the collected electronic data in order to estimate the risk to prosecute or defend a legal case. This is not a review task but is appropriately called early case assessment, which is better known as exploratory search in the information retrieval community. This paper first describes text mining methodologies that can be used for enhanc...