Ontology alignment has become an important process for identifying similarities and differences between ontologies, to facilitate their integration reuse. To this end, fuzzy string-matching algorithms have been developed strings similarity detection used in ontology alignment. However, a significant limitation of existing is reliance on lexical/syntactic contents only, which do not capture sema...

The article identifies the features of innovative projects that should be taken into account when building models information processes in decision support systems (DSS) for project management. It is shown that, terms taking these features, methods forming knowledge form ontologies and use analysis procedures based on precedent seem to promising. limitations existing methods, including those in...

With the explosive growth of information on the Web, it has become more difficult to access relevant information from the Web. One possible approach to solve this problem is web personalization. In Semantic Web, user access behavior models can be shared as ontology. Agent software can then utilize it to provide personalized services such as recommendation and search. To achieve this, we need to...

The success and proliferation of the Semantic Web depends heavily on construction of Web ontologies. However, classical ontology construction approaches are not sufficient for handling imprecise and uncertain information that is commonly found in many application domains. Therefore, great efforts on construction of fuzzy ontologies have been made in recent years. In this paper, we propose a for...

Time modeling is a crucial feature in many application domains. However, temporal information often is not crisp, but is uncertain, subjective and vague. This is particularly true when representing historical information, as historical accounts are inherently imprecise. Similarly, we conjecture that in the Semantic Web representing uncertain temporal information will be a common requirement. He...

We propose a novel semi-supervised clustering method called GO Fuzzy c-means, which enables the simultaneous use of biological knowledge and gene expression data in a probabilistic clustering algorithm. Our method is based on the fuzzy c-means clustering algorithm and utilizes the Gene Ontology annotations as prior knowledge to guide the process of grouping functionally related genes. Unlike tr...