By Zhongli Ding, Yun Peng, Rong Pan (auth.), Zongmin Ma Dr. (eds.)
This booklet covers in a good intensity the quick starting to be subject of instruments, innovations and purposes of sentimental computing (e.g., fuzzy good judgment, genetic algorithms, neural networks, tough units, Bayesian networks, and different probabilistic ideas) within the ontologies and Semantic net. How parts of the Semantic internet (like the RDF, Description Logics, ontologies) should be coated with a tender computing concentration is proven. The publication goals to supply a unmarried account of present reviews in smooth computing techniques to the ontologies and the Semantic net. the target of the ebook is to supply the cutting-edge info to researchers, practitioners, and graduate scholars of the internet intelligence, and while serving the knowledge know-how specialist confronted with non-traditional purposes that make the applying of traditional methods tough or impossible.
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Extra resources for Soft Computing in Ontologies and Semantic Web
6. The input of the implementation is an RDF(S) ontology, the URI of the root node of the overlap graph, and the URI of the subsumption property used in the ontology. Additionally, also an RDF data ﬁle that contains data records annotated according to the ontology may be given. The output is the overlap tables for every concept in the taxonomy extracted from the input RDF(S) ontology. Next, each submodule in the system is discussed brieﬂy. The preprocessing module transforms the taxonomy into a predeﬁned standard form.
For example, in Fig. 5 Lapland was connected directly to Russia, then the information about the disjointness of Lapland and Asia would have been lost. Modeling Uncertainty in Semantic Web Taxonomies 39 5 Computing the Overlaps Based on the solid path structure, the overlap table values o for a selected concept A and a referred concept B could be calculated by the algorithm 2, where notation Xs denotes the set of (sub)concepts subsumed by the concept X. The overlap table for A could be implemented by going through all the concepts of the graph and calculating the overlap value according to the above algorithm.
Web directory alignment is the task of learning mappings between heterogeneous Web directory classes. Our approach is based on a logical framework, which is combined with probability theory (probabilistic Datalog), and aims at ﬁnding the optimum mapping (the mapping with the highest matching probability). It borrows from other approaches like GLUE  the idea of combining several specialized components for ﬁnding the best mapping. Using a probabilistic, logic-based framework bears some nice features: First, in many cases mappings are not absolutely correct, but hold only with a certain probability.