Uncertainty Modeling for Data Mining: A Label Semantics Approach
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Topics in Cognitive Science, 1 , Joint models of neural and behavioral data.
Uncertainty Modeling For Data Mining A Label Semantics Approach
Springer: New York. Inferring expertise in knowledge and prediction ranking tasks. Topics in Cognitive Science, 4 , Topics in Semantic Representation. Psychological Review, 2 , In addition, he has served as a consultant for a variety of companies such as eBay, Yahoo, Netflix, Merriam Webster, Rubicon and Gimbal on machine learning problems. For his empirical research and computational modeling work, Dr. For Description Logics with subjective probabilities reasoning procedures for testing instance relations based on the completion method have been developed.
Fuzzy Description Logics DLs are a formalism for the representation of structured knowledge that is imprecise or vague by nature. In fuzzy DLs, restricting to a finite set of degrees of truth has proved to be useful, both for theoretical and practical reasons. In this paper, we propose finite fuzzy DLs as a generalization of existing approaches. We assume a finite totally ordered set of linguistic terms or labels, which is very useful in practice since expert knowledge is usually expressed using linguistic terms.
Then, we consider fuzzy DLs based on any smooth t-norm defined over this set. Finally, we extend our logic in two directions: by considering non-smooth t-norms and by considering additional DL constructors. Classical ontologies are not suitable to represent imprecise or vague information, which has led to several extensions using non-classical logics. In particular, several fuzzy extensions have been proposed in the literature. We discuss how to use it for fuzzy ontology representation and reasoning, and describe some implementation details and optimization techniques.
An empirical evaluation demonstrates that these optimizations considerably improve the performance of the reasoner.
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Term similarity assessment usually leads to situations where contradictory evidence support has different views concerning the meaning of a concept and how similar it is to other concepts. Human experts can resolve their differences through discussion, whereas ontology mapping systems need to be able to eliminate contradictions before similarity combination can achieve high quality results. In these situations, different similarities represent conflicting ideas about the interpreted meaning of the concepts.
Such contradictions can contribute to unreliable mappings, which in turn worsen both the mapping precision and recall. In order to avoid including contradictory beliefs in similarities during the combination process, trust in the beliefs needs to be established and untrusted beliefs should be excluded from the combination.
In this chapter, we propose a solution for establishing fuzzy trust to manage belief conflicts using a fuzzy voting model. An important problem for the success of ontology-based applications is how to provide persistent storage and querying. For that purpose, many RDF tools capable of storing and querying over a knowledge base, have been proposed. Recently, fuzzy extensions to ontology languages have gained considerable attention especially due to their ability to handle vague information.
In this paper we investigate on the issue of using classical RDF storing systems in order to provide persistent storing and querying over large scale fuzzy information.
Uncertainty Modeling for Data Mining
To accomplish this we propose a novel way for serializing fuzzy information into RDF triples, thus classical storing systems can be used without any extensions. Additionally, we extend the existing query languages of RDF stores in order to support expressive fuzzy querying services over the stored data. Finally, the proposed architecture is evaluated using an industrial application scenario about casting for TV commercials and spots.
Uncertainty is an intrinsic feature of real world knowledge, hence it is important to take it into account when building logic rule formalisms. In this paper, we first present two techniques of encoding uncertain knowledge and its fuzzy semantics in RIF-BLD presentation syntax. In addition, rules in Logic Programs LP are often used in combination with the other widely-used knowledge representation formalism of the Semantic Web, namely Description Logics DL , in many application scenarios of the Semantic Web.
It is well known that manually formalizing a domain is a tedious and cumbersome process. It is constrained by the knowledge acquisition bottleneck. Therefore, many researchers have developed algorithms and systems to help automate the process. Among them are systems that incorporate text corpora in the knowledge acquisition process.
Here, we provide a novel method for unsupervised bottom-up ontology generation. It is based on lexico-semantic structures and Bayesian reasoning to expedite the ontology generation process.
To illustrate our approach, we provide three examples generating ontologies in diverse domains and validate them using qualitative and quantitative measures. The examples include the description of high-throughput screening data relevant to drug discovery and two custom text corpora.
Our unsupervised method produces viable results with sometimes unexpected content. It is complementary to the typical top-down ontology development process. Our approach may therefore also be useful to domain experts. Extensive research activities are recently directed towards the Semantic Web as a future form of the Web.
Consequently, Web search as the key technology of the Web is evolving towards some novel form of Semantic Web search. In this paper, we further enhance this approach to Semantic Web search by the use of inductive reasoning techniques. This adds especially the important ability to handle inconsistencies, noise, and incompleteness, which are all very likely to occur in distributed and heterogeneous environments, such as the Web.
costawebdesign.es/ca-tienda-zithromax-250mg.php We report on a prototype implementation of the new approach and experimental results. The popularity of ontologies for representing the semantics behind many real-world domains has created a growing pool of ontologies on various topics. Different ontologists, experts, and organizations create a great variety of ontologies, often for narrow application domains. Some of the created ontologies frequently overlap with other ontologies in broader domains if they pertain to the Semantic Web. Sometimes, they model similar or matching theories that may be inconsistent.
To assist in the reuse of these ontologies, this paper describes a technique for enriching manually created ontologies by supplementing them with inductively derived rules, and reducing the number of inconsistencies. The derived rules are translated from decision trees with probability measures, created by executing a tree based data mining algorithm over the data being modelled.