Results 1 - 10 of 232

WebPIE: A Web-scale parallel inference engine using

by Jacopo Urbania, Spyros Kotoulasa, Jason Maassena, Frank Van Harmelena, Henri Bala
"... The large amount of Semantic Web data and its fast growth pose a significant computational challenge in performing efficient and scalable reasoning. On a large scale, the resources of single machines are no longer sufficient and we are required to distribute the process to improve performance. In th ..."
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. In this article, we propose a distributed technique to perform materializa-tion under the RDFS and OWL ter Horst semantics using the MapReduce programming model. We will show that a straightforward implementation is not efficient and does not scale. Our technique addresses the challenge of dis-tributed reasoning

Distributed OWL EL Reasoning: The Story So

by Core Scholar, Raghava Mutharaju, Pascal Hitzler, Prabhaker Mateti, Raghava Mutharaju, Pascal Hitzler, Prabhaker Mateti
"... Abstract. Automated generation of axioms from streaming data, such as traffic and text, can result in very large ontologies that single machine reasoners cannot handle. Reasoning with large ontologies requires dis-tributed solutions. Scalable reasoning techniques for RDFS, OWL Horst and OWL 2 RL now ..."
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Abstract. Automated generation of axioms from streaming data, such as traffic and text, can result in very large ontologies that single machine reasoners cannot handle. Reasoning with large ontologies requires dis-tributed solutions. Scalable reasoning techniques for RDFS, OWL Horst and OWL 2 RL

Distributed OWL EL Reasoning: The Story So

by Raghava Mutharaju, Pascal Hitzler, Prabhaker Mateti
"... Abstract. Automated generation of axioms from streaming data, such as traffic and text, can result in very large ontologies that single machine reasoners cannot handle. Reasoning with large ontologies requires dis-tributed solutions. Scalable reasoning techniques for RDFS, OWL Horst and OWL 2 RL now ..."
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Abstract. Automated generation of axioms from streaming data, such as traffic and text, can result in very large ontologies that single machine reasoners cannot handle. Reasoning with large ontologies requires dis-tributed solutions. Scalable reasoning techniques for RDFS, OWL Horst and OWL 2 RL

Controlling Communication i Distributed Planning Using Irrelevance Reasoning

by Michael Wolverton Marie Desjardins - In Proceedings of the Fifteenth National Conference on Artificial Intelligence, i 998. 72 FLAIRS-2001
"... Efficient and effective distributed planning requires careful control over how much information the plan-ning agents broadcast to one another. Sending too lit-tle information could result in incorrect plans, while sending too much information could overtax the dis-tributed planning system’s resource ..."
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Efficient and effective distributed planning requires careful control over how much information the plan-ning agents broadcast to one another. Sending too lit-tle information could result in incorrect plans, while sending too much information could overtax the dis-tributed planning system’s

Inference of local rainfall using qualitative reasoning

by Satoru Oishi, Shuichi Ikebuchi - In Proceedings of the 10th International Workshop on Qualitative Reasoning, AAAI , 1996
"... tetsu @ wrcn2.dpri.kyoto-u.ac.jp A new method, which is based on Qualitative Rea-soning, for severe rainfall forecasting which occurs on a relatively small scale is presented in this pa-per. First, the overview of existing numerical weather forecasting methods are shown with their relating scales. S ..."
Abstract - Cited by 2 (0 self) - Add to MetaCart
microphysical processes and is expected to infer the time series variation and dis-tribution of severe rainfall.

Scalable distributed ontology reasoning using DHT-based partitioning

by Qiming Fang, Ying Zhao, Guangwen Yang, Weimin Zheng - In Proceedings of the Asian Semantic Web Conference (ASWC , 2008
"... Abstract. Ontology reasoning is an indispensable step to fully exploit the im-plicit semantics of Semantic Web data. The inherent distribution characteristic of the Semantic Web and huge amount of ontology instance data necessitates effi-cient and scalable distributed ontology reasoning. Current res ..."
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. The published data from each node is distributed using a DHT-based partitioning and stored in well-designed relational databases to support convenient and efficient reasoning through cooperation of the dis-tributed nodes. A practical distributed ontology reasoning and querying system called DORS is developed

Integrating Probabilistic Reasoning with Constraint Satisfaction

by Eric I-hung Hsu , 2011
"... We hypothesize and confirm that probabilistic reasoning is closely related to constraint satisfaction at a formal level, and that this relationship yields effective algorithms for guiding constraint satisfaction and constraint optimization solvers. By taking a unified view of probabilistic inference ..."
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We hypothesize and confirm that probabilistic reasoning is closely related to constraint satisfaction at a formal level, and that this relationship yields effective algorithms for guiding constraint satisfaction and constraint optimization solvers. By taking a unified view of probabilistic

Age, Sex, and Educational Differences in Syllogistic Reasoning1

by Milton F. Nehrke
"... - ̂ reasoning is rare in affective stimulus situa-tions. " The basis for his statement was the judgments of college students regarding the validity of syllogisms which were emotional and nonemotional in content. He observed that the over-all frequency distribution of correct validity judgments ..."
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judgments for the emotional syllogisms was J-shaped with most students having low scores while the distribution for the nonemo-tional syllogisms was basically normally dis-tributed. In addition, he noted that the exact distribution depended on the order in which the syllogisms were administered. When

Chapter 1 DISTRIBUTEDRESOURCE ALLOCATION A Distributed Constraint Reasoning Approach

by Pragnesh Jay Modi, Paul Scerri, Wei-min Shen, Milind Tambe
"... Distributed resource allocation, where a set of agents must assign their re-sources to a set of dynamic tasks, is a challenging, open problem in current multi-agent systems research. In this article, we present three advances in ad-dressing distributed resource allocation. First, we propose a system ..."
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systematic for-malization of the problem and a general solution strategy that maps a formal model of resource allocation into a key problem solving paradigm, namely, dis-tributed constraint-based reasoning (DCR). Such formalizations are necessary to understand the complexity of different types of problems

Bernoulli’s Principle of Insufficient Reason and Conservation of Information in Computer Search

by William A. Dembski, Robert J. Marks Ii
"... Abstract—Conservation of information (COI) popularized by the no free lunch theorem is a great leveler of search algorithms, showing that on average no search outperforms any other. Yet in practice some searches appear to outperform others. In consequence, some have questioned the significance of CO ..."
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of generalization performance, conservation of information, endogenous information, equal dis-tribution of ignorance, evolutionary search, no free lunch theo-rem, principle of indifference, uniform distribution
Results 1 - 10 of 232