124,657 Hits in 1e+01 sec

Magic Inference Rules for Probabilistic Deduction under Taxonomic Knowledge [article]

Thomas Lukasiewicz
2013 arXiv   pre-print
More generally, we even show that all systems of inference rules for taxonomic and probabilistic knowledge-bases over conjunctive events are globally incomplete.  ...  We present locally complete inference rules for probabilistic deduction from taxonomic and probabilistic knowledge-bases over conjunctive events.  ...  Of course, we should exclude taxonomic and probabilistic knowledge-bases like the ones in (a) to (e) from the premises of our inference rules:A taxonomic and probabilistic knowledge-base KB is in consistent  ... 
arXiv:1301.7397v1 fatcat:ebghhza3uffh5md64upngc35by

A probabilistic approach to distributed system management

Randy N. Schauer, Anupam Joshi
2010 Proceeding of the 7th international conference on Autonomic computing - ICAC '10  
In this paper, we discuss the use of statistical inference, specifically Markov Logic Networks, in a distributed multi-agent system to provide the most effective means of managing these parameters.  ...  , especially in those systems that have been built through expansion and not as an initial purchase of identical nodes.  ...  Our approach provides a flexible solution that can be adapted to different types of distributed systems in order to remove the centralized knowledge base as both a single point of failure and a network  ... 
doi:10.1145/1809049.1809063 dblp:conf/icac/SchauerJ10 fatcat:zbhbyf6obve2fdiju7v3l3wl2m

Automatic Knowledge Base Construction using Probabilistic Extraction, Deductive Reasoning, and Human Feedback

Daisy Zhe Wang, Yang Chen, Sean Goldberg, Christan Grant, Kun Li
2012 North American Chapter of the Association for Computational Linguistics  
We envision an automatic knowledge base construction system consisting of three interrelated components.  ...  MADDEN is a knowledge extraction system applying statistical text analysis methods over database systems (DBMS) and massive parallel processing (MPP) frameworks; PROBKB performs probabilistic reasoning  ...  Acknowledgments This material is based upon work supported by the national Science Foundation Graduate Research Fellowship under Grant No. DGE-0802270 and a generous gift from Greenplum.  ... 
dblp:conf/naacl/WangCGGL12 fatcat:4xndivfud5bz7ijgtqrha5qmjy

A probabilistic interpretation of the medical expert system CADIAG-2

David Picado Muiño
2011 Soft Computing - A Fusion of Foundations, Methodologies and Applications  
CADIAG-2 consists of a knowledge base in the form of a set of IF-THEN rules that relate distinct medical entities, in this paper interpreted as conditional probabilistic statements, and an inference engine  ...  Keywords Knowledge-based systems · rule-based expert systems · fuzzy expert systems · probabilistic inference · CADIAG-2  ...  Rusnok for useful comments on previous drafts of this paper and for technical support with CADIAG-2.  ... 
doi:10.1007/s00500-011-0699-y fatcat:c7qjnvzbpfcwpnkbw5s3qfnmn4

Precise Propagation of Upper and Lower Probability Bounds in System P [article]

Angelo Gilio (Dipartimento di Matematica e Informatica - Universita` di Catania, Italy)
2000 arXiv   pre-print
In this paper we consider the inference rules of System P in the framework of coherent imprecise probabilistic assessments.  ...  This allows a more flexible and realistic use of System P in default reasoning and provides an exact illustration of the degradation of the inference rules when interpreted in probabilistic terms.  ...  Introduction In the applications of intelligent systems to automated uncertain reasoning the explicit knowledge of the agent is represented by a knowledge base K, constituted by a set of conditional assertions  ... 
arXiv:math/0003046v1 fatcat:cszueiv3szdyhnvtczw3iuuumi

Local probabilistic deduction from taxonomic and probabilistic knowledge-bases over conjunctive events

Thomas Lukasiewicz
1999 International Journal of Approximate Reasoning  
More generally, we even show that all systems of inference rules for probabilistic deduction in taxonomic and probabilistic knowledge-bases over conjunctive events that have a limited number of probabilistic  ...  We elaborate locally complete inference rules for probabilistic deduction from taxonomic and probabilistic knowledge-bases over conjunctive events.  ...  More precisely, all systems of inference rules for probabilistic deduction in taxonomic and probabilistic knowledge-bases over conjunctive events that have a limited number of probabilistic formulas in  ... 
doi:10.1016/s0888-613x(99)00006-7 fatcat:f7yblvz6o5bbxpwizexlkqqpzm


Xiaofeng Zhou, Yang Chen, Daisy Zhe Wang
2016 Proceedings of the VLDB Endowment  
To address these challenges, we develop a probabilistic knowledge base system, ARCHIMEDESONE, by scaling up the knowledge expansion and statistical inference algorithms.  ...  We design a web interface for users to query and update large knowledge bases. In this paper, we demonstrate the ARCHIMEDESONE system to showcase its efficient query and inference engines.  ...  We gratefully acknowledge the support of NSF under IIS Award # 1526753, DARPA under FA8750-12-2-0348-2 (DEFT/CUBISM), and a generous gift from Google. We also thank Dr. Milenko Petrovic and Dr.  ... 
doi:10.14778/3007263.3007284 fatcat:txierprif5e7djvqorulut6m3e

An infrastructure for probabilistic reasoning with web ontologies

Jakob Huber, Mathias Niepert, Jan Noessner, Joerg Schoenfisch, Christian Meilicke, Heiner Stuckenschmidt, Pascal Hitzler
2016 Semantic Web Journal  
Our results indicate that our system, which is based on a well-founded probabilistic semantics, is capable of solving relevant problems as good as or better than state of the art systems that have specifically  ...  These scenarios are ontology matching, and knowledge base verification, with a special focus on temporal reasoning.  ...  A log-linear logic knowledge base KB consists of a deterministic knowledge base C D and an uncertain knowledge base C U .  ... 
doi:10.3233/sw-160219 fatcat:ar723byi55bjvjafoqh3vq73ey

Integrating Logical and Probabilistic Reasoning for Decision Making [article]

John S. Breese, Edison Tse
2013 arXiv   pre-print
A uniform declarative, first-order, knowledge representation is combined with a set of integrated inference procedures for logical, probabilistic, and decision-theoretic reasoning.  ...  Given a query, a logical proof is produced if possible; if not, an influence diagram based on the query and the knowledge of the decision domain is produced and subsequently solved.  ...  The set of logically derivable conclusions from the knowledge base at the time of a probabilistic inference make up this state of information. knowledge base to make explicit any conditions under which  ... 
arXiv:1304.2751v1 fatcat:ki7rh3tgunaonlqkbustd33zve

Generating Ontologies from Relational Data with Fuzzy-Syllogistic Reasoning [chapter]

Bora İ. Kumova
2015 Communications in Computer and Information Science  
In that sense, a fuzzy syllogistic reasoner can be employed as a generic reasoner that combines possibilistic inferencing with probabilistic ontologies, thus facilitating knowledge exchange between ontology  ...  Fuzzy-syllogistic reasoning with the fuzzy-syllogistic system 4 S provides 2048 possible fuzzy inference schema for every possible triple concept relationship of an ontology.  ...  Moods of the FS system become inferences in FS reasoning.  ... 
doi:10.1007/978-3-319-18422-7_2 fatcat:uufri3e6zng2xp3sw6gjebcdaq

Knowledge Representation for Cognitive Robotic Systems

Emil Vassev, Mike Hinchey
2012 2012 IEEE 15th International Symposium on Object/Component/Service-Oriented Real-Time Distributed Computing Workshops  
Cognitive robots must use their knowledge against the perception of their world and generate appropriate actions in that world in compliance with some goals and beliefs.  ...  Cognitive robotics are autonomous systems capable of artificial reasoning.  ...  As shown in Definition 1, a Knowledge Base is a tuple of three main knowledge components -knowledge corpus (Kc), KB operators (Op) and inference primitives (Ip).  ... 
doi:10.1109/isorcw.2012.36 dblp:conf/isorc/VassevH12 fatcat:roda3klm5bab3jopuxzyyks23u

Towards Distributed MCMC Inference in Probabilistic Knowledge Bases

Mathias Niepert, Christian Meilicke, Heiner Stuckenschmidt
2012 North American Chapter of the Association for Computational Linguistics  
Inference in probabilistic knowledge bases is a computationally challenging problem.  ...  Probabilistic knowledge bases are commonly used in areas such as large-scale information extraction, data integration, and knowledge capture, to name but a few.  ...  Leveraging the theory of sampling independent sets from hypergraphs for efficient inference in probabilistic knowledge bases is straight-forward once the connection between consistent knowledge bases and  ... 
dblp:conf/naacl/NiepertMS12 fatcat:oknhn3sqevfe3jlho7xqzylkqy

Improving ESB Capabilities through Diagnosis Based on Bayesian Networks and Machine Learning

Roberto Koh-Dzul, Mariano Vargas-Santiago, Codé Diop, Ernesto Exposito, Francisco Moo-Mena, Jorge Gómez-Montalvo
2014 Journal of Software  
functioning conditions of the managed system.  ...  The base of our approach is building a Bayesian network as model representing runtime properties of the Managed Element and their relationships.  ...  Probabilistic Diagnostic The knowledge base can be used to make inferences with or without evidences.  ... 
doi:10.4304/jsw.9.8.2206-2211 fatcat:rpdos64tp5crrny4ckufmmlgpy

Evaluation and Comparison Criteria for Approaches to Probabilistic Relational Knowledge Representation [chapter]

Christoph Beierle, Marc Finthammer, Gabriele Kern-Isberner, Matthias Thimm
2011 Lecture Notes in Computer Science  
In the past ten years, the areas of probabilistic inductive logic programming and statistical relational learning put forth a large collection of approaches to combine relational representations of knowledge  ...  , and three approaches based on the principle of maximum entropy.  ...  (U-5) Grounding: Is there a mechanism for (consistent) grounding of a knowledge base?  ... 
doi:10.1007/978-3-642-24455-1_6 fatcat:orp6j3hmj5btnovnt4czh436xa

Towards an Interpretation of the Medical Expert System CADIAG 2 [chapter]

David Picado Muiño, Agata Ciabattoni, Thomas Vetterlein
2013 Studies in Fuzziness and Soft Computing  
In order to do so, we first provide a logical formalization of the inference process by means of a set of rules aimed at describing the steps along the inference and later attempt to provide a sound semantics  ...  The present paper responds to an attempt to interpret the inference process and output of the medical expert system CADIAG2.  ...  CADIAG2 consists basically of two pieces: a knowledge base and an inference engine.  ... 
doi:10.1007/978-3-642-36527-0_22 fatcat:gfjotbcpo5f37pphm5hnyp52la
« Previous Showing results 1 — 15 out of 124,657 results