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- item-1 hasContent claudia-damato.
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- item-5 hasContent thomas-lukasiewicz.
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- 4 creator fabio-g-cozman.
- 4 creator jose-eduardo-ochoa-luna.
- 4 type InProceedings.
- 4 label "An Algorithm for Learning with Probabilistic Description Logics".
- 4 sameAs 4.
- 4 abstract "Probabilistic Description Logics are the basis of ontologies in the Semantic Web. Knowledge representation and reasoning for these logics have been extensively explored in the last years; less attention has been paid to techniques that learn ontologies from data. In this paper we report on algorithms that learn probabilistic concepts and roles. We present an initial effort towards semi-automated learning using probabilistic methods. We combine ILP (Inductive Logic Programming) methods and a probabilistic classifier algorithm (search for candidate hypotheses is conducted by a Noisy-OR classifier). Preliminary results on a real world dataset are presented.".
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- 4 title "An Algorithm for Learning with Probabilistic Description Logics".
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- item-1 hasContent jose-eduardo-ochoa-luna.
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- item-2 hasContent fabio-g-cozman.
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- 5 creator kathryn-b-laskey.
- 5 creator laecio-santos.
- 5 creator marcelo-ladeira.
- 5 creator paulo-c-g-costa.
- 5 creator rommel-carvalho.
- 5 creator shou-matsumoto.
- 5 type InProceedings.
- 5 label "Probabilistic Ontology and Knowledge Fusion for Procurement Fraud Detection in Brazil".
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- 5 abstract "To cope with society’s demand for transparency and corruption prevention, the Brazilian Federal General Comptroller (CGU) has carried out a number of actions, including: awareness campaigns aimed at the private sector; campaigns to educate the public; research initiatives; and regular inspections and audits of municipalities and states. Although CGU has collected information from hundreds of different sources - Revenue Agency, Federal Police, and others - the process of fusing all this data has not been efficient enough to meet the needs of CGU’s decision makers. Therefore, it is natural to change the focus from data fusion to knowledge fusion. As a consequence, traditional syntactic methods must be augmented with techniques that represent and reason with the semantics of databases. However, commonly used approaches fail to deal with uncertainty, a dominant characteristic in corruption prevention. This paper presents the use of Probabilistic OWL (PR-OWL) to design and test a model that performs information fusion to detect possible frauds in procurements involving Federal money. To design this model, a recently developed tool for creating PR-OWL ontologies was used with support from PR-OWL specialists and careful guidance from a fraud detection specialist from CGU.".
- 5 hasAuthorList authorList.
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- 5 keyword "MEBN".
- 5 keyword "PR-OWL".
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- 5 keyword "knowledge fusion".
- 5 keyword "ontology".
- 5 keyword "probabilistic ontology".
- 5 keyword "procurement fraud detection".
- 5 title "Probabilistic Ontology and Knowledge Fusion for Procurement Fraud Detection in Brazil".
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- item-1 hasContent rommel-carvalho.
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- item-2 hasContent kathryn-b-laskey.
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- 6 creator kathryn-b-laskey.
- 6 type InProceedings.
- 6 label "Axiomatic First-Order Probability".
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- 6 abstract "Most languages for the Semantic Web have their logical basis in some fragment of first-order logic. Thus, integrating first-order logic with probability is fundamental for representing and reasoning with uncertainty in the semantic web. Defining semantics for probability logics presents a dilemma: a logic that assigns a real-valued probability to any first-order sentence cannot be axiomatized and lacks a complete proof theory. This paper develops a first-order axiomatic theory of probability in which probability is formalized as a function mapping Gödel numbers to elements of a real closed field. The resulting logic is fully first-order and recursively axiomatizable, and therefore has a complete proof theory. This gives rise to a plausible reasoning logic with a number of desirable properties: the logic can represent arbitrarily fine-grained degrees of plausibility intermediate between proof and disproof; all mathematical and logical assumptions can be explicitly represented as finite computational structures accessible to automated reasoners; contradictions can be discovered in finite time; and the logic supports learning from observation.".
- 6 hasAuthorList authorList.
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- 6 keyword "first-order logic".