An Algorithm for Learning with Probabilistic Description Logics
An Algorithm for Learning with Probabilistic Description Logics
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.
Floriana Esposito
Floriana Esposito
Giorgos Stoilos
Giorgos Stoilos
directed evidential network
Semantic Web
OWL
The OWL is a language for representing ontologies but it is unable to capture the uncertainty about the concepts for a domain. To address the problem of representing uncertainty, we propose in this paper, the theoretical aspects of our tool BeliefOWL which is based on evidential approach. It focuses on translating an ontology into a directed evidential network by applying a set of structural translation rules. Once the network is constructed, belief masses will be assigned to the different nodes in order to propagate uncertainties later.
ontology representation
BeliefOWL: an Evidential Representation in OWL Ontology
ontology
BeliefOWL: an Evidential Representation in OWL Ontology
Dempster-Shafer theory
Bart Gajderowicz
Bart Gajderowicz
Livia Predoiu
Livia Predoiu
Matthias Nickles
Matthias Nickles
José Eduardo Ochoa Luna
José Eduardo Ochoa Luna
Siyao Zheng
Siyao Zheng
Michael Pool
Michael Pool
Ben Yaghlane
Ben Yaghlane
Aldo Gangemi
Aldo
Gangemi
Pavel Smrz
Pavel Smrz
Fernando Bobillo
Fernando Bobillo
Trevor Martin
Trevor Martin
Amira Essaid
Amira Essaid
Fabio G. Cozman
Fabio G. Cozman
Combining Semantic Web Search with the Power of Inductive Reasoning
Combining Semantic Web Search with the Power of Inductive Reasoning
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. A very promising recent approach to such SemanticWeb search is based on combining standard Web search with ontological background knowledge and using standard Web search engines as the main inference motor of Semantic Web search. In this paper, we propose to 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 very likely to occur in distributed and heterogeneous environments, such as the Web. We report on a prototype implementation of the new approach and extensive experimental results.
Linked open data
Semantic Web
Position paper: Uncertainty reasoning for linked data
Linked open data offers a set of design patterns and conventions for sharing data across the semantic web. In this position paper we enumerate some key uncertainty representation issues which apply to linked data and suggest approaches to tackling them. We suggest the need for vocabularies to enable representation of link certainty, to handle ambiguous or imprecise values and to express sets of assumptions based on named graph combinators.
uncertainty reasoning
Position paper: Uncertainty reasoning for linked data
Johanna Voelker
Johanna Voelker
Carlos Henrique Ribeiro
Carlos Henrique Ribeiro
Anders L. Madsen
Anders L. Madsen
Rommel Carvalho
Rommel Carvalho
Sergej Sizov
Sergej Sizov
Thomas Lukasiewicz
Thomas Lukasiewicz
Marcelo Ladeira
Marcelo Ladeira
Georg Gottlob
Georg Gottlob
Dave Robertson
Dave Robertson
Kathryn B. Laskey
Kathryn B. Laskey
Evidential Nearest-Neighbors Classification for Inductive ABox Reasoning
Evidential Nearest-Neighbors Classification for Inductive ABox Reasoning
In the line of our investigation on inductive methods for Semantic Web reasoning, we propose an alternative way for approximate ABox reasoning based on the analogical principle of the nearest-neighbors. Once neighbors of a test individual are selected, a combination rule descending from the Dempster-Shafer theory can join together the evidence provided by the neighbor individuals. We show how to exploit the procedure for determining unknown class- and role-memberships or fillers for datatype properties which may be the basis for many further ABox inductive reasoning algorithms.
Anna Lisa Gentile
Anna Lisa
Gentile
Axiomatic First-Order Probability
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.
probability
first-order logic
Axiomatic First-Order Probability
Peter Vojtas
Peter Vojtas
Nicola Fanizzi
Nicola Fanizzi
Umberto Straccia
Umberto Straccia
Silvia Calegari
Silvia Calegari
Paulo C. G. Costa
Paulo C. G. Costa
Claudia d'Amato
Claudia d'Amato
Andrei Majidian
Andrei Majidian
Valentina Presutti
Valentina
Presutti
Dave Reynolds
Dave Reynolds
Jeff Z. Pan
Jeff Z. Pan
Bettina Fazzinga
Bettina Fazzinga
Andrea Giovanni Nuzzolese
Andrea Giovanni
Nuzzolese
Alireza Sadeghian
Alireza Sadeghian
ontology matching
The popularity of ontologies for representing the semantics behind many real-world domains has created a growing pool of ontologies on various topics. While different ontologists, experts, and organizations create the vast majority of ontologies, often for closed world systems, their domains frequently overlap in an open world system, such as the Semantic Web. These overlapping ontologies sometimes 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 created by executing a tree based data mining algorithm with probability measures over the data being modeled. These rules can be used to revise the ontology adding a higher level of granularity, in order to identify possible similarities missed by the original ontologists. We then discuss how this may be applied to ontology matching. We demonstrate the application of our technique by presenting an example, and discuss how various data types may be treated to generalize the semantics of an ontology for an open world system.
probabilistic ontology
Ontology Granulation Through Inductive Decision Trees
Ontology Granulation Through Inductive Decision Trees
ontology granulation
decision trees
Yung Peng
Yung Peng
Dataset about ursw2009.
Tue May 03 19:04:48 CEST 2016
knowledge fusion
Probabilistic Ontology and Knowledge Fusion for Procurement Fraud Detection in Brazil
ontology
UnBBayes
probabilistic ontology
MEBN
procurement fraud detection
PR-OWL
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.
Probabilistic Ontology and Knowledge Fusion for Procurement Fraud Detection in Brazil
Shou Matsumoto
Shou Matsumoto
creative knowledge discovery
fuzzy taxonomy
uncertainty in Semantic Web
A systematic form of creative knowledge discovery is outlined, requiring taxonomies to generalise knowledge structures and mappings between taxonomies to find parallels between knowledge structures from different domains. These share many of the features needed to handle uncertainty in the semantic web, and results will be relevant to the URSW community.
Fuzzy Taxonomies for Creative Knowledge Discovery
Fuzzy Taxonomies for Creative Knowledge Discovery
fuzzy association rules
Guilin Qi
Guilin Qi
Andreas Tolk
Andreas Tolk
Kenneth J. Laskey
Kenneth J. Laskey
Laécio Santos
Laécio Santos
Daniel Sánchez
Daniel Sánchez