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Structure Learning via Parameter Learning
"... A key challenge in information and knowledge management is to automatically discover the underlying structures and patterns from large collections of extracted information. This paper presents a novel structurelearning method for a new, scalable probabilistic logic called ProPPR. Our approach bui ..."
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A key challenge in information and knowledge management is to automatically discover the underlying structures and patterns from large collections of extracted information. This paper presents a novel structurelearning method for a new, scalable probabilistic logic called ProPPR. Our approach builds on the recent success of metainterpretive learning methods in Inductive Logic Programming (ILP), and we further extends it to a framework that enables robust and efficient structure learning of logic programs on graphs: using an abductive secondorder probabilistic logic, we show how firstorder theories can be automatically generated via parameter learning. To learn better theories, we then propose an iterated structural gradient approach that incrementally refines the hypothesized space of learned firstorder structures. In experiments, we show that the proposed method further improves the results, outperforming competitive baselines such as Markov Logic Networks (MLNs) and FOIL on multiple datasets with various settings; and that the proposed approach can learn structures in a large knowledge base in a tractable fashion.
Structure learning of probabilistic logic programs by searching the clause space
 CoRR/arXiv:1309.2080
, 2013
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Inducing Probabilistic Relational Rules from Probabilistic Examples∗
"... We study the problem of inducing logic programs in a probabilistic setting, in which both the example descriptions and their classification can be probabilistic. The setting is incorporated in the probabilistic rule learner ProbFOIL+, which combines principles of the rule learner FOIL with ProbLog ..."
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We study the problem of inducing logic programs in a probabilistic setting, in which both the example descriptions and their classification can be probabilistic. The setting is incorporated in the probabilistic rule learner ProbFOIL+, which combines principles of the rule learner FOIL with ProbLog, a probabilistic Prolog. We illustrate the approach by applying it to the knowledge base of NELL, the NeverEnding Language Learner.
Probabilistic Ontologies in Datalog+/
"... Abstract. In logic programming the distribution semantics is one of the most popular approaches for dealing with uncertain information. In this paper we apply the distribution semantics to the Datalog+/ language that is grounded in logic programming and allows tractable ontology querying. In the re ..."
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Abstract. In logic programming the distribution semantics is one of the most popular approaches for dealing with uncertain information. In this paper we apply the distribution semantics to the Datalog+/ language that is grounded in logic programming and allows tractable ontology querying. In the resulting semantics, called DISPONTE, formulas of a probabilistic ontology can be annotated with an epistemic or a statistical probability. The epistemic probability represents a degree of confidence in the formula, while the statistical probability considers the populations to which the formula is applied. The probability of a query is defined in terms of finite set of finite explanations for the query. We also compare the DISPONTE approach for Datalog+/ ontologies with that of Probabilistic Datalog+/ where an ontology is composed of a Datalog+/theory whose formulas are associated to an assignment of values for the random variables of a companion Markov Logic Network. 1
A Description Logics Tableau Reasoner in Prolog
"... Abstract. Description Logics (DLs) are gaining a widespread adoption as the popularity of the Semantic Web increases. Traditionally, reasoning algorithms for DLs have been implemented in procedural languages such as Java or C++. In this paper, we present the system TRILL for “Tableau Reasoner for de ..."
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Abstract. Description Logics (DLs) are gaining a widespread adoption as the popularity of the Semantic Web increases. Traditionally, reasoning algorithms for DLs have been implemented in procedural languages such as Java or C++. In this paper, we present the system TRILL for “Tableau Reasoner for descrIption Logics in proLog”. TRILL answers queries to SHOIN (D) knowledge bases using a tableau algorithm. Prolog nondeterminism is used for easily handling nondeterministic expansion rules that produce more than one tableau. Moreover, given a query, TRILL is able to return instantiated explanations for the query, i.e., instantiated minimal sets of axioms that allow the entailment of the query. The Thea2 library is exploited by TRILL for parsing ontologies and for the internal Prolog representation of DL axioms.
Tableau Reasoners for Probabilistic Ontologies Exploiting Logic Programming Techniques
"... Abstract. The adoption of Description Logics for modeling real world domains within the Semantic Web is exponentially increased in the last years, also due to the availability of a large number of reasoning algorithms. Most of them exploit the tableau algorithm which has to manage nondeterminism, ..."
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Abstract. The adoption of Description Logics for modeling real world domains within the Semantic Web is exponentially increased in the last years, also due to the availability of a large number of reasoning algorithms. Most of them exploit the tableau algorithm which has to manage nondeterminism, a feature that is not easy to handle using procedural languages such as Java or C++. Reasoning on real world domains also requires the capability of managing probabilistic and uncertain information. We thus present TRILL, for "Tableau Reasoner for descrIption Logics in proLog" and TRILL P , for "TRILL powered by Pinpointing formulas", which implement the tableau algorithm and return the probability of queries. TRILL P , instead of the set of explanations for a query, computes a Boolean formula representing them, speeding up the computation.
LPADbased fall risk assessment
"... Abstract. About 30 % of persons over 65 are subject to at least one fall during a year. A number of published studies identify statistical relations between risk factors and the probability of fall in terms of odds ratios. In this paper we present a tool based on the representation of risk factors a ..."
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Abstract. About 30 % of persons over 65 are subject to at least one fall during a year. A number of published studies identify statistical relations between risk factors and the probability of fall in terms of odds ratios. In this paper we present a tool based on the representation of risk factors as odds ratios. Such representation is exploited to automatically build a computational logic probabilistic program, that in turn computes the fall risk of the subject given the presence/absence of risk factors in his/her status.
Parameter and Structure Learning Algorithms for Statistical Relational Learning 5
"... My research activity focuses on the field of Machine Learning. Two key challenges in most machine learning applications are uncertainty and complexity. The standard framework for handling uncertainty is probability, for complexity is firstorder logic. Thus we would like to be able to learn and perf ..."
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My research activity focuses on the field of Machine Learning. Two key challenges in most machine learning applications are uncertainty and complexity. The standard framework for handling uncertainty is probability, for complexity is firstorder logic. Thus we would like to be able to learn and perform inference
77 Italian Machine Learning and Data Mining research: The last years
"... Abstract. With the increasing amount of information in electronic form the fields of Machine Learning and Data Mining continue to grow by providing new advances in theory, applications and systems. The aim of this paper is to consider some recent theoretical aspects and approaches to ML and DM with ..."
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Abstract. With the increasing amount of information in electronic form the fields of Machine Learning and Data Mining continue to grow by providing new advances in theory, applications and systems. The aim of this paper is to consider some recent theoretical aspects and approaches to ML and DM with an emphasis on the Italian research.