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534
Learning Stochastic Logic Programs
, 2000
"... Stochastic Logic Programs (SLPs) have been shown to be a generalisation of Hidden Markov Models (HMMs), stochastic contextfree grammars, and directed Bayes' nets. A stochastic logic program consists of a set of labelled clauses p:C where p is in the interval [0,1] and C is a firstorder r ..."
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Cited by 1181 (79 self)
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Stochastic Logic Programs (SLPs) have been shown to be a generalisation of Hidden Markov Models (HMMs), stochastic contextfree grammars, and directed Bayes' nets. A stochastic logic program consists of a set of labelled clauses p:C where p is in the interval [0,1] and C is a firstorder rangerestricted definite clause. This paper summarises the syntax, distributional semantics and proof techniques for SLPs and then discusses how a standard Inductive Logic Programming (ILP) system, Progol, has been modied to support learning of SLPs. The resulting system 1) nds an SLP with uniform probability labels on each definition and nearmaximal Bayes posterior probability and then 2) alters the probability labels to further increase the posterior probability. Stage 1) is implemented within CProgol4.5, which differs from previous versions of Progol by allowing userdefined evaluation functions written in Prolog. It is shown that maximising the Bayesian posterior function involves nding SLPs with short derivations of the examples. Search pruning with the Bayesian evaluation function is carried out in the same way as in previous versions of CProgol. The system is demonstrated with worked examples involving the learning of probability distributions over sequences as well as the learning of simple forms of uncertain knowledge.
BottomUp Relational Learning of Pattern Matching Rules for Information Extraction
, 2003
"... Information extraction is a form of shallow text processing that locates a specified set of relevant items in a naturallanguage document. Systems for this task require significant domainspecific knowledge and are timeconsuming and difficult to build by hand, making them a good application for ..."
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Cited by 400 (20 self)
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Information extraction is a form of shallow text processing that locates a specified set of relevant items in a naturallanguage document. Systems for this task require significant domainspecific knowledge and are timeconsuming and difficult to build by hand, making them a good application for machine learning. We present an algorithm, RAPIER, that uses pairs of sample documents and filled templates to induce patternmatch rules that directly extract fillers for the slots in the template. RAPIER is a bottomup learning algorithm that incorporates techniques from several inductive logic programming systems. We have implemented the algorithm in a system that allows patterns to have constraints on the words, partofspeech tags, and semantic classes present in the filler and the surrounding text. We present encouraging experimental results on two domains.
Clausal Discovery
, 1997
"... The clausal discovery engine Claudien is presented. Claudien is an inductive logic programming engine that fits in the descriptive data mining paradigm. Claudien addresses characteristic induction from interpretations, a task which is related to existing formalisations of induction in logic. In ch ..."
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Cited by 197 (34 self)
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The clausal discovery engine Claudien is presented. Claudien is an inductive logic programming engine that fits in the descriptive data mining paradigm. Claudien addresses characteristic induction from interpretations, a task which is related to existing formalisations of induction in logic. In characteristic induction from interpretations, the regularities are represented by clausal theories, and the data using Herbrand interpretations. Because Claudien uses clausal logic to represent hypotheses, the regularities induced typically involve multiple relations or predicates. Claudien also employs a novel declarative bias mechanism to define the set of clauses that may appear in a hypothesis.
Topdown induction of clustering trees
 In 15th Int’l Conf. on Machine Learning
, 1998
"... An approach to clustering is presented that adapts the basic topdown induction of decision trees method towards clustering. To this aim, it employs the principles of instance based learning. The resulting methodology is implemented in the TIC (Top down Induction of Clustering trees) system for firs ..."
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Cited by 153 (29 self)
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An approach to clustering is presented that adapts the basic topdown induction of decision trees method towards clustering. To this aim, it employs the principles of instance based learning. The resulting methodology is implemented in the TIC (Top down Induction of Clustering trees) system for first order clustering. The TIC system employs the first order logical decision tree representation of the inductive logic programming system Tilde. Various experiments with TIC are presented, in both propositional and relational domains. 1
Unification: A multidisciplinary survey
 ACM Computing Surveys
, 1989
"... The unification problem and several variants are presented. Various algorithms and data structures are discussed. Research on unification arising in several areas of computer science is surveyed, these areas include theorem proving, logic programming, and natural language processing. Sections of the ..."
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Cited by 124 (1 self)
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The unification problem and several variants are presented. Various algorithms and data structures are discussed. Research on unification arising in several areas of computer science is surveyed, these areas include theorem proving, logic programming, and natural language processing. Sections of the paper include examples that highlight particular uses
Computing Least Common Subsumers in Description Logics
 PROCEEDINGS OF THE 10TH NATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE
, 1992
"... Description logics are a popular formalism for knowledge representation and reasoning. This paper introduces a new operation for description logics: computing the "least common subsumer" of a pair of descriptions. This operation computes the largest set of commonalities between two descrip ..."
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Cited by 107 (14 self)
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Description logics are a popular formalism for knowledge representation and reasoning. This paper introduces a new operation for description logics: computing the "least common subsumer" of a pair of descriptions. This operation computes the largest set of commonalities between two descriptions. After arguing for the usefulness of this operation, we analyze it by relating computation of the least common subsumer to the wellunderstood problem of testing subsumption; a close connection is shown in the restricted case of "structural subsumption". We also present a method for computing the least common subsumer of "attribute chain equalities", and analyze the tractability of computing the least common subsumer of a set of descriptionsan important operation in inductive learning.
Mining Association Rules in Multiple Relations
 In Proceedings of the 7th International Workshop on Inductive Logic Programming
, 1997
"... . The application of algorithms for efficiently generating association rules is so far restricted to cases where information is put together in a single relation. We describe how this restriction can be overcome through the combination of the available algorithms with standard techniques from the fi ..."
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Cited by 102 (8 self)
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. The application of algorithms for efficiently generating association rules is so far restricted to cases where information is put together in a single relation. We describe how this restriction can be overcome through the combination of the available algorithms with standard techniques from the field of inductive logic programming. We present the system Warmr, which extends Apriori [2] to mine association rules in multiple relations. We apply Warmr to the natural language processing task of mining partofspeech tagging rules in a large corpus of English. Keywords: association rules, inductive logic programming 1 Introduction Association rules are generally recognized as a highly valuable type of regularities and various algorithms have been presented for efficiently mining them in large databases (cf. [1, 7, 2]). To the best of our knowledge, the application of these algorithms is so far restricted to cases where information is put together in a single relation. We describe how th...
Controlling the Complexity of Learning in Logic through Syntactic and TaskOriented Models
 INDUCTIVE LOGIC PROGRAMMING
, 1992
"... Due to the inadequacy of attributeonly representations for many learning problems, there is now a renewed interest in algorithms employing firstorder logic or restricted variants thereof as their knowledge representation. In this paper, we give a brief overview of the dimensions along which the ..."
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Cited by 100 (7 self)
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Due to the inadequacy of attributeonly representations for many learning problems, there is now a renewed interest in algorithms employing firstorder logic or restricted variants thereof as their knowledge representation. In this paper, we give a brief overview of the dimensions along which the complexity of learning in such representations can be controlled. We then present RDT, a modelbased learning algorithm for functionfree Horn clauses with negation that introduces two new means of complexity control, namely the use of syntactic rule models, and the use of a taskoriented domain topology. We briefly describe some preliminary application results of RDT within the knowledge acquisition system MOBAL, and present directions of further research.
Inductive Constraint Logic
, 1995
"... . A novel approach to learning first order logic formulae from positive and negative examples is presented. Whereas present inductive logic programming systems employ examples as true and false ground facts (or clauses), we view examples as interpretations which are true or false for the target theo ..."
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Cited by 94 (19 self)
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. A novel approach to learning first order logic formulae from positive and negative examples is presented. Whereas present inductive logic programming systems employ examples as true and false ground facts (or clauses), we view examples as interpretations which are true or false for the target theory. This viewpoint allows to reconcile the inductive logic programming paradigm with classical attribute value learning in the sense that the latter is a special case of the former. Because of this property, we are able to adapt AQ and CN2 type algorithms in order to enable learning of full first order formulae. However, whereas classical learning techniques have concentrated on concept representations in disjunctive normal form, we will use a clausal representation, which corresponds to a conjuctive normal form where each conjunct forms a constraint on positive examples. This representation duality reverses also the role of positive and negative examples, both in the heuristics and in the a...
Statistical relational learning for link prediction
 In Proceedings of the Workshop on Learning Statistical Models from Relational Data at IJCAI2003
, 2003
"... Link prediction is a complex, inherently relational, task. Be it in the domain of scientific citations, social networks or hypertext links, the underlying data are extremely noisy and the characteristics useful for prediction are not readily available in a “flat ” file format, but rather involve com ..."
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Cited by 87 (6 self)
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Link prediction is a complex, inherently relational, task. Be it in the domain of scientific citations, social networks or hypertext links, the underlying data are extremely noisy and the characteristics useful for prediction are not readily available in a “flat ” file format, but rather involve complex relationships among objects. In this paper, we propose the application of our methodology for Statistical Relational Learning to building link prediction models. We propose an integrated approach to building regression models from data stored in relational databases in which potential predictors are generated by structured search of the space of queries to the database, and then tested for inclusion in a logistic regression. We present experimental results for the task of predicting citations made in scientific literature using relational data taken from CiteSeer. This data includes the citation graph, authorship and publication venues of papers, as well as their word content. 1