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Relational learning vs. propositionalization - Investigations in inductive logic programming and propositional machine learning

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AI COMMUNICATIONS 2000年第4期13卷 275-276页

作者： Kramer, S Austrian Res Inst Artificial Intelligence A-1010 Vienna Austria

An attempt is made to determine how machine learning benefit from using a richer and more expressive representation. The approach taken is to compare the performance of a so-called upgrade of a propositional algorithm and the performance of a propositional learning algorithm applied to so-called propositionalization. This comparison is performed in learning domains that are essentially relational.

关键词： Machine Learning relational learning inductive logic programming propositional Machine Learning propositionalization

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A discriminative method for family-based protein remote homology detection that combines inductive logic programming and propositional models

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BMC BIOINFORMATICS 2011年第1期12卷 83-83页

作者： Bernardes, Juliana S. Carbone, Alessandra Zaverucha, Gerson Univ Fed Rio de Janeiro COPPE Programa Engn Sistemas & Comp BR-21945 Rio De Janeiro Brazil Univ Paris 06 UMR Genom Analyt 7238 F-75006 Paris France CNRS Lab Genom Microorganismes UMR7238 F-75006 Paris France

Background: Remote homology detection is a hard computational problem. Most approaches have trained computational models by using either full protein sequences or multiple sequence alignments (MSA), including all positions. However, when we deal with proteins in the "twilight zone" we can observe that only some segments of sequences (motifs) are conserved. We introduce a novel logical representation that allows us to represent physicochemical properties of sequences, conserved amino acid positions and conserved physico-chemical positions in the MSA. From this, inductive logic programming (ILP) finds the most frequent patterns (motifs) and uses them to train propositional models, such as decision trees and support vector machines (SVM). Results: We use the SCOP database to perform our experiments by evaluating protein recognition within the same superfamily. Our results show that our methodology when using SVM performs significantly better than some of the state of the art methods, and comparable to other. However, our method provides a comprehensible set of logical rules that can help to understand what determines a protein function. Conclusions: The strategy of selecting only the most frequent patterns is effective for the remote homology detection. This is possible through a suitable first-order logical representation of homologous properties, and through a set of frequent patterns, found by an ILP system, that summarizes essential features of protein functions.

关键词： Support Vector Machine Frequent Pattern logical Representation inductive logic programming Position Specific Score Matrix

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Relation Extraction from Texts with Symbolic Rules Induced by inductive logic programming

Relation Extraction from Texts with Symbolic Rules Induced b...

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International Conference on Tools with Artificial Intelligence

作者： Rinaldo Lima Fred Freitas Bernard Espinasse Informatics Center Federal University of Pernambuco Recife Brazil LSIS Aix Marseille University Marseille France

ISBN: (纸本)9781509001644

Relation Extraction (RE) is the task of detecting semantic relations between entities in text. Most of the state-of-the-art RE systems rely on statistical machine learning techniques which usually employ an attribute-value representation of features. Contrarily to this trend, we focus on an alternative approach to RE based on the automatic induction of symbolic extraction rules. We present OntoILPER, an RE system based on inductive logic programming which uses a domain ontology in its extraction process. Several experiments are discussed in this paper over the reACE 2004/2005 reference corpora. The results are encouraging and seem to demonstrate the effectiveness of the proposed solution.

关键词： Relation Extraction Ontology-based Information Extraction Ontology Population inductive logic programming

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A Proposal for an Abstract Model Building Using inductive logic programming

A Proposal for an Abstract Model Building Using Inductive Lo...

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International Conference on Computational Aspects of Social Networks(国际社会网络计算会议 CASoN 2010)

作者： Zhi Li Xueli Yu Zengrong Liu Kun Hu College of Computer and Software in Taiyuan University of Technology 79 Yingze West Street Taiyuan City Shanxi Province PRC Zip Code: 030024

Various ways of abstraction in reinforcement learning methods have been proposed. The central idea is to make use of the inherent structure in the MDP itself. Most traditional techniques do not scale up to even larger domains consisting of objects and relations. We present a proposal for abstract model building to construct relational Markov decision process. This approach separates the structural induction of the representation from the actual value function estimation. First a set of first-order features is induced utilizing inductive logic programming. These are then used as input for a regression algorithm that estimates Q-value functions per action in the induced states and determine a policy. In this way we hope to improve performance of standard Q-learaing.

关键词： inductive logic programming Preimage Q-Iearning Reinforcement learning

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Extracting Knowledge from a Mutation Database Related to Human Monogenic Disease Using inductive logic programming

Extracting Knowledge from a Mutation Database Related to Hum...

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International Conference on Bioscience, Biochemistry and Bioinformatics（ICBBB 2011）

作者： Tien-Dao Luu Ngoc-Hoan Nguyen Anne Friedrich Jean Muller Olivier Poch Institute of Genetics and Molecular and Cellular Biology IllkirchFrance Department of Genetics Genomics and MicrobiologyUniversity of StrasbourgStrasbourgFrance Genetic Diagnostics Laboratory CHU Strasbourg Nouvel Hpital CivilStrasbourgFrance

Understanding the effects of genetic variation on the phenotype of an individual is a major goal of biomedical research,especially for the development of diagnostics and effective therapeutic *** this work,we propose a methodology using inductive logic programming(ILP) to automatically extract knowledge about deleterious/neutral mutations from a multi-relational database,named *** used 8117 mutations in 805 proteins with known three-dimensional structure in our *** using ILP for learning,we obtained classification rules that can be interpreted by a human expert and that help to improve our understanding of the relationships between physico-chemical and evolutionary features and deleterious *** experimental results,compared with state-of-the-art methods,show that the proposed approach can be applied to predict the impact of single amino acid replacement on the function of a *** rules and the estimated effect of human non-synonymous polymorphisms on the function of a protein are available at http://***/sm2ph/***.

关键词： inductive logic programming knowledge discovery and data mining single nucleotide polymorphisms genotype-phenotype relationship SM2PH-db

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Machine Translation Model using inductive logic programming

Machine Translation Model using Inductive Logic Programming

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International Conference on Natural Language Processing and Knowledge Engineering(IEEE自然语言处理与知识工程国际会议 IEEE NLP-KE 2009)

作者： Aly Fahmy Ahmad Hossny Khaled Shaalan Faculty of Computers and Information Cairo University Cairo Egypt Faculty of Informatics British University in Dubai Dubai UAE

Rule based machine translation systems face different challenges in building the translation model in a form of transfer rules. Some of these problems require enormous human effort to state rules and their consistency. This is where different human linguists make different rules for the same sentence. A human linguist states rules to be understood by human rather than machines. The proposed translation model (from Arabic to English) tackles the mentioned problem of building translation model. This model employs inductive logic programming (ILP) to learn the language model from a set of example pairs acquired from parallel corpora and represent the language model in a rule-based format that maps Arabic sentence pattern to English sentence pattern. By testing the model on a small set of data, it generated translation rules with logarithmic growing rate and with word error rate 11%

关键词： Machine translation inductive logic programming rule based machine translation ezample based machine translation rule Induction Arabic to English

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Efficient program synthesis using constraint satisfaction in inductive logic programming

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2013年第1期14卷

作者： Kevin Murphy Bernhard Schölkopf John Ahlgren Shiu Yin Yuen Google MPI for Intelligent Systems Department of Electronic Engineering City University of Hong Kong Hong Kong China

We present NrSample, a framework for program synthesis in inductive logic programming. NrSample uses propositional logic constraints to exclude undesirable candidates from the search. This is achieved by representing constraints as propositional formulae and solving the associated constraint satisfaction problem. We present a variety of such constraints: pruning, input-output, functional (arithmetic), and variable splitting. NrSample is also capable of detecting search space exhaustion, leading to further speedups in clause induction and optimality. We benchmark NrSample against enumeration search (Aleph's default) and Progol's A* search in the context of program synthesis. The results show that, on large program synthesis problems, NrSample induces between 1 and 1358 times faster than enumeration (236 times faster on average), always with similar or better accuracy. Compared to Progol A*, NrSample is 18 times faster on average with similar or better accuracy except for two problems: one in which Progol A* substantially sacrificed accuracy to induce faster, and one in which Progol A* was a clear winner. Functional constraints provide a speedup of up to 53 times (21 times on average) with similar or better accuracy. We also benchmark using a few concept learning (non-program synthesis) problems. The results indicate that without strong constraints, the overhead of solving constraints is not compensated for.

关键词： Boolean satisfiability problem constraint satisfaction inductive logic programming program synthesis theory induction

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inductive equivalence in clausal logic and nonmonotonic logic programming

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MACHINE LEARNING 2011年第1期83卷 1-29页

作者： Sakama, Chiaki Inoue, Katsumi Wakayama Univ Dept Comp & Commun Sci Wakayama 6408510 Japan Natl Inst Informat Chiyoda Ku Tokyo 1018430 Japan

This paper provides a logical framework for comparing inductive capabilities among agents having different background theories. A background theory is called inductively equivalent to another background theory if the two theories induce the same hypotheses for any observation. Conditions of inductive equivalence change depending on the logic of representation languages and the logic of induction or inductive logic programming (ILP). In this paper, we consider clausal logic and nonmonotonic logic programs as representation languages for background theories. Then we investigate conditions of inductive equivalence in four different frameworks of induction, cautious induction , brave induction , learning from satisfiability , and descriptive induction . We observe that several induction algorithms in Horn ILP systems require weaker conditions of equivalence under restricted problem settings. We address that inductive equivalence can be used for verification and evaluation of induction algorithms, and argue problems for optimizing background theories in ILP.

关键词： inductive equivalence inductive logic programming Nonmonotonic logic programs

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Object Identity Reloaded-A Comprehensive Reference for an Efficient and Effective Framework for logic-Based Machine Learning

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ELECTRONICS 2025年第8期14卷 1523-1523页

作者： Ferilli, Stefano Univ Bari Dept Comp Sci Via E Orabona 4 I-70125 Bari Italy

Sub-symbolic Machine Learning (ML) techniques, and specifically Neural Network-based ones, recently took over the research landscape, thanks to their efficiency and impressive effectiveness. On the other hand, the recent debate on ethics and AI and the first regulations on AI are progressively calling for anthropocentricity, which in turn requires explicit, human-understandable, and explainable approaches and representations that allow humans to be active parts in the loop. In these cases, logic-based approaches are more suitable. The inductive logic programming (ILP) branch of research in ML provides an anwer to this need and a uniform and unifying framework for three relevant industrial and research concerns: management of databases, implementation of software systems, and modeling of human-like reasoning strategies. A particular ILP framework based on the Object Identity (OI) assumption was proposed in the 1990s, for which desirable theoretical and pratical properties were demonstrated and working tools and systems that successfully approached real-world and classical problems in AI were developed. In an age when mainstream research and media seem to reduce AI and ML to just deep learning, this paper celebrates the 30th anniversary of OI by providing for the first time a comprehensive overview of the framework to be used as a reference for researchers still interested in investigating the ILP approach to ML.

关键词： machine learing inductive logic programming incrementality object identity

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inductive learning of robot task knowledge from raw data and online expert feedback

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MACHINE LEARNING 2025年第4期114卷 1-33页

作者： Meli, Daniele Fiorini, Paolo Univ Verona Dept Comp Sci Verona Italy Univ Verona Dept Engn Innovat Med Str Le Grazie 15 I-37135 Verona Italy

The increasing level of autonomy of robots poses challenges of trust and social acceptance, especially in human-robot interaction scenarios. This requires an interpretable implementation of robotic cognitive capabilities, possibly based on formal methods as logics for the definition of task specifications. However, prior knowledge is often unavailable in complex realistic scenarios. In this paper, we propose an offline algorithm based on inductive logic programming from noisy examples to extract task specifications (i.e., action preconditions, constraints and effects) directly from raw data of few heterogeneous (i.e., not repetitive) robotic executions. Our algorithm leverages on the output of any unsupervised action identification algorithm from video-kinematic recordings. Combining it with the definition of very basic, almost task-agnostic, commonsense concepts about the environment, which contribute to the interpretability of our methodology, we are able to learn logical axioms encoding preconditions of actions, as well as their effects in the event calculus paradigm. Since the quality of learned specifications depends mainly on the accuracy of the action identification algorithm, we also propose an online framework for incremental refinement of task knowledge from user's feedback, guaranteeing safe execution. Results in a standard manipulation task and benchmark for user training in the safety-critical surgical robotic scenario, show the robustness, data- and time-efficiency of our methodology, with promising results towards the scalability in more complex domains.

关键词： inductive logic programming Answer set programming Learning under uncertainty Online learning Human-robot interaction Explainable AI

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