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17th International Conference of the Italian-Association-for-Artificial-Intelligence (AI*IA)

作者： Pazienza, Andrea Ferilli, Stefano Univ Bari Dipartimento Informat Bari Italy

ISBN: (纸本)9783030038403;9783030038397

Noisy (uncertain, missing, or inconsistent) information, typical of many real-world domains, may dramatically affect the performance of logic-based Machine Learning. Multistrategy Learning approaches have been tried to solve this problem by coupling inductive logic programming with other kinds of inference. While uncertainty has been tackled using probabilistic approaches, and abduction has been used to deal with missing data, inconsistency is still an open problem. In the Multistrategy Learning perspective, this paper proposes to attack this latter kind of noise using (abstract) Argumentation, an inferential strategy aimed at handling conflicting information. More specifically, it defines a pre-processing operator based on abstract argumentation that can detect and remove noisy atoms from the observations before running the learning system on the polished data. Quantitative and qualitative experiments point out some strengths and weaknesses of the proposed approach, and suggest lines for future research on this topic.

关键词： inductive logic programming Conflicting information Abstract argumentation Multistrategy Learning

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Pairwise saturations in inductive logic programming

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ARTIFICIAL INTELLIGENCE REVIEW 2017年第3期47卷 395-415页

作者： Drole, Miha Kononenko, Igor Univ Ljubljana Fac Comp & Informat Sci Vecna Pot 113 Ljubljana 1001 Slovenia

One of the main issues when using inductive logic programming (ILP) in practice remain the long running times that are needed by ILP systems to induce the hypothesis. We explore the possibility of reducing the induction running times of systems that use asymmetric relative minimal generalisation (ARMG) by analysing the bottom clauses of examples that serve as inputs into the generalisation operator. Using the fact that the ARMG covers all of the examples and that it is a subset of the variabilization of one of the examples, we identify literals that cannot appear in the ARMG and remove them prior to computing the generalisation. We apply this procedure to the ProGolem ILP system and test its performance on several real world data sets. The experimental results show an average speedup of compared to the base ProGolem system and compared to ProGolem extended with caching, both without a decrease in the accuracy of the produced hypotheses. We also observe that the gain from using the proposed method varies greatly, depending on the structure of the data set.

关键词： inductive logic programming Bottom-up Saturation Machine learning

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Improving scalability of inductive logic programming via pruning and best-effort optimisation

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EXPERT SYSTEMS WITH APPLICATIONS 2017年 87卷 291-303页

作者： Kazmi, Mishal Schuller, Peter Saygin, Yucel Sabanci Univ Fac Engn & Nat Sci Istanbul Turkey Marmara Univ Fac Engn Istanbul Turkey

inductive logic programming (ILP) combines rule-based and statistical artificial intelligence methods, by learning a hypothesis comprising.a set of rules given background knowledge and constraints for the search space. We focus on extending the XHAIL algorithm for ILP which is based on Answer Set programming and we evaluate our extensions using the Natural Language Processing application of sentence chunking. With respect to processing natural language, ILP can cater for the constant change in how we use language on a daily basis. At the same time, ILP does not require huge amounts of training examples such as other statistical methods and produces interpretable results, that means a set of rules, which can be analysed and tweaked if necessary. As contributions we extend XHAIL with (i) a pruning mechanism within the hypothesis generalisation algorithm which enables learning from larger datasets, (ii) a better usage of modern solver technology using recently developed optimisation methods, and (iii) a time budget that permits the usage of suboptimal results. We evaluate these improvements on the task of sentence chunking using three datasets from a recent SemEval competition. Results show that our improvements allow for learning on bigger datasets with results that are of similar quality to state-of-the-art Systems on the same task. Moreover, we compare the hypotheses obtained on datasets to gain insights on the structure of each dataset. (c) 2017 Elsevier Ltd. All rights reserved.

关键词： Answer Set programming inductive logic programming Natural Language Processing Chunking

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Predicate invention-based specialization in inductive logic programming

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JOURNAL OF INTELLIGENT INFORMATION SYSTEMS 2016年第1期47卷 33-55页

作者： Ferilli, Stefano Univ Bari Dipartimento Informat Bari BA Italy Univ Bari Ctr Interdipartimentale Log & Sue Applicaz Bari BA Italy

Three relevant areas of interest in symbolic Machine Learning are incremental supervised learning, multistrategy learning and predicate invention. In many real-world tasks, new observations may point out the inadequacy of the learned model. In such a case, incremental approaches allow to adjust it, instead of learning a new model from scratch. Specifically, when a negative example is wrongly classified by a model, specialization refinement operators are needed. A powerful way to specialize a theory in inductive logic programming is adding negated preconditions to concept definitions. This paper describes an empowered specialization operator that allows to introduce the negation of conjunctions of preconditions using predicate invention. An implementation of the operator is proposed, and experiments purposely devised to stress it prove that the proposed approach is correct and viable even under quite complex conditions.

关键词： Machine Learning inductive logic programming Predicate invention

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Extracting Time-oriented Relationships of Nutrients to Losing Body Fat Mass Using inductive logic programming 15

Extracting Time-oriented Relationships of Nutrients to Losin...

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15th IEEE International Conference on Cognitive Informatics and Cognitive Computing (ICCI*CC)

作者： Ushikubo, Sho Kanamori, Katsutoshi Ohwada, Hayato Tokyo Univ Sci Fac Sci & Tech Noda Chiba Japan

ISBN: (纸本)9781509038466

This study was performed to extract rules for reducing body fat mass so as to prevent lifestyle-related diseases. Lifestyle-related diseases have been increasing in Japan, even among younger people. Body fat mass is related to lifestyle-related diseases. Hence, finding rules for reducing body fat mass is very meaningful. We obtained lifestyle time-series data on five male subjects who are in their 20s and not obese. The data includes the amount of body fat mass of each subject and a variety of features such as sleep, exercise, and nutrient intake. We used inductive logic programming (ILP) to apply this data because ILP can more flexibly learn rules than other machine-learning methods. As a result of applying the data to ILP, our ILP system successfully extracted rules of time-oriented relationships of nutrients to decrease body fat mass based on limited data. Intake of various nutrients one day and two days prior was effective in reducing body fat mass. Moreover, we determined that nutrients related to losing body fat mass include vitamin B2, pantothenic acid, fat, vitamin B1, and biotin. © 2016 IEEE.

关键词： Body fat mass inductive logic programming Nutrients Time-series data

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Relation Extraction from Texts with Symbolic Rules Induced by inductive logic programming 27

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

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27th IEEE International Conference on Tools with Artificial Intelligence (ICTAI)

作者： Lima, Rinaldo Freitas, Fred Espinasse, Bernard Univ Fed Pernambuco Informat Ctr Recife PE Brazil Aix Marseille Univ LSIS Marseille France

ISBN: (纸本)9781509001637

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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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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Techniques to enhance efficiency and effectiveness of inductive logic programming systems: the TWEETY approaches

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JOURNAL OF EXPERIMENTAL & THEORETICAL ARTIFICIAL INTELLIGENCE 2014年第1期26卷 51-107页

作者： Liu, Chongbing Pontelli, Enrico New Mexico State Univ Dept Comp Sci Las Cruces NM 88003 USA

This paper presents four novel approaches to enhance efficiency and effectiveness of inductive logic programming (ILP) systems, along with their implementation in a new ILP system, called TWEETY. The proposed approaches include (1) a new declaration mechanism, called connection declarations, for bottom clause construction, which is simpler but more expressive than the commonly used mode declarations;(2) a new covering technique, called super_covering, which reduces the examples in such a way that recursion can be learned, independently from the ordering of the examples;(3) a new search heuristics, called neg_coverage heuristics, which guides the search using only the number of negative examples covered by each hypothesis and (4) a new search algorithm, called doubly_guided_search, which searches for best clauses by alternating the use of two search heuristics, i.e. the traditional coverage search heuristics and the new neg_coverage search heuristics. The TWEETY system is shown to be more effective and efficient than the state-of-the-art ILP system ALEPH;the proposed techniques can be used to enhance efficiency and effectiveness of ALEPH and other systems based on the same ILP principles.

关键词： logic programming inductive logic programming machine learning efficiency

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Knowledge discovery for pancreatic cancer using inductive logic programming

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IET SYSTEMS BIOLOGY 2014年第4期8卷 162-168页

作者： Qiu, Yushan Shimada, Kazuaki Hiraoka, Nobuyoshi Maeshiro, Kensei Ching, Wai-Ki Aoki-Kinoshita, Kiyoko F. Furuta, Koh Univ Hong Kong Dept Math Adv Modeling & Appl Comp Lab Hong Kong Hong Kong Peoples R China Natl Canc Ctr Hepatobiliary & Pancreat Surg Div Tokyo Japan Natl Canc Ctr Res Inst Div Pathol Tokyo 104 Japan Fukuoka Univ Dept Surg Fukuoka 81401 Japan Soka Univ Fac Engn Dept Bioinformat Tokyo Japan Natl Canc Ctr Div Clin Labs Tokyo Japan

Pancreatic cancer is a devastating disease and predicting the status of the patients becomes an important and urgent issue. The authors explore the applicability of inductive logic programming (ILP) method in the disease and show that the accumulated clinical laboratory data can be used to predict disease characteristics, and this will contribute to the selection of therapeutic modalities of pancreatic cancer. The availability of a large amount of clinical laboratory data provides clues to aid in the knowledge discovery of diseases. In predicting the differentiation of tumour and the status of lymph node metastasis in pancreatic cancer, using the ILP model, three rules are developed that are consistent with descriptions in the literature. The rules that are identified are useful to detect the differentiation of tumour and the status of lymph node metastasis in pancreatic cancer and therefore contributed significantly to the decision of therapeutic strategies. In addition, the proposed method is compared with the other typical classification techniques and the results further confirm the superiority and merit of the proposed method.

关键词： inductive logic programming Metastatic Malignant Neoplasm to the Lymph Node data mining clinical laboratory maladjustment Cancer of Pancreas Tumours Pancreatic Neoplasms Status

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Knowledge Acquisition in Model Driven Development Transformations: An inductive logic programming Approach

Knowledge Acquisition in Model Driven Development Transforma...

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IEEE Region 10 Conference (TENCON)

作者： Al-Jamimi, Hamdi A. Ahmed, Moataz A. King Fahd Univ Petr & Minerals Informat & Comp Sci Dept Dhahran Saudi Arabia

ISBN: (纸本)9781479940752

Model transformation by example is a novel trend in model-driven software engineering. The rationale behind this is to utilize existing knowledge represented by source and target models of previously developed systems;such as requirements analysis and software design models, respectively. Such knowledge can be utilized to derive transformation rules to be applied in future system developments. To achieve this goal, machine learning techniques can assist in discovering and formalizing desired transformation rules. inductive logic programming (ILP) represents a highly applicable machine learning technique in this context. Given a set of examples and background knowledge encoded as a set of first-order logic descriptions, an ILP system attempts to derive rules describing different transformation steps in a purely declarative way. The induced rules follow the same logical description as the given examples and background knowledge. The objective of this work is to introduce initial setup of an ILP system that can be utilized to derive analysis-design transformation rules from a set of examples that represent pairs of analysis-design models.

关键词： Model Driven Development Requirement Analysis Software Design Model Transformation inductive logic programming

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