检索结果-内蒙古大学图书馆

logical scalability and efficiency of relational learning algorithms

VLDB JOURNAL 2019年第2期28卷 147-171页

作者： Picado, Jose Termehchy, Arash Fern, Alan Ataei, Parisa Oregon State Univ Sch EECS Corvallis OR 97331 USA

Relational learning algorithms learn the definition of a new relation in terms of existing relations in the database. The same database may be represented under different schemas for various reasons, such as efficiency, data quality, and usability. Unfortunately, the output of current relational learning algorithms tends to vary quite substantially over the choice of schema, both in terms of learning accuracy and efficiency. We introduce the property of schema independence of relational learning algorithms, and study both the theoretical and empirical dependence of existing algorithms on the common class of (de) composition schema transformations. We show theoretically and empirically that current relational learning algorithms are generally not schema independent. We propose Castor, a relational learning algorithm that achieves schema independence.

关键词： Relational learning Machine Learning Schema independence Database transformations inductive logic programming

来源：评论

学校读者我要写书评

暂无评论

A logic-based relational learning approach to relation extraction: The OntoILPER system

引用

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2019年 78卷 142-157页

作者： Lima, Rinaldo Espinasse, Bernard Freitas, Fred Univ Fed Rural Pernambuco Dept Comp Recife PE Brazil Univ Toulon & Var Aix Marseille Univ LIS UMR CNRS 7020 Toulon France Univ Fed Pernambuco Ctr Informat Recife PE Brazil

Relation Extraction (RE), the task of detecting and characterizing semantic relations between entities in text, has gained much importance in the last two decades, mainly in the biomedical domain. Many papers have been published on Relation Extraction using supervised machine learning techniques. Most of these techniques rely on statistical methods, such as feature-based and tree-kernels-based methods. Such statistical learning techniques are usually based on a propositional hypothesis space for representing examples, i.e., they employ an attribute-value representation of features. This kind of representation has some drawbacks, particularly in the extraction of complex relations which demand more contextual information about the involving instances, i.e., it is not able to effectively capture structural information from parse trees without loss of information. In this work, we present OntoILPER, a logic-based relational learning approach to Relation Extraction that uses inductive logic programming for generating extraction models in the form of symbolic extraction rules. OntoILPER takes profit of a rich relational representation of examples, which can alleviate the aforementioned drawbacks. The proposed relational approach seems to be more suitable for Relation Extraction than statistical ones for several reasons that we argue. Moreover, OntoILPER uses a domain ontology that guides the background knowledge generation process and is used for storing the extracted relation instances. The induced extraction rules were evaluated on three protein-protein interaction datasets from the biomedical domain. The performance of OntoILPER extraction models was compared with other state-of-the-art RE systems. The encouraging results seem to demonstrate the effectiveness of the proposed solution.

关键词： Relation extraction Rule induction Information extraction inductive logic programming Relational learning

来源：评论

学校读者我要写书评

暂无评论

Disjunctive Abduction

引用

NEW GENERATION COMPUTING 2019年第2期37卷 219-243页

作者： Inoue, Katsumi Sakama, Chiaki Natl Inst Informat Tokyo Japan Grad Univ Adv Studies SOKENDAI Dept Informat Tokyo Japan Tokyo Inst Technol Sch Comp Dept Comp Sci Tokyo Japan Wakayama Univ Dept Comp & Commun Sci Wakayama Japan

Given an observation or a goal, abduction infers candidate hypotheses to explain the observation or to achieve the goal. In this paper, we consider disjunctive abduction, in which disjunctions play important roles in abduction. Machine-oriented intelligent systems can utilize disjunctive abduction in many ways. For example, consider the case in which multiple explanations exist for an observation. We may need to select the best explanation among them, but often it is difficult to choose only one given insufficient information. In that case, we can leave the decision indefinite by considering a disjunctive explanation, which is a disjunction of possible alternative explanations. Disjunctive explanations can offer a consistent way to theory changes when they are incorporated or assimilated into the current knowledge base. The assimilated knowledge base preserves the intended semantics from the collection of all possible updated knowledge bases. A merit of disjunctive explanations is that we only need one current knowledge base at a time, still keeping every possible change in a single state. This method is extended to accomplish an update when there are multiple ways to remove old unnecessary hypotheses from the knowledge base. The proposed framework is well applicable to view updates in disjunctive databases. We also show that disjunctive abduction can be considered in inductive logic programming.

关键词： Abduction logic programming Knowledge assimilation inductive logic programming

来源：评论

学校读者我要写书评

暂无评论

Please delete that! Why should I?: Explaining learned irrelevance classifications of digital objects

引用

KUNSTLICHE INTELLIGENZ 2019年第1期33卷 35-44页

作者： Siebers, Michael Schmid, Ute Univ Bamberg Cognit Syst Bamberg Germany Friedrich Alexander Univ Erlangen Nurnberg Chair Work & Org Psychol Erlangen Germany

Dare2Del is an assistive system which facilitates intentional forgetting of irrelevant digital objects. For an assistive system to be helpful, the user has to trust the system's decisions. Explanations are a crucial component in establishing this trust. We will introduce different types of explanations which can vary along different dimensions such as level of detail and modality suitable for different application contexts. We will outline the cognitive companion system Dare2Del which is intended to support users managing digital objects in a working environment. Core of Dare2Del is an interpretable machine learning mechanism which induces decision rules to classify whether a digital objects is irrelevant. In this paper, we focus on irrelevance of files. We formalize the decision making process as logic inference. Finally, we present a method to generate verbal explanations for irrelevance decisions and point out how such explanations can be constructed on different levels of details. Furthermore, we show how verbal explanations can be related to the path context of the file. We conclude with a short discussion of the scope and restrictions of our approach.

关键词： Irrelevant digital objects Verbal explanations inductive logic programming

来源：评论

学校读者我要写书评

暂无评论

Automated identification of protein-ligand interaction features using inductive logic programming: a hexose binding case study

引用

BMC BIOINFORMATICS 2012年第1期13卷 1-11页

作者： Santos, Jose C. A. Nassif, Houssam Page, David Muggleton, Stephen H. Sternberg, Michael J. E. Univ London Imperial Coll Sci Technol & Med Dept Comp Sci Computat Bioinformat Lab London SW7 2BZ England Univ Wisconsin Madison Dept Biostat & Med Informat Dept Comp Sci Madison WI 53706 USA Univ London Imperial Coll Sci Technol & Med Ctr Bioinformat Dept Life Sci London SW7 2AZ England

Background: There is a need for automated methods to learn general features of the interactions of a ligand class with its diverse set of protein receptors. An appropriate machine learning approach is inductive logic programming (ILP), which automatically generates comprehensible rules in addition to prediction. The development of ILP systems which can learn rules of the complexity required for studies on protein structure remains a challenge. In this work we use a new ILP system, ProGolem, and demonstrate its performance on learning features of hexose-protein interactions. Results: The rules induced by ProGolem detect interactions mediated by aromatics and by planar-polar residues, in addition to less common features such as the aromatic sandwich. The rules also reveal a previously unreported dependency for residues CYS and LEU. They also specify interactions involving aromatic and hydrogen bonding residues. This paper shows that inductive logic programming implemented in ProGolem can derive rules giving structural features of protein/ligand interactions. Several of these rules are consistent with descriptions in the literature. Conclusions: In addition to confirming literature results, ProGolem's model has a 10-fold cross-validated predictive accuracy that is superior, at the 95% confidence level, to another ILP system previously used to study protein/hexose interactions and is comparable with state-of-the-art statistical learners.

关键词： Support Vector Machine Hexose Protein Data Bank inductive logic programming Hypothesis Space

来源：评论

学校读者我要写书评

暂无评论

Towards inductive learning of surgical task knowledge: a preliminary case study of the peg transfer task

引用

Procedia Computer Science 2020年 176卷 440-449页

作者： Daniele Meli Paolo Fiorini Mohan Sridharan Department of Computer Science University of Verona strada Le Grazie 15 Verona 37135 Italy School of Computer Science University of Birmingham Edgbaston Birmingham B152TT UK

Autonomy in robotic surgery will significantly improve the quality of interventions in terms of safety and recovery time for the patient, and reduce fatigue of surgeons and hospital costs. A key requirement for such autonomy is the ability of the surgical system to encode and reason with commonsense task knowledge, and to adapt to variations introduced by the surgical scenarios and the individual patients. However, it is difficult to encode all the variability in surgical scenarios and in the anatomy of individual patients a priori, and new knowledge often needs to be acquired and merged with the existing knowledge. At the same time, it is not possible to provide a large number of labeled training examples in the robotic surgery. This paper presents a framework based on inductive logic programming and answer set semantics for incrementally learning domain knowledge from a limited number of executions of basic surgical tasks. As an illustrative example, we focus on the peg transfer task, and learn state constraints and the preconditions of actions starting from different levels of prior knowledge. We do so using a small dataset comprising human and robotic executions with the da Vinci surgical robot in a challenging simulated scenario.

关键词： Incremental knowledge acquisition Surgical robotics inductive logic programming Answer set programming

来源：评论

学校读者我要写书评

暂无评论

Rethinking Static Analysis by Combining Discrete and Continuous Reasoning 26th

Rethinking Static Analysis by Combining Discrete and Continu...

引用

26th International Static Analysis Symposium (SAS)

作者： Naik, Mayur Univ Penn Dept Comp & Informat Sci 200 S 33Rd St Philadelphia PA 19104 USA

ISBN: (纸本)9783030323042;9783030323035

Static analyses predominantly use discrete modes of logical reasoning to derive facts about programs. Despite significant strides, this form of reasoning faces new challenges in modern software applications and practices. These challenges concern not only traditional analysis objectives such as scalability, accuracy, and soundness, but also emerging ones such as tailoring analysis conclusions based on relevance or severity of particular code changes, and needs of individual programmers. We advocate seamlessly extending static analyses to leverage continuous modes of logical reasoning in order to address these challenges. Central to our approach is expressing the specification of the static analysis in a constraint language that is amenable to computing provenance information. We use the logic programming language Datalog as proof-of-concept for this purpose. We illustrate the benefits of exploiting provenance even in the discrete setting. Moreover, by associating weights with constraints, we show how to amplify these benefits in the continuous setting. We also present open problems in aspects of analysis usability, language expressiveness, and solver techniques. The overall process constitutes a fundamental rethinking of how to design, implement, deploy, and adapt static analyses.

关键词： Static analysis Constraint solving Provenance Probabilistic logics Alarm ranking inductive logic programming

来源：评论

学校读者我要写书评

暂无评论

Discrete Stochastic Search and Its Application to Feature-Selection for Deep Relational Machines 28th

Discrete Stochastic Search and Its Application to Feature-Se...

引用

28th International Conference on Artificial Neural Networks (ICANN)

作者： Dash, Tirtharaj Srinivasan, Ashwin Joshi, Ramprasad S. Baskar, A. BITS Pilani Dept Comp Sci & Informat Syst KK Birla Goa Campus Pilani 403726 Goa India

ISBN: (纸本)9783030304843;9783030304836

We use a model for discrete stochastic search in which one or more objects ("targets") are to be found by a search over n locations ("boxes"), where n is infinitely large. Each box has some probability that it contains a target, resulting in a distribution H over boxes. We model the search for the targets as a stochastic procedure that draws boxes using some distribution S. We derive first a general expression on the expected number of misses E[Z] made by the search procedure in terms of H and S. We then obtain an expression for an optimal distribution S* to minimise E[Z]. This results in a relation between: the entropy of H and the KL-divergence between H and S*. This result induces a 2-partitions over the boxes consisting of those boxes with H probability greater than I and the rest. We use this result to devise a stochastic search procedure for the practical situation when H is unknown. We present results from simulations that agree with theoretical predictions;and demonstrate that the expected misses by the optimal seeker decreases as the entropy of H decreases, with a maximum obtained for uniform H. Finally, we demonstrate applications of this stochastic search procedure with a coarse assumption about H. The theoretical results and the procedure are applicable to stochastic search over any aspect of machine learning that involves a discrete search-space: for example, choice over features, structures or discretized parameter-selection. In this work, the procedure is used to select features for Deep Relational Machines (DRMs) which are Deep Neural Networks (DNNs) defined in terms of domain-specific knowledge and built with features selected from large, potentially infinite-attribute space. Empirical results obtained across over 70 real-world datasets show that using the stochastic search procedure results in significantly better performances than the state-of-the-art.

关键词： Deep Neural Network inductive logic programming Relational learning Stochastic search Infinite-attribute space

来源：评论

学校读者我要写书评

暂无评论

Towards Comprehensible Representation of Controllers using Machine Learning 34

Towards Comprehensible Representation of Controllers using M...

引用

34th IEEE/ACM International Conference on Automated Software Engineering (ASE)

作者： Balasubramaniam, Gargi Tech Univ Munich Dept Informat Munich Germany

ISBN: (纸本)9781728125084

From the point of view of a software engineer, having safe and optimal controllers for real life systems like cyber physical systems is a crucial requirement before deployment. Given the mathematical model of these systems along with their specifications, model checkers can be used to synthesize controllers for them. The given work proposes novel approaches for making controller analysis easier by using machine learning to represent the controllers synthesized by model checkers in a succinct manner, while also incorporating the domain knowledge of the system. It also proposes the implementation of a visualization tool which will be integrated into existing model checkers. A lucid controller representation along with a tool to visualize it will help the software engineer debug and monitor the system much more efficiently.

关键词： Cyber Physical Systems Controller Synthesis Model Checking Machine Learning inductive logic programming Domain Knowledge Strategy Representation

来源：评论

学校读者我要写书评

暂无评论

inductive logic programming Through Estimation of Distribution Algorithm

Inductive Logic Programming Through Estimation of Distributi...

引用

IEEE Congress on Evolutionary Computation (CEC)

作者： Pitangui, Cristiano Grijo Zaverucha, Gerson Univ Fed Rio de Janeiro Rio De Janeiro RJ Brazil

ISBN: (纸本)9781424478354

Genetic Algorithms (GAs) are known for their capacity to explore large search spaces and due to this ability, they were to some extent applied to inductive logic programming (ILP) problem. Although Estimation of Distribution Algorithms (EDAs) perform better in most problems when compared to standard GAs, this kind of algorithm have not been applied to ILP. This work presents an ILP system based on EDA. Preliminary results show that the proposed system is superior when compared to a "standard" GA and it is very competitive when compared to the state of the art ILP system Aleph.

关键词： inductive logic programming Estimation of Distribution Algorithms Probabilistic Models

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：