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Learning function-free Horn expressions

MACHINE LEARNING 1999年第3期37卷 241-275页

作者： Khardon, R Univ Edinburgh Div Informat Edinburgh EH9 3JZ Midlothian Scotland

The problem of learning universally quantified function free first order Horn expressions is studied. Several models of learning from equivalence and membership queries are considered, including the model where interpretations are examples (Learning from Interpretations), the model where clauses are examples (Learning from Entailment), models where extensional or intentional background knowledge is given to the learner (as done in inductive logic programming), and the model where the reasoning performance of the learner rather than identification is of interest (Learning to Reason). We present learning algorithms for all these tasks for the class of universally quantified function free Horn expressions. The algorithms are polynomial in the number of predicate symbols in the language and the number of clauses in the target Horn expression but exponential in the arity of predicates and the number of universally quantified variables. We also provide lower bounds for these tasks by way of characterising the VC-dimension of this class of expressions. The exponential dependence on the number of variables is the main gap between the lower and upper bounds.

关键词： computational learning theory inductive logic programming Horn expressions direct products

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Inclusion of domain-knowledge into GNNs using mode-directed inverse entailment

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MACHINE LEARNING 2022年第2期111卷 575-623页

作者： Dash, Tirtharaj Srinivasan, Ashwin Baskar, A. BITS Pilani APPCAIR KK Birla Goa Campus Pilani 403726 Goa India BITS Pilani Dept CS & IS KK Birla Goa Campus Pilani 403726 Goa India

We present a general technique for constructing Graph Neural Networks (GNNs) capable of using multi-relational domain knowledge. The technique is based on mode-directed inverse entailment (MDIE) developed in inductive logic programming (ILP). Given a data instance e and background knowledge B, MDIE identifies a most-specific logical formula perpendicular to(B)(e) that contains all the relational information in B that is related to e. We represent perpendicular to(B)(e) by a "bottom-graph" that can be converted into a form suitable for GNN implementations. This transformation allows a principled way of incorporating generic background knowledge into GNNs: we use the term 'BotGNN' for this form of graph neural networks. For several GNN variants, using real-world datasets with substantial background knowledge, we show that BotGNNs perform significantly better than both GNNs without background knowledge and a recently proposed simplified technique for including domain knowledge into GNNs. We also provide experimental evidence comparing BotGNNs favourably to multi-layer perceptrons that use features representing a "propositionalised" form of the background knowledge;and BotGNNs to a standard ILP based on the use of most-specific clauses. Taken together, these results point to BotGNNs as capable of combining the computational efficacy of GNNs with the representational versatility of ILP.

关键词： Neuro-symbolic learning inductive logic programming Mode-directed inverse entailment Graph neural networks Background knowledge

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MP-SPILDL: A Massively Parallel inductive logic Learner in Description logic

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IEEE ACCESS 2024年 12卷 130884-130895页

作者： Algahtani, Eyad King Saud Univ Coll Appl Comp Sci Dept Informat Syst Riyadh 11362 Saudi Arabia

This article presents MP-SPILDL, a massively parallel inductive logic learner in Description logic (DL). MP-SPILDL is a scalable inductive logic programming (ILP) algorithm that exploits existing Big Data infrastructure to perform large-scale inductive logic learning in DL (the ALCQI((D)) DL language in particular). MP-SPILDL targets accelerating both hypothesis search and hypothesis evaluation by aggregating the computing power of multi-core CPUs with their vector/SIMD instructions and multi-GPUs in a Hadoop cluster. In terms of hypothesis search, MP-SPILDL employs a novel MapReduce-based algorithm that performs distributed parallel hypothesis search. MP-SPILDL also employs a novel MapReduce-based procedure that eliminates all redundant hypotheses generated after each learning iteration. Moreover, MP-SPILDL utilizes deterministic ordering of hypotheses' operands to avoid exploring redundant areas of the search space, similar to the DL-Learner, the state of the art in DL-based ILP literature. In terms of hypothesis evaluation, MP-SPILDL performs parallel hypothesis evaluation, which uses all CPU cores combined with their vector instructions and all multi-GPUs of all machines in the Hadoop cluster. According to the experimental results using an Apache Spark implementation on a Hadoop cluster of three worker machines (36 total CPU cores, 7 total GPUs), MP-SPILDL achieved speedups of up to 13.3 folds using parallel beam search with $beamWidth = 32 and CPU-based vectorized hypothesis evaluation - the best-case scenario. On small datasets such as Michalski's trains, MP-SPILDL achieved a slower performance than the baseline, representing the worst-case scenario.

关键词： logic Big Data Description logic Graphics processing units Clustering algorithms Resource management OWL Machine learning logic programming inductive logic programming description logic GPU big data MapReduce apache spark

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

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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

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Learning and generalising semantic knowledge from object scenes

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ROBOTICS AND AUTONOMOUS SYSTEMS 2008年第11期56卷 891-900页

作者： D'Este, Claire Sammut, Claude Univ New S Wales ARC Ctr Excellence Autonomous Syst Sch Comp Sci & Engn Sydney NSW 2052 Australia

The robot described in this paper learns words that relate to objects and their attributes, and also learns concepts, which may be recursive, that involve relationships between several objects. Once the system is explicitly taught some words by a human teacher it finds new objects that might help to refine its concepts. Once it has found a new object, it tries to generalise its concepts to include the new object and asks the teacher for feedback. The robot learns further properties of objects by interacting with them, by touching them or walking around them to gain a new perspective. The system learns semantic knowledge front spoken interactions, using speech recognition and generation, motion segmentation, feature extraction from images using Ripple Down Rules and generalisation using inductive logic programming. (C) 2008 Elsevier B.V. All rights reserved.

关键词： Human-Robot Communication inductive logic programming Ripple Down Rules Word learning Concept learning

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Improving Hit Ratio of ILP-based Concept Discovery System with Memoization

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COMPUTER JOURNAL 2014年第1期57卷 138-153页

作者： Mutlu, Alev Senkul, Pinar Middle E Tech Univ Dept Comp Engn TR-06531 Ankara Turkey

Although inductive logic programming (ILP)-based concept discovery systems have applications in a wide range of domains, they still suffer from scalability and efficiency issues. One of the reasons for the efficiency problem is the high number of query executions necessary in the concept discovery process. Owing to the refinement operator of ILP-based concept discovery systems, these queries repeat frequently. In this work, we propose a method to improve the look-up table hit ratio for repeating queries of ILP-based concept discovery systems with memoization capabilities. The proposed method introduces modifications on search space evaluation and the covering steps of such systems so that query results of the previous iterations can be exploited. Experimental results show that the proposed method improves the hash table hit ratio of ILP-based concept discovery systems with an affordable cost of extra memory consumption.

关键词： inductive logic programming concept discovery efficiency memoization hit count

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Type Extension Trees for feature construction and learning in relational domains

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ARTIFICIAL INTELLIGENCE 2013年 204卷 30-55页

作者： Jaeger, Manfred Lippi, Marco Passerini, Andrea Frasconi, Paolo Aalborg Univ Inst Datal Aalborg Denmark Univ Siena Dipartimento Ingn Informaz & Sci Matemat I-53100 Siena Italy Univ Trento Dipartimento Ingn & Sci Informaz Trento Italy Univ Florence Dipartimento Ingn Informaz I-50121 Florence Italy

Type Extension Trees are a powerful representation language for "count-of-count" features characterizing the combinatorial structure of neighborhoods of entities in relational domains. In this paper we present a learning algorithm for Type Extension Trees (TET) that discovers informative count-of-count features in the supervised learning setting. Experiments on bibliographic data show that TET-learning is able to discover the count-of-count feature underlying the definition of the h-index, and the inverse document frequency feature commonly used in information retrieval. We also introduce a metric on TET feature values. This metric is defined as a recursive application of the Wasserstein-Kantorovich metric. Experiments with a k-NN classifier show that exploiting the recursive count-of-count statistics encoded in TET values improves classification accuracy over alternative methods based on simple count statistics. (C) 2013 Elsevier B.V. All rights reserved.

关键词： Statistical relational learning inductive logic programming Feature discovery

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Integrating induction and abduction in logic programming

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INFORMATION SCIENCES 1999年第1期116卷 25-54页

作者： Lamma, E Mello, P Milano, M Riguzzi, F Univ Bologna DEIS I-40136 Bologna Italy

We propose an approach for the integration of abduction and induction in logic programming. We define an Abductive Learning Problem as an extended inductive logic programming problem where both the background and target theories are abductive theories and where abductive derivability is used as the coverage relation instead of deductive derivability. The two main benefits of this integration are the possibility of learning in presence of incomplete knowledge and the increased expressive power of the background and target theories. We present the system LAP (Learning Abductive Programs) that is able to solve this extended learning problem and we describe, by means of examples, four different learning tasks that can be performed by the system: learning from incomplete knowledge, learning rules with exceptions, learning from integrity constraints and learning recursive predicates. (C) 1999 Elsevier Science Inc. All rights reserved.

关键词： abductive logic programming inductive logic programming machine learning nonmonotonic reasoning

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EMBODIED CONCEPT DISCOVERY THROUGH QUALITATIVE ACTION MODELS

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INTERNATIONAL JOURNAL OF UNCERTAINTY FUZZINESS AND KNOWLEDGE-BASED SYSTEMS 2011年第3期19卷 453-475页

作者： Kosmerlj, Aljaz Bratko, Ivan Zabkar, Jure Univ Ljubljana Artificial Intelligence Lab Fac Comp & Informat Sci SI-1001 Ljubljana Slovenia

We present a novel approach to embodied learning of qualitative models. We introduce, algorithm STRUDEL that enables an autonomous robot to discover new concepts by performing experiments in its environment. The robot collects data about its actions and its observations of the environment. Prom the obtained data, the robot lean is qualitative descriptive models of the effects that its actions have in the environment. Models are learned using inductive logic programming. We describe two experiments with a humanoid robot Nao in which Nao learns descriptive qualitative models which contain what can be interpreted as simple definitions of the concepts of movability and stability.

关键词： Cognitive robotics inductive logic programming predicate invention qualitative modeling symbolic modeling

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Learning concepts from sensor data of a mobile robot

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MACHINE LEARNING 1996年第2-3期23卷 305-332页

作者： Klingspor, V Morik, KJ Rieger, AD Computer Science Dept. LS VIII Univ. Dortmund Dortmund Germany

Machine learning can be a most valuable tool for improving the flexibility and efficiency of robot applications. Many approaches to applying machine learning to robotics are known. Some approaches enhance the robot's high-level processing, the planning capabilities, Other approaches enhance the low-level processing, the control of basic actions. In contrast, the approach presented in this paper uses machine learning for enhancing the link between the low-level representations of sensing and action and the high-level representation of planning. The aim is to facilitate the communication between the robot and the human user. A hierarchy of concepts is learned from route records of a mobile robot. Perception and action are combined at every level, i.e., the concepts are perceptually anchored. The relational learning algorithm GRDT has been developed which completely searches in a hypothesis space, that is restricted by rule schemata, which the user defines in terms of grammars.

关键词： inductive logic programming robot navigation combining sensing and action perceptually anchored concepts

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