In the domain of crystal engineering, various schemes have been proposed for the classification of hydrogen bonding (H-bonding) patterns observed in 3D crystal structures. In this study, the aim is to complement these...
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In the domain of crystal engineering, various schemes have been proposed for the classification of hydrogen bonding (H-bonding) patterns observed in 3D crystal structures. In this study, the aim is to complement these schemes with rules that predict H-bonding in crystals from 2D structural information only. Modern computational power and the advances in inductive logic programming (ILP) can now provide computational chemistry with the opportunity for extracting structure-specific rules from large databases that can be incorporated into expert systems. ILP technology is here applied to H-bonding in crystals to develop a self-extracting expert system utilizing data in the Cambridge Structural Database of small molecule crystal structures. A clear increase in performance was observed when the ILP system DMAX was allowed to refer to the local structural environment of the possible H-bond donor/acceptor pairs. This ability distinguishes ILP from more traditional approaches that build rules on the basis of global molecular properties.
inductive logic programming (ILP) is a study of machine learning systems that use clausal theories in first-order logic as a representation language. In this paper, we survey theoretical foundations of ILP from the vi...
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inductive logic programming (ILP) is a study of machine learning systems that use clausal theories in first-order logic as a representation language. In this paper, we survey theoretical foundations of ILP from the viewpoints of logic of Discovery and Machine Learning, and try to unify these two views with the support of the modern theory of logicprogramming. Firstly, we define several hypothesis construction methods in ILP and give their proof-theoretic foundations by treating then as a procedure which complete incomplete proofs. Next, we discuss the design of individual learning algorithms using these hypothesis construction methods. We review known results on learning logic programs in computational learning theory, and show that these algorithms are instances of a generic learning strategy with proof completion methods.
When comparing inductive logic programming (ILP) and attribute-value learning techniques, there is a trade-off between expressive power and efficiency. inductive logic programming techniques are typically more express...
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When comparing inductive logic programming (ILP) and attribute-value learning techniques, there is a trade-off between expressive power and efficiency. inductive logic programming techniques are typically more expressive but also less efficient. Therefore, the data sets handled by current inductive logic programming systems are small according to general standards within the data mining community. The main source of inefficiency lies in the assumption that several examples may be related to each other, so they cannot be handled independently. Within the learning from interpretations framework for inductive logic programming this assumption is unnecessary, which allows to scale up existing ILP algorithms. In this paper we explain this learning setting in the context of relational databases. We relate the setting to propositional data mining and to the classical ILP setting, and show that learning from interpretations corresponds to learning from multiple relations and thus extends the expressiveness of propositional learning, while maintaining its efficiency to a large extent (which is not the case in the classical ILP setting). As a case study, we present two alternative implementations of the ILP system TILDE (Top-down Induction of logical DEcision trees): TILDEclassic, which loads all data in main memory, and TILDELDS, which loads the examples one by one. We experimentally compare the implementations, showing TILDELDS can handle large data sets (in the order of 100,000 examples or 100 MB) and indeed scales up linearly in the number of examples.
Introducing fuzzy predicates in inductive logic programming may serve two different purposes: allowing for more adaptability when learning classical rules or getting more expressivity by learning fuzzy rules. This lat...
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Introducing fuzzy predicates in inductive logic programming may serve two different purposes: allowing for more adaptability when learning classical rules or getting more expressivity by learning fuzzy rules. This latter concern is the topic of this paper. Indeed, introducing fuzzy predicates in the antecedent and in the consequent of rules may convey different non-classical meanings. The paper focuses on the learning of gradual and certainty rules, which have an increased expressive power and have no simple crisp counterpart. The benefit and the application domain of each kind of rules are discussed. Appropriate confidence degrees for each type of rules are introduced. These confidence degrees play a major role in the adaptation of the classical FOIL inductive logic programming algorithm to the induction of fuzzy rules for guiding the learning process. The method is illustrated on a benchmark example and a case-study database.
In this article we analyze a well-known and extensively researched problem: how to find all datasets, on the one hand, and on the other hand only those that are of value to the user when dealing with a specific spatia...
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In this article we analyze a well-known and extensively researched problem: how to find all datasets, on the one hand, and on the other hand only those that are of value to the user when dealing with a specific spatially oriented task. In analogy with existing approaches to a similar problem from other fields of human endeavor, we call this software solution 'a spatial data recommendation service.' In its final version, this service should be capable of matching requests created in the user's mind with the content of the existing datasets, while taking into account the user's preferences obtained from the user's previous use of the service. As a result, the service should recommend a list of datasets best suited to the user's needs. In this regard, we consider metadata, particularly natural language definitions of spatial entities, a crucial piece of the solution. To be able to use this information in the process of matching the user's request with the dataset content, this information must be semantically preprocessed. To automate this task we have applied a machine learning approach. With inductive logic programming (ILP) our system learns rules that identify and extract values for the five most frequent relations/properties found in Slovene natural language definitions of spatial entities. The initially established quality criterion for identifying and extracting information was met in three out of five examples. Therefore we conclude that ILP offers a promising approach to developing an information extraction component of a spatial data recommendation service.
In considering key events of genomic disorders in the development and progression of cancer, the correlation between genomic instability and carcinogenesis is currently under investigation. In this work, we propose an...
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In considering key events of genomic disorders in the development and progression of cancer, the correlation between genomic instability and carcinogenesis is currently under investigation. In this work, we propose an inductive logic programming approach to the problem of modeling evolution patterns for breast cancer. Using this approach, it is possible to extract fingerprints of stages of the disease that can be used in order to develop and deliver the most adequate therapies to patients. Furthermore, such a model can help physicians and biologists in the elucidation of molecular dynamics underlying the aberrations-waterfall model behind carcinogenesis. By showing results obtained some hints about further approach to the hypotheses. on a real-world dataset, we try to give knowledge-driven validations of such
Many inductive logic programming (ILP) methods are incapable of learning programs from probabilistic background knowledge, for example, coming from sensory data or neural networks with probabilities. We propose Proppe...
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Many inductive logic programming (ILP) methods are incapable of learning programs from probabilistic background knowledge, for example, coming from sensory data or neural networks with probabilities. We propose Propper, which handles flawed and probabilistic background knowledge by extending ILP with a combination of neurosymbolic inference, a continuous criterion for hypothesis selection (binary cross-entropy) and a relaxation of the hypothesis constrainer (NoisyCombo). For relational patterns in noisy images, Propper can learn programs from as few as 8 examples. It outperforms binary ILP and statistical models such as a graph neural network.
In many applications of inductive logic programming (ILP), learning occurs from a knowledge base that contains a large number of examples. Storing such a knowledge base may consume a lot of memory. Often, there is a s...
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In many applications of inductive logic programming (ILP), learning occurs from a knowledge base that contains a large number of examples. Storing such a knowledge base may consume a lot of memory. Often, there is a substantial overlap of information between different examples. To reduce memory consumption, we propose a method to represent a knowledge base more compactly. We achieve this by introducing a meta-theory able to build new theories out of other ( smaller) theories. In this way, the information associated with an example can be built from the information associated with one or more other examples and redundant storage of shared information is avoided. We also discuss algorithms to construct the information associated with example theories and report on a number of experiments evaluating our method in different problem domains.
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...
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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.
inductive logic programming (ILP) is concerned with learning relational descriptions that typically have the form of logic programs. In a transformation approach, an ILP task is transformed into an equivalent learning...
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