In this paper we address an issue that has been brought to the attention of the database community with the advent of the Semantic Web, i.e., the issue of how ontologies (and semantics conveyed by them) can help solvi...
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In this paper we address an issue that has been brought to the attention of the database community with the advent of the Semantic Web, i.e., the issue of how ontologies (and semantics conveyed by them) can help solving typical database problems, through a better understanding of Knowledge Representation (KR) aspects related to databases. In particular, we investigate this issue from the 1LP perspective by considering two database problems, (i) the definition of views and (ii) the definition of constraints, for a database whose schema is represented also by means of an ontology. Both can be reformulated as I LP problems and can benefit from the expressive and deductive power of the KR framework DL+LOG(V). We illustrate the application scenarios by means of examples.
inductive logic programming (ILP) is concerned with the induction of logic programs from examples and background knowledge. In ILP, the shift of attention from program synthesis to knowledge discovery resulted in adva...
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inductive logic programming (ILP) is concerned with the induction of logic programs from examples and background knowledge. In ILP, the shift of attention from program synthesis to knowledge discovery resulted in advanced techniques that are practically applicable for discovering knowledge in relational databases. This paper gives a brief introduction to ILP, presents selected ILP techniques for relational knowledge discovery and reviews selected ILP applications.
Describes a tool for quantitatively discriminating between meningioma and astrocytoma tumors. One of the uses of magnetic resonance imaging (MRI) in clinical diagnosis is in-vivo discrimination between tumor and norma...
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Describes a tool for quantitatively discriminating between meningioma and astrocytoma tumors. One of the uses of magnetic resonance imaging (MRI) in clinical diagnosis is in-vivo discrimination between tumor and normal tissue and between tumor types in the brain. There is much interest in increasing the qualitative and quantitative information available from these images. This article presents a study that uses the inductive logic programming tool Progol on measurements of signal intensities in clinical scan images of 28 patients (18 with meningiomas and 10 with astrocytomas) to attempt to discover knowledge that quantitatively dissriminates between the two types of tumors.
inductive logic programming (ILP) is a form of logic-based machine learning. The goal is to induce a hypothesis (a logic program) that generalises given training examples and background knowledge. As ILP turns 30, we ...
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inductive logic programming (ILP) is a form of logic-based machine learning. The goal is to induce a hypothesis (a logic program) that generalises given training examples and background knowledge. As ILP turns 30, we review the last decade of research. We focus on (i) new meta-level search methods, (ii) techniques for learning recursive programs, (iii) new approaches for predicate invention, and (iv) the use of different technologies. We conclude by discussing current limitations of ILP and directions for future research.
inductive logic programming (ILP) is the area of AI which deals with the induction of hypothesised predicate definitions from examples and background knowledge. logic programs are used as a single representation for e...
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inductive logic programming (ILP) is the area of AI which deals with the induction of hypothesised predicate definitions from examples and background knowledge. logic programs are used as a single representation for examples, background knowledge and hypotheses. ILP is differentiated from most other forms of Machine Learning (ML) both by its use of an expressive representation language and its ability to make use of logically encoded background knowledge. This has allowed successful applications of ILP in areas such as molecular biology and natural language which both have rich sources of background knowledge and both benefit from the use of an expressive concept representation languages. For instance, the ILP system Progol has recently been used to generate comprehensible descriptions of the 23 most populated fold classes of proteins, where no such descriptions had previously been formulated manually. In the natural language area ILP has not only been shown to have higher accuracies than various other ML approaches in learning the past tense of English but also shown to be capable of learning accurate grammars which translate sentences into deductive database queries. The area of Learning Language in logic (LLL) is producing a number of challenges to existing ILP theory and implementations. In particular, language applications of ILP require revision and extension of a hierarchically defined set of predicates in which the examples are typically only provided for predicates at the top of the hierarchy. New predicates often need to be invented, and complex recursion is usually involved. Advances in ILP theory and implementation related to the challenges of LLL are already producing beneficial advances in other sequence-oriented applications of ILP. In addition LLL is starting to develop its own character as a sub-discipline of AI involving the confluence of computational linguistics, machine learning and logicprogramming. (C) 1999 Elsevier Science B.V. All rights re
We present a systems biology application of ILP, where the goal is to predict the regulation of a gene under a certain condition from binding site information, the state of regulators, and additional information. In t...
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We present a systems biology application of ILP, where the goal is to predict the regulation of a gene under a certain condition from binding site information, the state of regulators, and additional information. In the experiments, the boosted Tilde model is on par with the original model by Middendorf et al. based on alternating decision trees (ADTrees), given the same information. Adding functional categorizations and protein-protein interactions, however, it is possible to improve the performance substantially. We believe that decoding the regulation mechanisms of genes is an exciting new application of learning in logic, requiring data integration from various sources and potentially contributing to a better understanding on a system level.
Effectiveness and efficiency are two most important properties of ILP approaches. For both top-down and bottom-up search-based approaches, greater efficiency is usually gained at the expense of effectiveness. In this ...
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ISBN:
(纸本)9783540696087
Effectiveness and efficiency are two most important properties of ILP approaches. For both top-down and bottom-up search-based approaches, greater efficiency is usually gained at the expense of effectiveness. In this paper, we propose a bottom-up approach, called ILP by instance patterns, for the problem of concept learning in ILP. This approach is based on the observation that each example has its own pieces of description in the background knowledge, and the example together with these descriptions constitute a instance of the concept subject to learn. Our approach first captures the instance structures by patterns, then constructs the final theory purely from the patterns. On the effectiveness aspect, this approach does not assume determinacy of the learned concept. On the efficiency aspect, this approach is more efficient than existing ones due to its constructive nature, the fact that after the patterns are obtained, both the background and examples are not needed anymore, and the fact that it does not perform coverage test and needs no theorem prover.
The integration of inductive logic programming (ILP) and Bottlenose Dolphin Optimization (BDO) in this research addresses a pressing issue in today's information-saturated landscape: the proliferation of fake news...
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ISBN:
(纸本)9798350348798;9798350348804
The integration of inductive logic programming (ILP) and Bottlenose Dolphin Optimization (BDO) in this research addresses a pressing issue in today's information-saturated landscape: the proliferation of fake news. In an era where misleading information can spread rapidly, traditional methods often fall short in effectively identifying deceptive *** combat this challenge, our approach harnesses the synergies of ILP and BDO. ILP plays a crucial role in constructing logical rules that capture intricate relationships within news data. By doing so, it delves deep into the content, seeking out patterns and inconsistencies that may not be obvious at first glance. BDO, on the other hand, takes inspiration from the social behavior of bottlenose dolphins to optimize the process of generating these rules. Just as dolphins collaborate and communicate to solve complex problems, BDO helps refine the logical rules for better accuracy. Ultimately, this research underscores the potential of bio-inspired optimization, such as BDO, combined with the precision of logicprogramming (ILP) to strengthen the integrity of information dissemination platforms. In an age where the veracity of information is paramount, this innovative approach offers a promising solution to combat the spread of fake news and promote the dissemination of authentic, reliable information.
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 Distr...
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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.
We present a novel approach to non-monotonic ILP and its implementation called TAL (Top-directed Abductive Learning). TAL overcomes some of the completeness problems of ILP systems based on Inverse Entailment and is t...
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ISBN:
(纸本)9783939897170
We present a novel approach to non-monotonic ILP and its implementation called TAL (Top-directed Abductive Learning). TAL overcomes some of the completeness problems of ILP systems based on Inverse Entailment and is the first top-down ILP system that allows background theories and hypotheses to be normal logic programs. The approach relies on mapping an ILP problem into an equivalent ALP one. This enables the use of established ALP proof procedures and the specification of richer language bias with integrity constraints. The mapping provides a principled search space for an ILP problem, over which an abductive search is used to compute inductive solutions.
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