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.
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 r...
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This paper explores a hybrid approach to the multimodal co-construction of explanations for robot faults, integrating inductive logic programming (ILP) and Large Language Models (LLMs). As AI and robotics continue to ...
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ISBN:
(纸本)9798400704635
This paper explores a hybrid approach to the multimodal co-construction of explanations for robot faults, integrating inductive logic programming (ILP) and Large Language Models (LLMs). As AI and robotics continue to permeate various aspects of daily life, the ability of these systems to explain their actions and failures is crucial for fostering user trust and ensuring safe interactions. We propose a framework that combines the interpretability of ILP, which generates logical rules from data, with the linguistic capabilities of LLMs, which provide natural language explanations. This approach enables the generation of coherent, contextually appropriate explanations that can be tailored to the needs of users.
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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Ontologies are one of the important and effective parts of semantic web which constitute the infrastructure and background knowledge of this realm of web science. Finding valid mappings as much as possible between the...
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ISBN:
(纸本)9781538653647
Ontologies are one of the important and effective parts of semantic web which constitute the infrastructure and background knowledge of this realm of web science. Finding valid mappings as much as possible between the concepts or entities of ontologies, especially for the large ones, is a prominent task to align those concepts together and finally merge and integrate their ontologies to make a general and global ontology that is smaller and more flexible in many applications of semantic web. This paper describes a new learning-based ontology mapping method in which inductive logic programming (ILP) is used to learn ontology mapping using information gathered from instances of each entity in order to make some correct and valid alignments between concepts of different ontologies.
This paper presents an interactive system for exploring and editing logic-based machine learning models specialised for the relational reasoning problem domain. Prior work has highlighted the value of visual interface...
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ISBN:
(纸本)9798400701078
This paper presents an interactive system for exploring and editing logic-based machine learning models specialised for the relational reasoning problem domain. Prior work has highlighted the value of visual interfaces for enabling effective user interaction during model training. However, these existing systems require two-dimensional tabular data and are not well-suited to relational machine learning tasks. logic-based methods, such as those developed in the field of inductive logic programming, can address this;they retain relational information by operating directly on raw relational data while remaining inherently interpretable and editable to allow for human intervention. However, such systems require logical expertise to operate effectively and do not enable visual exploration. We aim to address this;taking design inspiration from equivalent interfaces for propositional learning, we present a visual interface that enhances the usability of inductive logic programming systems for domain experts without a background in computational logic.
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