Results 31 to 40 of about 4,200 (297)
Towards neural-symbolic integration: the evolutionary neural logic networks [PDF]
, 2004 This work presents the application of a new methodology for the production of neural logic networks into two real-world problems from the medical domain.
Tsakonas, Athanasios
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Logical Rule-Based Knowledge Graph Reasoning: A Comprehensive Survey
Mathematics, 2023 With its powerful expressive capability and intuitive presentation, the knowledge graph has emerged as one of the primary forms of knowledge representation and management.
Zefan Zeng, Qing Cheng, Yuehang Si
doaj +1 more source
Applying Inductive Logic Programming to Process Mining [PDF]
, 2007 The management of business processes has recently received a lot of attention. One of the most interesting problems is the description of a process model in a language that allows the checking of the compliance of a process execution (or trace) to the ...
RIGUZZI, Fabrizio +7 more
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Preprocessing in Inductive Logic Programming
CoRR, 2021 Inductive logic programming is a type of machine learning in which logic programs are learned from examples. This learning typically occurs relative to some background knowledge provided as a logic program. This dissertation introduces bottom preprocessing, a method for generating initial constraints on the programs an ILP system must consider.
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Inductive Logic Programming in Clementine [PDF]
, 2000 This paper describes the integration of ILP with Clementine. Background on ILP and Clementine is provided, with a description of Clementine's target users. The benefits of ILP to data mining are outlined, and ILP is compared with pre-existing data mining algorithms. Issues of integration between ILP and Clementine are discussed.
Sam Brewer, Tom Khabaza
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MathematicsSome knowledge bases (KBs) extracted from Wikipedia articles can achieve very high average precision values (over 95% in DBpedia). However, subtle mistakes including inconsistencies, outliers, and erroneous relations are usually ignored in the ...
Yan Wu +3 more
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Contributions to Inductive Logic Programming [PDF]
, 1996 Contents Preface iii 1 What is Inductive Logic Programming? 1 1.1 The importance of learning : : : : : : : : : : : : : : : : : : : : : 1 1.2 Inductive learning : : : : : : : : : : : : : : : : : : : : : : : : : : 2 1.3 The problem setting for ILP : : : :
R. M. De Wolf +2 more
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Advanced Functional Materials, EarlyView.A nanounit‐assembled hydrogel employing a “pull‐and‐push” strategy simultaneously scavenges pro‐inflammatory cell‐free DNA (cfDNA) and delivers regenerative therapeutics in response to burn‐induced hyperthermia. By repolarizing macrophages and promoting angiogenesis, this multifunctional platform accelerates burn wound healing, offering a blueprint for
Han‐Sem Kim +9 more
wiley +1 more source
Synthesizing Recursive Logic Programs by Inverting General Resolution
IEEE AccessA fundamental scalability restriction of most Inductive Logic Programming (ILP) systems is that they search syntactically defined program spaces and cannot utilize relations in data.
Taosheng Qiu, Ryutaro Ichise
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LLM‐Integrated Human–Robot Interaction System for Microrobots
Advanced Robotics Research, EarlyView.This paper proposes an LLM‐based control framework for guiding microrobots using human natural language. This framework can convert the natural human speech into safe and executable command sets for reliable navigation in complex environments. The experimental results show high accuracy and robustness in task performance, demonstrating the potential of
Bairong Zhu, Amar Salehi, Tingting Yu
wiley +1 more source