数据与知识工程 (DKE) 促进了这两个相关领域之间的思想交流和互动。 DKE 覆盖了全球范围内的研究人员、设计师、经理和用户。该杂志的主要目的是识别、调查和分析设计和有效使用这些系统的基本原则。

Data and Knowledge Engineering (DKE) facilitates the exchange of ideas and interaction between the two related fields of data and knowledge. The journal encompasses researchers, designers, managers, and users from around the globe. Its primary aim is to identify, investigate, and analyze the fundamental principles for designing and effectively utilizing these systems.

《数据与知识工程》(Data and Knowledge Engineering) 促进了数据与知识这两个相关领域之间的思想交流与互动。该期刊覆盖了全球范围内的研究人员、设计师、管理者和用户。其主要目的是识别、调查和分析设计及有效使用这些系统的基本原则。

Data and Knowledge Engineering

Computing the semantic similarity between pairs of terms plays a vital role within a myriad of shared data applications, such as data integration and ontology evolution. A first step towards building such applications is to determine which terms are semantically similar to each other. One feasible way to compute the similarity of two terms is to assess their word similarity by exploiting different knowledge resources, e.g., ontologies or domain corpora. Recently, information-theoretic approaches have shown promising results by computing the information content of concepts from the knowledge provided by ontologies. In these methods, the Most Informative Common Subsumers MICSs of two concepts play an important role in determining their similarity. While intuitive, surprisingly, these approaches often do not mirror human judgement well. In this paper, we investigate the effect of choosing a suitable subsumer, called Consensus Common Subsumer (CCS) among all common ancestors of two concepts, on the quality of the term similarity assessment. We cast this issue as an optimization problem by adopting the Particle Swarm Optimization algorithm as one of the most capable optimization approaches. An empirical evaluation based on well-established biomedical benchmarks and ontologies illustrates the accuracy of the proposed approach compared to state-of-the-art approaches.

Samira Babalou

Alsayed Algergawy

Birgitta König-Ries

Data & Knowledge Engineering

SimBio: Adopting Particle Swarm Optimization for ontology-based biomedical term similarity assessment

SimBio: 采用粒子群优化进行基于本体的生物医学术语相似性评估

Anil Sharma

Suresh Kumar

Ontology-based semantic retrieval of documents using Word2vec model

基于本体的语义检索使用Word2vec模型

Derya Birant

Alp Kut

ST-DBSCAN: An algorithm for clustering spatial–temporal data

ST-DBSCAN：一种用于聚类时空数据的算法

Maryam Maslek Elayam

Cyril Ray

Christophe Claramunt

A hierarchical graph-based model for mobility data representation and analysis

基于层次图的移动数据表示与分析模型

Artificial intelligence and digital twins have become more popular in recent years and have seen usage across different application domains for various scenarios. This study reviews the literature at the intersection of the two fields, where digital twins integrate an artificial intelligence component. We follow a systematic literature review approach, analyzing a total of 149 related studies. In the assessed literature, a variety of problems are approached with an artificial intelligence-integrated digital twin, demonstrating its applicability across different fields. Our findings indicate that there is a lack of in-depth modeling approaches regarding the digital twin, while many articles focus on the implementation and testing of the artificial intelligence component. The majority of publications do not demonstrate a virtual-to-physical connection between the digital twin and the real-world system. Further, only a small portion of studies base their digital twin on real-time data from a physical system, implementing a physical-to-virtual connection.

Tim Kreuzer

Panagiotis Papapetrou

Jelena Zdravkovic

Artificial intelligence in digital twins—A systematic literature review

数字孪生中的人工智能——系统文献综述

Aya Sahbi

Céline Alec

Pierre Beust

Semantic vs. LLM-based approach: A case study of KOnPoTe vs. Claude for ontology population from French advertisements

语义与基于大语言模型的方法：KOnPoTe与Claude在从法语广告中构建本体论的案例研究

Louenas Bounia

Insaf Setitra

Enhancing the intelligibility of decision trees with concise and reliable probabilistic explanations

通过简洁可靠的概率解释提升决策树的可理解性

Owen Eriksson

Paul Johannesson

Maria Berholtz

Pär Ågerfalk

Turning Conceptual Modeling Institutional – The prescriptive role of conceptual models in transforming institutional reality

将概念建模制度化——概念模型在转变制度现实中的规范性作用

François Camelin

Samir Loudni

Gilles Pesant

Charlotte Truchet

Coupling MDL and Markov chain Monte Carlo to sample diverse pattern sets

耦合最小描述长度与马尔可夫链蒙特卡罗方法以采样多样化模式集

Abbreviation	Data. Knowl. Eng.
Journal Impact	2.54
Quartiles(Global)	COMPUTER SCIENCE, INFORMATION SYSTEMS(Q2)

ISSN	0169-023X, 1872-6933
h-index	94

Publication Information	Publisher: Elsevier B.V.，Publishing cycle: Monthly，Journal Type: journal，Open Access Journals: No
Basic data	Year of publication: 1987，Proportion of original research papers: 97.06%，Self Citation Rate:7.40%， Gold OA Rate: 26.63%
Average review cycle	网友分享经验：平均12.0个月
Average recruitment ratio	网友分享经验：较易

Data and Knowledge Engineering

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