Construction and exploitation of an historical knowledge graph to deal with the evolution of ontologies

Cardoso, Silvio Domingos; Silveira, Marcos Da; Pruski, Cédric

doi:10.1016/j.knosys.2020.105508

Cited by 24 publications

(14 citation statements)

References 21 publications

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“…In a specific professional domain, it usually relies on human knowledge and expert experiences to design the ontology layer for knowledge graph [11]. As for the commonly used steel knowledge graph, according to the sorted out concepts from the data set, the hierarchy of classes ontology we designed is shown in Fig.…”

Section: Methodsmentioning

confidence: 99%

A Named Entity Extraction Method for Commonly Used Steel Knowledge Graph

et al. 2021

Lecture Notes in Electrical Engineering

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Knowledge graph, as a semantic network, provides a method to model knowledge from the perspective of entities and their relationships. Since a large quantity of material knowledge is widely distributed, using the structure of the knowledge graph is an effective approach to clarify the internal connections between related knowledge. Named entity recognition is a crucial step to construct a knowledge graph. However, most of the professional domain datasets are label-free and collected from multiple heterogeneous sources. To identify the entities from these datasets, a rule-based and deep learning hybrid method is proposed. The knowledge extracted by regular expression was used to semiautomatically label the corpus, and then trained by deep learning model, which achieved good results. The constructed knowledge graph regarding to the commonly used steels is of great significance to the integration and management of related knowledge, and it lays a foundation for future engineering application.

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Section: Methodsmentioning

confidence: 99%

A Named Entity Extraction Method for Commonly Used Steel Knowledge Graph

et al. 2021

Lecture Notes in Electrical Engineering

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“…Additionally, there are some top-level descriptors that have no parents and are the roots of the trees in the MeSH hierarchy, which are called MeSH trees 4 . The MeSH trees are grouped into sixteen MeSH categories 5 , and each descriptor belongs to one or more MeSH trees and corresponding MeSH categories. For example, the AD descriptor belongs to the "Nervous System Diseases (C10)" tree in the "Diseases" (C) MeSH category and to the "Mental Disorders (F03)" tree in the "Psychiatry and Psychology" (F).…”

Section: Mesh Structurementioning

confidence: 99%

“…More recently, Cardoso et al [5] suggested the interlinking of distinct versions of MeSH developing a historical knowledge graph, to extend queries for biomedical literature retrieval and for supporting the maintenance of semantic annotations. In particular, they introduce "evolution connections" between descriptor elements (e.g.…”

Section: Related Workmentioning

confidence: 99%

What is all this new MeSH about? Exploring the semantic provenance of new descriptors in the MeSH thesaurus

Nentidis,

Krithara,

Tsoumakas

et al. 2021

Preprint

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The Medical Subject Headings (MeSH) thesaurus is a controlled vocabulary widely used in biomedical knowledge systems, particularly for semantic indexing of scientific literature. As the MeSH hierarchy evolves through annual version updates, some new descriptors are introduced that were not previously available. This paper explores the conceptual provenance of these new descriptors. In particular, we investigate whether such new descriptors have been previously covered by older descriptors and what is their current relation to them. To this end, we propose a framework to categorize new descriptors based on their current relation to older descriptors. Based on the proposed classification scheme, we quantify, analyse and present the different types of new descriptors introduced in MeSH during the last fifteen years. The results show that only about 25% of new MeSH descriptors correspond to new emerging concepts, whereas the rest were previously covered by one or more existing descriptors, either implicitly or explicitly. Most of them were covered by a single existing descriptor and they usually end up as descendants of it in the current hierarchy, gradually leading towards a more fine-grained MeSH vocabulary. These insights about the dynamics of the thesaurus are useful for the retrospective study of scientific articles annotated with MeSH, but could also be used to inform the policy of updating the thesaurus in the future.

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“…Formal knowledge is usually described by an ontological approach, which is the formal, explicit specification of a shared conceptualization of a domain. The machine interpretability of formal knowledge (codified knowledge) has been increasingly critical to improving the performance of information retrieval and image understanding (Möller, et al, 2008;Wildemuth, 2004) and organizing solutions to other real problems that exist in a specific research domain (Cardoso, Da Silveira, & Pruski, 2020;Tsatsaronis, Varlamis, 2013). Thus, understanding different transition patterns of formal knowledge is critical to tracking credible and valuable scientific knowledge and promoting innovation in science and technology.…”

Section: Introductionmentioning

confidence: 99%

Understanding knowledge role transitions: A perspective of knowledge codification

Yang

Yong

et al. 2022

Quantitative Science Studies

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Informal knowledge constantly transitions into formal domain knowledge in the dynamic knowledge base. This article focuses on an integrative understanding of the knowledge role transition from the perspective of knowledge codification. The transition process is characterized by several dynamics involving a variety of bibliometric entities, such as authors, keywords, institutions, and venues. We thereby designed a series of temporal and cumulative indicators to respectively explore transition possibility (whether new knowledge could be transitioned into formal knowledge) and transition pace (how long it would take). By analyzing the large-scale metadata of publications that contain informal knowledge and formal knowledge in the PubMed database, we find that multi-dimensional variables are essential to comprehensively understand knowledge role transition. More significantly, (a) the early funding support is more important for improving transition pace; (b) the journal impact has a positive correlation with the transition possibility but a negative correlation with transition pace; (c) the weaker knowledge relatedness raises the transition possibility, whereas the stronger knowledge relatedness improves the transition pace. Peer Review https://publons.com/publon/10.1162/qss_a_00221

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Construction and exploitation of an historical knowledge graph to deal with the evolution of ontologies

Cited by 24 publications

References 21 publications

A Named Entity Extraction Method for Commonly Used Steel Knowledge Graph

A Named Entity Extraction Method for Commonly Used Steel Knowledge Graph

What is all this new MeSH about? Exploring the semantic provenance of new descriptors in the MeSH thesaurus

Understanding knowledge role transitions: A perspective of knowledge codification

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