STSM: An Infrastructure for Unifying Steel Knowledge and Discovering New Knowledge

Zhang, Xiaoming; Lv, Pengtao; Wang, Jianxia

doi:10.14257/ijdta.2014.7.6.16

Cited by 7 publications

(6 citation statements)

References 41 publications

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“…In the future, we will explore the following research directions: (1) we will try to discover new relationships between entities in the generated domain knowledge graph to implement the knowledge graph completion. (2) We will explore the details of how to combine multi-modal knowledge base with Visual Question Answering system, so as to show more attractive practical significance of the multi-modal knowledge graph. 3We will continue to extend the method to make it suitable to construct general knowledge graphs.…”

Section: Discussionmentioning

confidence: 99%

“…The upper concepts of the elements in V Gi are called the extensional association concepts (EC Gi ), which are derived from the conceptual hierarchy by WordNet or DBpedia. The collection of extensional association concepts is defined as Equation (2).…”

Section: Problem Description and Concept Definitionmentioning

confidence: 99%

“…The research aiming at the semantic representation of domain-specific data generally focuses on text data or structured data, such as transforming structured databases into knowledge graph to provide semantic query services [1] and extracting knowledge from unstructured source data to build new ontologies (e.g., STSM [2], X. Zhang [3]). Even some ontology (e.g., MMOY [4] and Materials ontology [5]) do not focus on multimedia data at the beginning of construction, so the ability of these knowledge graph to process multimedia data (such as images, video, audio, etc.)…”

Section: Introductionmentioning

confidence: 99%

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From Vision to Content: Construction of Domain-Specific Multi-Modal Knowledge Graph

et al. 2019

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Knowledge graphs are usually constructed to describe the various concepts that exist in real world as well as the relationships between them. There are many knowledge graphs in specific fields, but they usually pay more attention on text or structured data, ignoring the image vision information, and cannot play an adequate role in the emerging visualization applications. Aiming at this issue, we design a method that integrates image vision information and text information derived from Wikimedia Commons to construct a domain-specific multi-modal knowledge graph, taking the metallic materials domain as an example to illustrate the method. The text description of each image is regarded as its context semantic to acquire the image's context semantic labels based on the DBpedia resource. Furthermore, we adopt deep neural network model instead of simple visual descriptors to acquire the image's visual semantic labels using the concepts from WordNet. In order to fuse the visual semantic labels and context semantic labels, a path-based concept extension and fusion strategy is proposed based on the conceptual hierarchies of WordNet and DBpedia to obtain the effective extension concepts as well as the links between them, increasing the scale of the knowledge graph and enhancing the correlation between images. The experimental results show that the maximum extension level has a significant impact on the quality of the generated domain knowledge graph, and the best extension level number is respectively determined for both DBpedia and WordNet. In addition, the results of this paper are compared with IMGpedia to further show the effectiveness of the proposed method. INDEX TERMS Knowledge graph, information fusion, multi-modal knowledge, metallic materials knowledge. IV. CONSTRUCTION METHOD OF DOMAIN-SPECIFIC MULTI-MODAL KNOWLEDGE GRAPH A. APPROACH OVERVIEW

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Section: Discussionmentioning

confidence: 99%

Section: Problem Description and Concept Definitionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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From Vision to Content: Construction of Domain-Specific Multi-Modal Knowledge Graph

et al. 2019

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“…The defined axioms A d mainly pay attention to C e as well as C d , where C e describes the constraints for equivalent relations between classes and C d denotes the constraints for disjoint relations between classes. The restrictive axioms A r describe the relations between concepts in an ontology and contains the value restrictions R v (the constraints of domain and range of properties) and cardinality restrictions R c (the quantity of property values) (Bühmann and Lehmann, 2012; Zhang et al , 2014). For example, in a UO, the A d of Derived-Unit can be expressed as Derived-Unit≡Unitㄇ彐 has-factor.unit-Factor, where Derived-Unit, Unit and unit-Factor are classes, Unit represents the base unit, has-factor is an object property and unit-Factor is the conversion factor.…”

Section: The Comparison Of Units Ontologiesmentioning

confidence: 99%

“…material science, sports and biology). In the material science domain, there are some existing materials ontologies (Zhang et al , 2015) like the materials ontology (named as AMO in this paper) designed by Ashino (2010), MatOnto (Cheung et al , 2009) and STSM (Zhang et al , 2014). SI units are used in AMO (Ashino, 2010) and the UO of NASA is applied in MatOnto (Cheung et al , 2009) to strengthen the expression ability.…”

Section: Reuse Strategies Of the Uo In Materials Ontologymentioning

confidence: 99%

A survey on units ontologies: architecture, comparison and reuse

Zhang

Zhao

et al. 2017

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Purpose With the increasing spread of ontologies in various domains, units have gradually become an essential part of ontologies and units ontologies have been developed to offer a better expression ability for the practical usage. From the perspectives of architecture, comparison and reuse, the purpose of this paper is to provide a comprehensive survey on four mainstream units ontologies: quantity-unit-dimension-type, quantities, units, dimensions and values, ontology of units of measure and units ontology (UO) of the open biomedical ontologies, in order to address well the state of the art and the reuse strategies of the UO. Design/methodology/approach An architecture of units ontologies is presented, in which the relations between key factors (i.e. units of measure, quantity and dimension) are discussed. The criteria for comparing units ontologies are developed from the perspectives of organizational structure, pattern design and application scenario. Then, the authors compare four typical units ontologies based on the proposed comparison criteria. Furthermore, how to reuse these units ontologies is discussed in materials science domain by utilizing two reuse strategies of partial reference and complete reference. Findings Units ontologies have attracted high attention in the scientific domain. Based on the comparison of four popular units ontologies, this paper finds that different units ontologies have different design features from the perspectives of basis structure, units conversion and axioms design; a UO is better to be applied to the application areas that satisfy its design features; and many challenges remain to be done in the future research of the UO. Originality/value This paper makes an extensive review on units ontologies, by defining the comparison criteria and discussing the reuse strategies in the materials domain. Based on this investigation, guidelines are summarized for the selection and reuse of units ontologies.

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A core reference ontology for steelmaking process knowledge modelling and information management

Cao

Beden

Beckmann

2022

Computers in Industry

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STSM: An Infrastructure for Unifying Steel Knowledge and Discovering New Knowledge

Abstract: There are rich data resources in materials science, but these data resources are heterogeneous in level of system, structure, syntax, and

Cited by 7 publications

References 41 publications

From Vision to Content: Construction of Domain-Specific Multi-Modal Knowledge Graph

From Vision to Content: Construction of Domain-Specific Multi-Modal Knowledge Graph

A survey on units ontologies: architecture, comparison and reuse

A core reference ontology for steelmaking process knowledge modelling and information management

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