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With the rapid development of materials informatics and the Semantic Web, the semantic-driven solution has emerged to improve traditional query technology, which is hard to discover implicit knowledge from materials data. However, it is a nontrivial thing for materials scientists to construct a semantic query, and the query results are usually presented in RDF/XML format which is not convenient for users to understand. This paper aims to propose an approach to construct semantic query and visualize the query results for metallic materials domain.

The authors design a query builder to generate SPARQL query statements automatically based on domain ontology and query conditions inputted by users. Moreover, a semantic visualization model is defined based on the materials science tetrahedron to support the visualization of query results in an intuitive, dynamic and interactive way.

Based on the Semantic Web technology, the authors design an automatic semantic query builder to help domain experts write the normative semantic query statements quickly and simply, as well as a prototype (named MatViz) is developed to visually show query results, which could help experts discover implicit knowledge from materials data. Moreover, the experiments demonstrate that the proposed system in this paper can rapidly and effectively return visualized query results over the metallic materials data set.

This paper mainly discusses an approach to support semantic query and visualization of metallic materials data. The implementation of MatViz will be a meaningful work for the research of metal materials data integration.

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.

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.

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.

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.

Online encyclopedia has facilitated users to easily access interesting knowledge and find solutions for daily problems. However, for the staff in specific domains, especially in secret-related domains, a domain-micropedia is still necessary for work.

In this paper, the authors propose an approach to extract entities from DBpedia and construct the SDPedia in space debris mitigation domain. First, the authors select the root categories about space debris mitigation domain by manual methods. Subsequently, the authors propose Distance of Electrical Resistance, Pages Common Words and AVDP algorithms to implement the extraction. The authors also achieve the data visualization by generating swf files and embedding them into web pages.

In the experiments, the precision, recall and F1-measure are used to evaluate the proposed algorithms. The authors set a series of thresholds to pursue the highest F1-measure. The experimental data indicate that the AVDP algorithm gets the highest F1-measure and is statistically effective for the entities extraction from DBpedia.

The authors propose an approach of deriving linked data from DBpedia and construct their own SDPedia, which has been applied in the space debris mitigation domain currently. Compared with DBpedia, the authors also add the linked data visualization. Moreover, the methodology can be used in many other domains in the future.

The integration and retrieval of the vast data have attracted sufficient attention, thus the W3C workgroup releases R2RML to standardize the transformation from relational data to semantic-aware data. However, it only provides a data transform mechanism to resource description framework (RDF). The generation of mapping alignments still needs manual work or other algorithms. Therefore, the purpose of this paper is to propose a domain-oriented automatic mapping method and an application of the R2RML standard.

In this paper, materials science is focussed to show an example of domain-oriented mapping. source field concept and M3B2 (Metal Materials Mapping Background Base) knowledge bases are established to support the auto-recommending algorithm. As for the generation of RDF files, the idea is to generate the triples and the links, respectively. The links of the triples follow the object-subject relationship, and the links of the object properties can be achieved by the range individuals and the trail path.

Consequently based on the previous work, the authors proposed Engine for Metal Materials Mapping Background Base (EM3B2), a semantic integration engine for materials science. EM3B2 not only offers friendly graphical interfaces, but also provides auto-recommending mapping based on materials knowledge to enable users to avoid vast manually work. The experimental result indicates that EM3B2 supplies accurate mapping. Moreover, the running time of E3MB2 is also competitive as classical methods.

This paper proposed EM3B2 semantic integration engine, which contributes to the relational database-to-RDF mapping by the application of W3C R2RML standard and the domain-oriented mapping.

Empirical results remain unclear as to whether organizational unlearning can improve radical innovation performance. The purpose of this study is to investigate how, and under which conditions, organizational unlearning influences firms’ radical innovation performance.

Drawing on the knowledge-based view, this study develops a theoretical model that hypothesizes a positive relationship between organizational unlearning and radical innovation performance, which is mediated by knowledge generation strategies. It also proposes that the impact of unlearning on knowledge generation strategies will be moderated by dysfunctional competition. Using survey data from 191 Chinese manufacturing firms, the hierarchical regressions were used to test the hypotheses.

The empirical results show that organizational unlearning not only impacts radical innovation performance directly, but also indirectly affects radical innovation performance through two distinct types of knowledge generation strategies: (internal) knowledge creation and (external) information searching. Moreover, dysfunctional competition plays a dual role, strengthening the positive relationship between organizational unlearning and information search and weakening the positive relationship between organizational unlearning and knowledge creation.

The present research broadens the understanding of how to promote radical innovation performance, which has great potential to improve the performance of firms on the market. Specifically, it deepens the knowledge of how organizational unlearning facilitates radical innovation performance by focusing on two distinct types of knowledge generation strategies as the crucial links, and enriches existing literature on the effectiveness of organizational unlearning in a dysfunctional competitive environment.

Practicing organizational unlearning for firms’ long-term success requires firms to develop and implement appropriate knowledge generation strategies in accordance with the characteristics of market competition in their operating environment.

This study offers new insights into how and under what conditions organizational unlearning affects radical innovation performance, enhancing the understanding of how organizational unlearning can be implemented to drive firm radical innovation.

This paper aims to investigate the impact of exploratory innovation on the quality of new product development (NPD), and how do polychronicity of the top management team (TMT) and interfunctional coordination (IFC) moderate the above relationship.

The hypotheses were tested by the survey data of 210 Chinese enterprises. Hierarchical regression analyzes were used to test the hypotheses in this research.

The results indicate that exploratory innovation facilitates NPD quality. TMT polychronicity weakens the effect of exploratory innovation on NPD quality. IFC strengthens the effect of exploratory innovation on NPD quality.

This study provides managers with insight on the relationship between exploratory innovation and NPD quality. To improve NPD quality, managers should be paying more attention to exploratory innovation. Furthermore, this study helps managers to understand how the relationship changes with the increases of TMT polychronicity and IFC.

This study highlights the value of exploratory innovation in increasing NPD quality based on the knowledge-based view. By incorporating TMT polychronicity and IFC based on the attention-based view, it offers much richer understandings of how exploratory innovation affects NPD quality.