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The paper aims to develop a new method for potential relations retrieval. It aims to find common aspects between co-occurrence analysis and ontology to build a model of semantic information retrieval based on co-occurrence analysis.

This paper used a literature review, co-occurrence analysis, ontology build and other methods to design a model and process of semantic information retrieval based on co-occurrence analysis. Archaeological data from Wuhan University Library's bibliographic retrieval systems was used for experimental analysis.

The literature review found that semantic information retrieval research mainly concentrates on ontology-based query techniques, semantic annotation and semantic relation retrieval. Moreover most recent systems can only achieve obvious relations retrieval. Ontology and co-occurrence analysis have strong similarities in theoretical ideas, data types, expressions, and applications.

The experiment data came from a Chinese university which perhaps limits its usefulness elsewhere.

This paper constructed a model to understand potential relations retrieval. An experiment proved the feasibility of co-occurrence analysis used in semantic information retrieval. Compared with traditional retrieval, semantic information retrieval based on co-occurrence analysis is more user-friendly.

This study is one of the first to combine co-occurrence analysis with semantic information retrieval to find detailed relationships.

This paper aims to explore the characteristics of the co-occurrence network of online tags and propose new approaches of applying social network analysis by utilising social tagging in order to organise data.

The authors collected online resources labelled “tag” from 7 November 2004 to 31 October 2011 from the CiteULike website, comprising 684 papers and their URLs, titles and data on tagging (users, times, and tags). They examined the co-occurrence network of online tags by using the analyses of social networks, including the analysis of coherence, the analysis of centricity and core to periphery categorical analysis.

Some features of the co-occurrence of online tags are as follows: the internet is subject to the “small world” phenomenon, as well as being “scale-free”. The structure of the internet reflects stable areas of core knowledge. In addition to five possible applications of social network analysis, social tagging has the greatest significance in organising online resources.

This research finds that co-occurrence of tags online is an effective way to organise and index data. Some suggestions are provided on the organisation of online resources.

This paper aims to systematically find the main research differences and similarities between social media and social networks in marketing research using the bibliometric perspective and provides suggestions for firms to improve their marketing strategies effectively.

The methods of co-word analysis and network analysis have been used to analyze the two research fields of social media and social networks. Specifically, this study selects 2,424 articles from 27 marketing academic journals present in the database Web of Science, ranging from January 1, 1996 to August 8, 2020.

The results show that social networks and social media are both research hotspots within the discipline of marketing research. The different intimacy nodes of social networks are more complex than social media. Additionally, the research scope of social networks is broader than social media in marketing research as shown by the keyword co-occurrence analysis. The overlap between social media and social networks in marketing research is reflected in the strong focus on their mixed mutual effects.

This paper explores the differences and similarities between social networks and social media in marketing research from the bibliometric perspective and provides a developing trend of their research hotspots in social media and social networks marketing research by keyword co-occurrence analysis and cluster analysis. Additionally, this paper provides some suggestions for firms looking to improve the efficiency of their marketing strategies from social and economic perspectives.

The purpose of this paper is to investigate Japanese consumer perceptions of the benefits of consuming fresh leafy vegetables.

An online bulletin board survey was conducted in Japan to collect responses to an open-ended question about reasons for consuming fresh leafy vegetables. A total of 897 responses were analysed using word co-occurrence network analysis. A community detection method and centrality measures were used to interpret the resulting network map.

Using a community detection algorithm, the authors identify six major groups of words that represent respondents’ core motives for consuming leafy vegetables. While Japanese consumers view health benefits to be most important, sensory factors, such as texture, colour, and palatability, and convenience factors also influence attitudes. The authors find that centrality measures can be useful in identifying keywords that appear in various contexts of consumer responses.

This is the first paper to use a quantitative text analysis to examine consumer perceptions for fresh leafy vegetables. The analysis also provides pointers for creating visually interpretable co-occurrence network maps from textual data and discusses the role of community structure and centrality in interpreting such maps.

With the development of economic globalization and the growth of cross-border technology flow, the internationalization of innovation has become an important strategy for enterprises in global competition for both investment optimization and technological advancement. The purpose of this paper is to reveal the research evolution in internationalization of innovation, investigate the hot spot transformation, and predict the future research trends.

The main research approaches in this study are literature co-citation analysis and keyword co-occurrence analysis. Co-citation is applied as a semantic similarity measure for related papers that makes use of citation relationships. Co-occurrence frequency analysis of keywords is also carried out to reveal the hot spots in research of internationalization of innovation. With the data downloaded from Web of Science, Citespace was used as a tool of scientometrics to visualize the node papers, knowledge mapping and keyword co-occurrence ranking in different stages of research evolution. The literature being analyzed in this study come from paper collection by searching the titles, abstracts and keywords, for terms that include “international innovation”, “international R&D”, “international technology”, “globalizational innovation”, “globalizational R&D”, “globalizational technology”, “multinational innovation”, “multinational R&D” and “multinational technology”.

The investigation reveals that there are two distinct stages in research evolution of the internationalization of innovation. The direction of innovation diffusion has turned from “one-way trickle down from developed countries” to “two-way interaction between developed countries and emerging countries”. Meanwhile, the research hotspots have been transformed since 2000 from “detail and operation-focused” to “profound and strategy-focused”.

The paper gives an insight into the internationalization of innovation field using literature from the Web of Science as an illustration.

This article aims to explore valuable insights into the construction progress monitoring (CPM) research domain, which include main research topics, knowledge gaps and future research themes. For a long time, CPM has been significantly researched with increasing enthusiasm. Although a few review studies have been carried out, there is non-existence of a quantitative review study that can deliver a holistic picture of CPM.

The science mapping-based scientometric analysis was systematically processed with 1,835 CPM-related journal articles retrieved from Scopus. The co-authorship analysis and direct citation analysis were carried out to identify the most influential researchers, countries and publishers of the knowledge domain. The co-occurrence analysis of keyword was assessed to reveal the most dominating research topics and research trend with the visual representation of the considered research domain.

This study reveals seven clusters of main research topics from the keyword co-occurrence analysis. The evolution of research confirms that CPM-related research studies were mainly focused on fundamental and traditional CPM research topics before 2007. The period between 2007 and 2020 has seen a shift of research efforts towards digitalization and automation. The result suggests Building Information Modelling (BIM) as the most common, growing and influential research topic in the CPM research domain. It has been used in combination with different data acquisition technologies (e.g. photogrammetry, videogrammetry, laser scanning, Internet of Things (IoT) sensors) and data analytics approaches (e.g. machine learning and computer vision).

This study provides the horizon of potential research in the research domain of CPM to researchers, policymakers and practitioners by availing of main research themes, current knowledge gaps and future research directions.

This paper represents the first scientometric study depicting the state-of-the-art of the research by assessing the current knowledge domain of CPM.

The paper aims to explore whether topic analysis (identification of the core contents, trends and topic distribution in the target field) can be performed using a more low-cost and easily applicable method that relies on a small dataset, and how we can obtain this small dataset based on the features of the publications.

The paper proposes a topic analysis method based on prolific and authoritative researchers (PARs). First, the authors identify PARs in a specific discipline by considering the number of publications and citations of authors. Based on the research publications of PARs (small dataset), the authors then construct a keyword co-occurrence network and perform a topic analysis. Finally, the authors compare the method with the traditional method.

The authors found that using a small dataset (only 6.47% of the complete dataset in our experiment) for topic analysis yields relatively high-quality and reliable results. The comparison analysis reveals that the proposed method is quite similar to the results of traditional large dataset analysis in terms of publication time distribution, research areas, core keywords and keyword network density.

Expert opinions are needed in determining the parameters of PARs identification algorithm. The proposed method may neglect the publications of junior researchers and its biases should be discussed.

This paper gives a practical way on how to implement disciplinary analysis based on a small dataset, and how to identify this dataset by proposing a PARs-based topic analysis method. The proposed method presents a useful view of the data based on PARs that can produce results comparable to traditional method, and thus will improve the effectiveness and cost of interdisciplinary topic analysis.

This paper proposes a PARs-based topic analysis method and verifies that topic analysis can be performed using a small dataset.

The purpose of this paper is to examine publication characteristics and dynamic evolution of the Industrial Management & Data Systems (IMDS) over the past 25 years from volume 94, issue 1, in 1994 through volume 118, issue 9, in 2018, using a bibliometric analysis, and identify the leading trends that have affected the journal during this time frame.

A bibliometric approach was used to provide a basic overview of the IMDS, including distribution of publication and citations, articles citing the IMDS, top-cited papers and publication patterns. Then, a complex network analysis was employed to present the most productive, influential and active authors, institutes and countries/regions. In addition, cluster analysis and alluvial diagram were used to analyze author keywords.

This study presents the basic bibliometric results for the IMDS and focuses on exploring its performance over the last 25 years. And it reveals the most productive, influential and active authors, institutes and countries/regions in IMDS. Moreover, this study detects the existence of at least five different keywords clusters and discovers how themes have evolved through the intricate citation relationships in IMDS.

The main contribution of this paper is the use of multiple analysis techniques from a complex network paradigm to emphasize the time evolving nature of the co-occurrence networks and to explore the variation of the collaboration networks in the IMDS. For the first time, the evolution of research themes is revealed with a purely data-driven approach.

This research aimed to visualize and analyze the co-word network and thematic clusters of the intellectual structure in the field of linked data during 1900–2021.

This applied research employed a descriptive and analytical method, scientometric indicators, co-word techniques, and social network analysis. VOSviewer, SPSS, Python programming, and UCINet software were used for data analysis and network structure visualization.

The top ranks of the Web of Science (WOS) subject categorization belonged to various fields of computer science. Besides, the USA was the most prolific country. The keyword ontology had the highest frequency of co-occurrence. Ontology and semantic were the most frequent co-word pairs. In terms of the network structure, nine major topic clusters were identified based on co-occurrence, and 29 thematic clusters were identified based on hierarchical clustering. Comparisons between the two clustering techniques indicated that three clusters, namely semantic bioinformatics, knowledge representation, and semantic tools were in common. The most mature and mainstream thematic clusters were natural language processing techniques to boost modeling and visualization, context-aware knowledge discovery, probabilistic latent semantic analysis (PLSA), semantic tools, latent semantic indexing, web ontology language (OWL) syntax, and ontology-based deep learning.

This study adopted various techniques such as co-word analysis, social network analysis network structure visualization, and hierarchical clustering to represent a suitable, visual, methodical, and comprehensive perspective into linked data.

This paper aims to explore the relationship between open innovation (OI) and absorptive capacity (AC) using a bibliometric analysis of existing literature.

The bibliometric analysis is used to review the covered research articles in the Web of Science (WoS) database. The time span covered over 20 years from the year 2000 to 2020.

The study suggests that it is an attracting and growing field for researchers, and there exists a close relationship between OI and AC. Further, the literature has parted into three research streams (1) AC and OI: dependency and interchangeability; (2) OI and its future avenues (3) OI and AC: critical factor for firm innovation performance which elaborate various future scopes to study.

The study's limitations exist with the biasness in database selection criteria, such as the possible non-inclusion of crucial articles.

The study’s implications are to discern close association and path dependency of AC and OI; and facilitate the innovation performance of the firm via developing of AC.

The approach used is a novelty, and the conclusions can better understand the relationship between both terms (OI and AC). Thus, it can help increase firm innovation performance.