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1 – 10 of over 3000Wen Lou and Junping Qiu
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…
Abstract
Purpose
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.
Design/methodology/approach
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.
Findings
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.
Research limitations/implications
The experiment data came from a Chinese university which perhaps limits its usefulness elsewhere.
Practical implications
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.
Originality/value
This study is one of the first to combine co-occurrence analysis with semantic information retrieval to find detailed relationships.
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Keywords
Ma Feicheng and Li Yating
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…
Abstract
Purpose
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.
Design/methodology/approach
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.
Findings
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.
Originality/value
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.
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Fan Yu, Junping Qiu and Wen Lou
This paper aims to solve the disadvantages of content-based domain ontology (CBDO) and metadata-based domain ontology (MDO) and improve organization and discovery efficiency of…
Abstract
Purpose
This paper aims to solve the disadvantages of content-based domain ontology (CBDO) and metadata-based domain ontology (MDO) and improve organization and discovery efficiency of library resources by resource ontology (RO).
Design/methodology/approach
The paper constructed an RO model. Methods of informetrics are utilized to reveal semantic relationships among library resources. Methods of ontology, ontology-relational database mapping (O-R mapping) and relational database modelling are utilized to construct RO. Take author co-occurrence for example, the paper demonstrated the capability of RO model.
Findings
RO not only revealed the deep-level semantic relationships of metadata of library resources but also realized totally computer-automated processing. RO improved the efficiency of knowledge organization and discovery.
Research limitations/implications
Semantic relationships revealed by RO are limited to simple metadata, which makes it difficult to reveal fine-grained semantic relationships. Ongoing research focuses on the revelation of semantic relationships based on the title and abstract.
Practical implications
The paper includes implications for utilizing methods of Informetrics to construct ontology.
Originality/value
This paper proposed a standardized process of ontology construction in library resources. It may be of potential interest for anyone who needs to effectively organize library resources.
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Jianwei Zhang, Seiya Tomonaga, Shinsuke Nakajima, Yoichi Inagaki and Reyn Nakamoto
Identifying important users from social media has recently attracted much attention in the information and knowledge management community. Although researchers have focused on…
Abstract
Purpose
Identifying important users from social media has recently attracted much attention in the information and knowledge management community. Although researchers have focused on users’ knowledge levels on certain topics or influence degrees on other users in social networks, previous works have not studied users’ prediction ability on future popularity. This paper aims to propose a novel approach to find prophetic bloggers based on their buzzword prediction ability.
Design/methodology/approach
The main approach is to conduct a time-series analysis in the blogosphere considering four factors: post earliness, content similarity, entry frequency and buzzword coverage. Our method has four steps: categorizing a blogger into knowledgeable categories, identifying past buzzwords, analyzing a buzzword’s peak time content and growth period and, finally, evaluating a blogger’s prediction ability on a buzzword and on a category.
Findings
Experimental results on real-world blog data consisting of 150 million entries from 11 million bloggers demonstrate that the proposed approach can find prophetic bloggers and outperforms others that do not take temporal features into account.
Originality/value
To the best of the authors’ knowledge, our approach is the first successful attempt to identify prophetic bloggers. Finding prophetic bloggers can bring great values for two reasons. First, as prophetic bloggers tend to post creative and insightful information, analysis on their blog entries may help find future buzzword candidates. Second, communication with prophetic bloggers can help understand future trends, gain insight into early adopters’ thoughts on new technology or even foresee things that will become popular.
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Mika Morishima, Tamaki Mitsuno and Koya Kishida
Many Japanese hay fever (HF) sufferers wear a hygienic face mask to prevent pollen inhalation, but most find it very uncomfortable. The purpose of this paper is to identify the…
Abstract
Purpose
Many Japanese hay fever (HF) sufferers wear a hygienic face mask to prevent pollen inhalation, but most find it very uncomfortable. The purpose of this paper is to identify the problems associated with mask wearing through repeated surveys. This information can be used in the improved design of a hygienic face mask that can be worn without discomfort by HF sufferers.
Design/methodology/approach
In 2009 (n=1,519), 2012 (n=2,994), and 2015 (n=3,213), repeated surveys of university students were conducted. HF sufferers were queried regarding symptoms, countermeasures, and problems associated with wearing a hygienic face mask. Holistic perspectives for each year were obtained by a co-occurrence analysis of the aggregated data. The triplet co-occurrence of specific problems was compared among the surveys using the χ2 test. Temporary and contemporary co-occurrence relationships were also determined.
Findings
Most Japanese university students with HF wore a hygienic face mask. In each survey, the most common problems associated with mask use were related to its thermal, hygroscopic, and air-flow properties. Contemporary problems with co-occurrence relationships were “humidity,” “breathing difficulty,” and “mist over eyeglasses” for males and, “humidity,” “breathing difficulty,” and “make-up coming off” for females.
Originality/value
The results of this study will contribute to improving hygienic face mask design. The co-occurrence of contemporary problems related to mask use was identified by comparing the results obtained in each year. The thermal, hygroscopic, and air flow properties of the mask cause these problems, and the air gap between the mask and the wearer’s face influences the inherent physical properties of the mask. To measure the air gap, a suitable hydrostatic pressure-balanced experimental method was applied, and the data were demonstrated experimentally.
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Masahiro Ito, Kotaro Nakayama, Takahiro Hara and Shojiro Nishio
Recently, the importance and effectiveness of Wikipedia Mining has been shown in several researches. One popular research area on Wikipedia Mining focuses on semantic relatedness…
Abstract
Purpose
Recently, the importance and effectiveness of Wikipedia Mining has been shown in several researches. One popular research area on Wikipedia Mining focuses on semantic relatedness measurement, and research in this area has shown that Wikipedia can be used for semantic relatedness measurement. However, previous methods are facing two problems; accuracy and scalability. To solve these problems, the purpose of this paper is to propose an efficient semantic relatedness measurement method that leverages global statistical information of Wikipedia. Furthermore, a new test collection is constructed based on Wikipedia concepts for evaluating semantic relatedness measurement methods.
Design/methodology/approach
The authors' approach leverages global statistical information of the whole Wikipedia to compute semantic relatedness among concepts (disambiguated terms) by analyzing co‐occurrences of link pairs in all Wikipedia articles. In Wikipedia, an article represents a concept and a link to another article represents a semantic relation between these two concepts. Thus, the co‐occurrence of a link pair indicates the relatedness of a concept pair. Furthermore, the authors propose an integration method with tfidf as an improved method to additionally leverage local information in an article. Besides, for constructing a new test collection, the authors select a large number of concepts from Wikipedia. The relatedness of these concepts is judged by human test subjects.
Findings
An experiment was conducted for evaluating calculation cost and accuracy of each method. The experimental results show that the calculation cost of this approach is very low compared to one of the previous methods and more accurate than all previous methods for computing semantic relatedness.
Originality/value
This is the first proposal of co‐occurrence analysis of Wikipedia links for semantic relatedness measurement. The authors show that this approach is effective to measure semantic relatedness among concepts regarding calculation cost and accuracy. The findings may be useful to researchers who are interested in knowledge extraction, as well as ontology researches.
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Kaushik Ghosh and Prabir Kumar Das
This study aims to examine the characteristics of cross-border central bank digital currencies (CBDCs) while pinpointing research trends and adoption variables at both individual…
Abstract
Purpose
This study aims to examine the characteristics of cross-border central bank digital currencies (CBDCs) while pinpointing research trends and adoption variables at both individual and macroeconomic levels. Additionally, it delves into the impact of terminology within CBDC-related scholarly literature themes.
Design/methodology/approach
The authors perform a bibliometric study using the metadata of academic papers about CBDC from ScienceDirect, Scopus and Web of Science (WoS), three reputable research databases. Word maps are produced using VOSviewer, an open-source bibliometric analytics program, to find pertinent and predominate words and phrases based on their frequency, placement, connection and co-occurrence. Additionally, the authors use the R programing language to assess the Jaccard similarity between bibliometric metadata and the financial terms in the Loughran-McDonald Master Dictionary (LMMD).
Findings
The study pinpoints the factors that affect CBDC adoption at the micro and macroeconomic levels. Insights into prospective future study themes are provided by the analysis of the metadata corpus, which shows significant and predominate words/phrases and themes in CBDC literature. Notably, the relatively low Jaccard similarity scores in the scholarly literature on CBDC-related topics across all three bibliometric databases suggest a restricted concentration on financial issues. This shows that CBDC research is still in its early stages and that there are still many undiscovered financial aspects.
Originality/value
The identification of literature’s themes using dominant and pertinent words based on bibliometric metadata, considering factors such as frequency and co-occurrence, enriches the evolving field of meta-analysis. Additionally, the use of the Jaccard index to assess the coverage of financial terms within bibliometric metadata represents a unique approach, shedding light on the distinctive aspects of CBDC research.
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Yuzhuo Wang, Chengzhi Zhang, Min Song, Seongdeok Kim, Youngsoo Ko and Juhee Lee
In the era of artificial intelligence (AI), algorithms have gained unprecedented importance. Scientific studies have shown that algorithms are frequently mentioned in papers…
Abstract
Purpose
In the era of artificial intelligence (AI), algorithms have gained unprecedented importance. Scientific studies have shown that algorithms are frequently mentioned in papers, making mention frequency a classical indicator of their popularity and influence. However, contemporary methods for evaluating influence tend to focus solely on individual algorithms, disregarding the collective impact resulting from the interconnectedness of these algorithms, which can provide a new way to reveal their roles and importance within algorithm clusters. This paper aims to build the co-occurrence network of algorithms in the natural language processing field based on the full-text content of academic papers and analyze the academic influence of algorithms in the group based on the features of the network.
Design/methodology/approach
We use deep learning models to extract algorithm entities from articles and construct the whole, cumulative and annual co-occurrence networks. We first analyze the characteristics of algorithm networks and then use various centrality metrics to obtain the score and ranking of group influence for each algorithm in the whole domain and each year. Finally, we analyze the influence evolution of different representative algorithms.
Findings
The results indicate that algorithm networks also have the characteristics of complex networks, with tight connections between nodes developing over approximately four decades. For different algorithms, algorithms that are classic, high-performing and appear at the junctions of different eras can possess high popularity, control, central position and balanced influence in the network. As an algorithm gradually diminishes its sway within the group, it typically loses its core position first, followed by a dwindling association with other algorithms.
Originality/value
To the best of the authors’ knowledge, this paper is the first large-scale analysis of algorithm networks. The extensive temporal coverage, spanning over four decades of academic publications, ensures the depth and integrity of the network. Our results serve as a cornerstone for constructing multifaceted networks interlinking algorithms, scholars and tasks, facilitating future exploration of their scientific roles and semantic relations.
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Shelley Haines, Omar H. Fares, Myuri Mohan and Seung Hwan (Mark) Lee
This study aims to examine YouTube comments relevant to sustainable fashion posted on fashion haul videos over the past decade (2011–2021). It is guided by two research questions…
Abstract
Purpose
This study aims to examine YouTube comments relevant to sustainable fashion posted on fashion haul videos over the past decade (2011–2021). It is guided by two research questions: (1) How have sustainable fashion-related comments posted on YouTube fashion haul videos changed over time? and (2) What themes are relevant to sustainable fashion in the comments posted on fashion haul videos?
Design/methodology/approach
A data set of comments from 110 fashion haul videos posted on YouTube was refined to only include comments with keywords related to sustainable fashion. Leximancer, a machine learning technique, was employed to identify concepts within the data and co-occurrences between concepts. Linguistic Inquiry and Word Count software was employed to assess the prevalence of concepts and identify sentiment over time.
Findings
Over the decade, the authors identified increased comments and conversations relevant to sustainable fashion. For instance, conversations surrounding sustainable fashion were linked to “waste” and “addicted” between 2011 and 2013, which evolved to include “environment” and “clothes” between 2014 and 2016, to “buy” and “workers” between 2017 and 2019 and “sustainable” between 2020 and 2021, demonstrating the changes in conversation topics over time.
Practical implications
With increasing engagement from YouTube viewers on sustainable fashion, retail-affiliated content that promotes sustainable fashion is proposed as one approach to engage viewers and promote sustainable practices in the fashion industry, whereby content creators can partner with retailers to feature products and educate viewers on the benefits of sustainable fashion.
Originality/value
The findings suggest that consumers are becoming more aware of and responsive to sustainable fashion. The originality of this research stems from identifying the source of this interest.
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Rong Wang, Wenlin Liu and Shuyang Gao
The purpose of this paper is to conceptualize the use of Twitter hashtag as a strategy to enhance the visibility and symbolic power of social movement-related information. It…
Abstract
Purpose
The purpose of this paper is to conceptualize the use of Twitter hashtag as a strategy to enhance the visibility and symbolic power of social movement-related information. It examined how characteristics of hashtag drove information virality during a networked social movement.
Design/methodology/approach
Twitter data from two days during the Occupy Wall Street Movement in 2011 were collected. With network analysis, the authors identified popular hashtag types and examined hashtag co-occurrence patterns during the two contrasting movement days. It also provides a comparative analysis of how major types of viral hashtag may play different roles depending on different movement cycles.
Findings
The authors found that the role of hashtag influencing information virality may vary based on the context of the tweets. For example, movement participants applied more strategic hashtag combinations during the unexpected event day to reach different social circles. Consistent patterns were identified in mobilizing influential actors such as public figures. Different use patterns of media outlet hashtag were found across the two days.
Originality/value
Implications on how hashtag type and event dynamics may shape hashtag co-occurrence patterns were discussed.
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