Relevant bibliographies by topics / Microsoft academic graph

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Academic literature on the topic 'Microsoft academic graph'

Author: Grafiati
Published: 5 June 2025
Last updated: 15 July 2025

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Journal articles on the topic "Microsoft academic graph"

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Wang, Kuansan, Zhihong Shen, Chiyuan Huang, Chieh-Han Wu, Yuxiao Dong, and Anshul Kanakia. "Microsoft Academic Graph: When experts are not enough." Quantitative Science Studies 1, no. 1 (2020): 396–413. http://dx.doi.org/10.1162/qss_a_00021.

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An ongoing project explores the extent to which artificial intelligence (AI), specifically in the areas of natural language processing and semantic reasoning, can be exploited to facilitate the studies of science by deploying software agents equipped with natural language understanding capabilities to read scholarly publications on the web. The knowledge extracted by these AI agents is organized into a heterogeneous graph, called Microsoft Academic Graph (MAG), where the nodes and the edges represent the entities engaging in scholarly communications and the relationships among them, respectively. The frequently updated data set and a few software tools central to the underlying AI components are distributed under an open data license for research and commercial applications. This paper describes the design, schema, and technical and business motivations behind MAG and elaborates how MAG can be used in analytics, search, and recommendation scenarios. How AI plays an important role in avoiding various biases and human induced errors in other data sets and how the technologies can be further improved in the future are also discussed.
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Färber, Michael, and Lin Ao. "The Microsoft Academic Knowledge Graph enhanced: Author name disambiguation, publication classification, and embeddings." Quantitative Science Studies 3, no. 1 (2022): 51–98. http://dx.doi.org/10.1162/qss_a_00183.

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Abstract Although several large knowledge graphs have been proposed in the scholarly field, such graphs are limited with respect to several data quality dimensions such as accuracy and coverage. In this article, we present methods for enhancing the Microsoft Academic Knowledge Graph (MAKG), a recently published large-scale knowledge graph containing metadata about scientific publications and associated authors, venues, and affiliations. Based on a qualitative analysis of the MAKG, we address three aspects. First, we adopt and evaluate unsupervised approaches for large-scale author name disambiguation. Second, we develop and evaluate methods for tagging publications by their discipline and by keywords, facilitating enhanced search and recommendation of publications and associated entities. Third, we compute and evaluate embeddings for all 239 million publications, 243 million authors, 49,000 journals, and 16,000 conference entities in the MAKG based on several state-of-the-art embedding techniques. Finally, we provide statistics for the updated MAKG. Our final MAKG is publicly available at https://makg.org and can be used for the search or recommendation of scholarly entities, as well as enhanced scientific impact quantification.
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Färber, Michael, and David Lamprecht. "The data set knowledge graph: Creating a linked open data source for data sets." Quantitative Science Studies 2, no. 4 (2021): 1324–55. http://dx.doi.org/10.1162/qss_a_00161.

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Abstract Several scholarly knowledge graphs have been proposed to model and analyze the academic landscape. However, although the number of data sets has increased remarkably in recent years, these knowledge graphs do not primarily focus on data sets but rather on associated entities such as publications. Moreover, publicly available data set knowledge graphs do not systematically contain links to the publications in which the data sets are mentioned. In this paper, we present an approach for constructing an RDF knowledge graph that fulfills these mentioned criteria. Our data set knowledge graph, DSKG, is publicly available at http://dskg.org and contains metadata of data sets for all scientific disciplines. To ensure high data quality of the DSKG, we first identify suitable raw data set collections for creating the DSKG. We then establish links between the data sets and publications modeled in the Microsoft Academic Knowledge Graph that mention these data sets. As the author names of data sets can be ambiguous, we develop and evaluate a method for author name disambiguation and enrich the knowledge graph with links to ORCID. Overall, our knowledge graph contains more than 2,000 data sets with associated properties, as well as 814,000 links to 635,000 scientific publications. It can be used for a variety of scenarios, facilitating advanced data set search systems and new ways of measuring and awarding the provisioning of data sets.
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Hajikhani, Arash, Lukas Pukelis, Arho Suominen, et al. "Connecting firm's web scraped textual content to body of science: Utilizing microsoft academic graph hierarchical topic modeling." MethodsX 9 (2022): 101650. http://dx.doi.org/10.1016/j.mex.2022.101650.

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Huang, Yong, Wei Lu, Jialin Liu, Qikai Cheng, and Yi Bu. "Towards transdisciplinary impact of scientific publications: A longitudinal, comprehensive, and large-scale analysis on Microsoft Academic Graph." Information Processing & Management 59, no. 2 (2022): 102859. http://dx.doi.org/10.1016/j.ipm.2021.102859.

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Angioni, Simone, Angelo Salatino, Francesco Osborne, Diego Reforgiato Recupero, and Enrico Motta. "AIDA: A knowledge graph about research dynamics in academia and industry." Quantitative Science Studies 2, no. 4 (2021): 1356–98. http://dx.doi.org/10.1162/qss_a_00162.

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Abstract Academia and industry share a complex, multifaceted, and symbiotic relationship. Analyzing the knowledge flow between them, understanding which directions have the biggest potential, and discovering the best strategies to harmonize their efforts is a critical task for several stakeholders. Research publications and patents are an ideal medium to analyze this space, but current data sets of scholarly data cannot be used for such a purpose because they lack a high-quality characterization of the relevant research topics and industrial sectors. In this paper, we introduce the Academia/Industry DynAmics (AIDA) Knowledge Graph, which describes 21 million publications and 8 million patents according to the research topics drawn from the Computer Science Ontology. 5.1 million publications and 5.6 million patents are further characterized according to the type of the author’s affiliations and 66 industrial sectors from the proposed Industrial Sectors Ontology (INDUSO). AIDA was generated by an automatic pipeline that integrates data from Microsoft Academic Graph, Dimensions, DBpedia, the Computer Science Ontology, and the Global Research Identifier Database. It is publicly available under CC BY 4.0 and can be downloaded as a dump or queried via a triplestore. We evaluated the different parts of the generation pipeline on a manually crafted gold standard yielding competitive results.
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Manghi, Paolo, Claudio Atzori, Michele De Bonis, and Alessia Bardi. "Entity deduplication in big data graphs for scholarly communication." Data Technologies and Applications 54, no. 4 (2020): 409–35. http://dx.doi.org/10.1108/dta-09-2019-0163.

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PurposeSeveral online services offer functionalities to access information from “big research graphs” (e.g. Google Scholar, OpenAIRE, Microsoft Academic Graph), which correlate scholarly/scientific communication entities such as publications, authors, datasets, organizations, projects, funders, etc. Depending on the target users, access can vary from search and browse content to the consumption of statistics for monitoring and provision of feedback. Such graphs are populated over time as aggregations of multiple sources and therefore suffer from major entity-duplication problems. Although deduplication of graphs is a known and actual problem, existing solutions are dedicated to specific scenarios, operate on flat collections, local topology-drive challenges and cannot therefore be re-used in other contexts.Design/methodology/approachThis work presents GDup, an integrated, scalable, general-purpose system that can be customized to address deduplication over arbitrary large information graphs. The paper presents its high-level architecture, its implementation as a service used within the OpenAIRE infrastructure system and reports numbers of real-case experiments.FindingsGDup provides the functionalities required to deliver a fully-fledged entity deduplication workflow over a generic input graph. The system offers out-of-the-box Ground Truth management, acquisition of feedback from data curators and algorithms for identifying and merging duplicates, to obtain an output disambiguated graph.Originality/valueTo our knowledge GDup is the only system in the literature that offers an integrated and general-purpose solution for the deduplication graphs, while targeting big data scalability issues. GDup is today one of the key modules of the OpenAIRE infrastructure production system, which monitors Open Science trends on behalf of the European Commission, National funders and institutions.
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Phan, Truong Ho Viet, Thanh Trung Ho, and Phuc Do. "Topic based object Influence analysis in Social networks." Science and Technology Development Journal 16, no. 4 (2013): 68–78. http://dx.doi.org/10.32508/stdj.v16i4.1586.

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In this article, we focus on researching models in social networks analysis and use them to develop the system which enables users to exactly find topics as well as necessary research of authors. In this research, we study Author – Conference – Topic (ACT) model to find out the topic relevant to users’ requirements. With each of these topics, the system will create a graph of Topical Affinity Propagation (TAP) in purpose of analyzing the topic based influence of authors. Experimental results on the corpus extracted from Microsoft Academic Research include 34,330 authors, 19,921 articles, 1,335 conference and 424,501 co-author relations.
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Gupta, Anamika, Shikha Gupta, Mukul Bisht, Prestha Hooda, and Md Salik. "Document Co-citation Analysis using Concept Lattice." Engineering, Technology & Applied Science Research 13, no. 5 (2023): 11837–42. http://dx.doi.org/10.48084/etasr.6201.

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Document Co-citation Analysis (DCA) is a method to identify and analyze the relationships between co-cited documents. In this paper, we attempt to use concept lattice for DCA. Concept lattice is a graph structure given in Formal Concept Analysis (FCA), a branch of mathematics based on the concept and its hierarchy. The experiments are conducted on an extensive repository of citations extracted from DBLP, ACM, MAG (Microsoft Academic Graph), and other sources, having a total of 5,354,309 papers and 48,227,950 citation relationships. In this paper, it is established that the concept lattice supports DCA and helps to identify a set of co-cited documents and their co-citation strength. It also provides navigation to reflect the subset-superset relationship of the co-citations. Further, the concept lattice helps identify the hierarchy among the documents and answers the most relevant queries related to DCA.
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Hoi, Huynh Tan. "FINDING OUT THE INFLUENCE OF TOPIC BASED OBJECT ACCORDING TO SOCIAL NETWORKS." INFORMATION TECHNOLOGY IN INDUSTRY 9, no. 1 (2021): 948–53. http://dx.doi.org/10.17762/itii.v9i1.222.

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In this research paper, the author mainly focuses on researching models in social networks analysis and then use them to develop the system which is able to allow users to search right topics as well as right research papers of other authors. In this paper, the author do research on Author – Conference –Topic model for the topic relevant to users’ requirements. With each of these topics, the system will create a graph
 of Topical Affinity Propagation (TAP) with the aim to analyze the topics based influence of authors. The results on the corpus extracted from Microsoft Academic Research include 34,330 authors, 19,921 articles, 1,335 conference and 424,501 co-author relations.
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Book chapters on the topic "Microsoft academic graph"

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Panagopoulos, George, Christos Xypolopoulos, Konstantinos Skianis, Christos Giatsidis, Jie Tang, and Michalis Vazirgiannis. "Scientometrics for Success and Influence in the Microsoft Academic Graph." In Complex Networks and Their Applications VIII. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-36683-4_80.

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Färber, Michael. "The Microsoft Academic Knowledge Graph: A Linked Data Source with 8 Billion Triples of Scholarly Data." In Lecture Notes in Computer Science. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-30796-7_8.

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Leite, Aline Chaves, and Myriam Angelica Dornelas. "Covid-19: Sudden changes in the academic landscape and their impacts on quality of life in teaching work." In EMERGING ISSUES RELATED TO THE CORONA VIRUS PANDEMIC (COVID 19). Seven Editora, 2023. http://dx.doi.org/10.56238/emerrelcovid19-041.

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Faced with the pandemic scenario and the consequent sudden changes in the academic environment, teachers were significantly impacted by the need to adapt their teaching strategies when they needed to transfer their work environment into their homes. Thus, it is important to study the QWL of teachers in order to understand how and how much they were impacted and what measures can be adopted to mitigate these effects, since QoL and QWL can influence the quality of the education offered. Thus, the present study aimed to analyze the Quality of Life at Work of teachers of a public educational institution in the face of a pandemic scenario. This research was classified as qualitative and descriptive. Data collection took place through the collection of primary data that occurred through field research with the application of a semi-structured questionnaire, whose questions dealt with the characteristics of the profile of the respondents and the impacts of the pandemic on the QWL of teachers. The tabulation of the data was done through the digital platforms Google Forms and Microsoft Excel 2013, where, through its resources, absolute numbers, percentages and the generation of graphs and tables were obtained. For qualitative research, content analysis was used. The partial results showed that the QWL of the professors surveyed was considerably impacted, especially due to the increased workload. It was concluded that the changes caused by the pandemic period negatively impacted the QWL of the respondents. The criteria of Work and total living space, working conditions and social integration in the organization stand out as the most impacted in the pandemic period. It is suggested, from the data collected, future studies on the investigated problem, for better understanding and mitigating suggestions.
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Conference papers on the topic "Microsoft academic graph"

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Qin, Hongwu, Juntao Zeng, and Xiuqin Ma. "Trend Analysis of Research Direction in Computer Science Based on Microsoft Academic Graph." In CONF-CDS 2021: The 2nd International Conference on Computing and Data Science. ACM, 2021. http://dx.doi.org/10.1145/3448734.3450470.

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Kovačič, Ivan, David Bajs, and Milan Ojsteršek. "Methodology for the Assessment of the Text Similarity of Documents in the CORE Open Access Data Set of Scholarly Documents." In 7th Student Computer Science Research Conference. University of Maribor Press, 2021. http://dx.doi.org/10.18690/978-961-286-516-0.12.

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This paper describes the methodology of data preparation and analysis of the text similarity required for plagiarism detection on the CORE data set. Firstly, we used the CrossREF API and Microsoft Academic Graph data set for metadata enrichment and elimination of duplicates of doc-uments from the CORE 2018 data set. In the second step, we used 4-gram sequences of words from every document and transformed them into SHA-256 hash values. Features retrieved using hashing algorithm are compared, and the result is a list of documents and the percentages of cov-erage between pairs of documents features. In the third step, called pairwise feature-based ex-haustive analysis, pairs of documents are checked using the longest common substring.
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