Microsoft Academic Search – http://academic.research.microsoft.com

Hands, Africa S.

doi:10.1080/07317131.2012.682026

Cited by 9 publications

(4 citation statements)

References 0 publications

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“…It is important to highlight some significant differences between DataMed and broad harvesting of web-based data sets. DataMed is not the equivalent of Google Scholar 6 or Microsoft Academic 7 for data.…”

Section: Starting the Data Discovery Journeymentioning

confidence: 99%

Finding useful data across multiple biomedical data repositories using DataMed

et al. 2017

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The value of broadening searches for data across multiple repositories has been identified by the biomedical research community. As part of the US National Institutes of Health (NIH) Big Data to Knowledge initiative, we work with an international community of researchers, service providers and knowledge experts to develop and test a data index and search engine, which are based on metadata extracted from various data sets in a range of repositories. DataMed is designed to be, for data, what PubMed has been for the scientific literature. DataMed supports the findability and accessibility of data sets. These characteristics—along with interoperability and reusability—compose the four FAIR principles to facilitate knowledge discovery in today’s big data–intensive science landscape.

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Section: Starting the Data Discovery Journeymentioning

confidence: 99%

Finding useful data across multiple biomedical data repositories using DataMed

et al. 2017

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“…Our methods are evaluated on a collection of topic-labelled learning resources by simulating an e-Learning recommendation task. We use a collection from Microsoft Academic Search (MAS) (Hands, 2012), in which the author-defined keywords associated with each paper identifies the topics they contain. The keywords represent what relevance would mean in an e-Learning domain and we exploit them for judging document relevance.…”

Section: Evaluating Learning Resource Representationmentioning

confidence: 99%

Improving e‐learning recommendation by using background knowledge

2018

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“…In GS, the profiles are created voluntarily and the authors can freely edit and modify their profiles, on the other hand, in MA, the profiles are automatically generated but authors can perform restricted editing on their profiles such as merging or suggesting changes (Ortega & Aguillo, 2014). MA aims to not only help researchers find scholarly articles online, but also to help them discover relationships between authors and organizations (Hands, 2012). MA enables researchers to see the top authors, publications, and journals of a specific scientific domain (Harzing, 2016).…”

Section: Introductionmentioning

confidence: 99%

“…In addition, it provides visualizations using Microsoft Academic Graph which shows publications, citations among publications, authors, and relations of authors to institutions, publication venues, and research fields (Microsoft, 2017b). The co-author graph and co-author path offered by MA can be a valuable tool for analyzing collaboration in research (Hands, 2012).…”

Section: Introductionmentioning

confidence: 99%

Implementing Recommendation Algorithms in a Large-Scale Biomedical Science Knowledge Base

Perrie¹,

Hao²,

Hayat³

et al. 2017

Preprint

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The number of biomedical research articles published has doubled in the past 20 years. Search engine based systems naturally center around searching, but researchers may not have a clear goal in mind, or the goal may be expressed in a query that a literature search engine cannot easily answer, such as identifying the most prominent authors in a given field of research. The discovery process can be improved by providing researchers with recommendations for relevant papers or for researchers who are dealing with related bodies of work. In this paper we describe several recommendation algorithms that were implemented in the Meta platform. The Meta platform contains over 27 million articles and continues to grow daily. It provides an online map of science that organizes, in real time, all published biomedical research. The ultimate goal is to make it quicker and easier for researchers to: (a) filter through scientific papers, (b) find the most important work, and (c) keep up with emerging research results. Meta generates and maintains a semantic knowledge network consisting of five different core entities: authors, papers, journals, institutions, and concepts (fields). As papers are published, the Meta data science platform detects, disambiguates and organizes the mentions of the core entities in a given paper thereby integrating new papers into its knowledge network. We implemented several recommendation algorithms and evaluated their efficiency in this large-scale biomedical knowledge base. We selected recommendation algorithms that could take advantage of the unique environment of the Meta platform such as those that make use of diverse datasets such as a citation networks, text content, semantic tag content, and co-authorship information and those that can scale to very large datasets. In this paper, we describe the recommendation algorithms that were implemented and report on their relative efficiency and the challenges associated with developing and deploying a production recommendation engine system. Related WorkMajor online scientific databases that are currently in use by biomedical researchers are PubMed, Google Scholar (GS), Web of Science (WoS), Scopus, Microsoft Academic (MA), Semantic Scholar (S2), and Meta. PubMed is a free online resource developed and maintained by the National Centre for Biotechnology Information (NCBI) in the United States (Canese & Weis, 2013; NCBI, 2017). It comprises over 27 million references from the MEDLINE database, in addition to other life science journals and online books (NIH, 2017). PubMed is mostly focused on medicine and biomedical literature whereas the other resources described below include various scientific fields (Falagas et al., 2008). It provides search filters that help trim the search results to a specific clinical study or specific topic. It also provides approximately 50 search fields and tags (e.g., first author name, publisher, title, etc.) (NCBI, 2017). Search results in PubMed can be sorted based on different criteria such as publication date or releva...

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Microsoft Academic Search – http://academic.research.microsoft.com

Cited by 9 publications

References 0 publications

Finding useful data across multiple biomedical data repositories using DataMed

Finding useful data across multiple biomedical data repositories using DataMed

Improving e‐learning recommendation by using background knowledge

Implementing Recommendation Algorithms in a Large-Scale Biomedical Science Knowledge Base

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