%0 Journal Article %1 shahid_insights_2019 %A Shahid, Abdul %A Afzal, Muhammad Tanvir %A Abdar, Moloud %A Basiri, Mohammad Ehsan %A Zhou, Xujuan %A Yen, Neil Y. %A Chang, Jia-Wei %D 2019 %J The Journal of Supercomputing %K terminologieextraktion %R 10.1007/s11227-019-03009-y %T Insights into relevant knowledge extraction techniques : a comprehensive review %U https://doi.org/10.1007/s11227-019-03009-y %X More than 50 million journal papers will have been published by the end of 2019 with 2 million more journal papers published every year. The number of conference papers is even higher, and millions of other types of scientific research are added to the knowledge base every year. Scientific databases such as Web of Science, Scopus, and PubMed index millions of scientific papers and Google Scholar indexes a huge amount of scientific knowledge across diverse domains. However, current systems provide long lists of results when users attempt to find relevant papers, leaving them with little choice other than manually skimming through the lists. This article surveys different techniques used to identify relevant research papers by knowledge-based organizations. We categorized current literature content as content, metadata, collaborative filtering, and citation based techniques and identified the strengths and limitation for each approach. Further, we evaluated the published techniques and research-based products used to identify relevant documents and identified the strengths and limitations of each approach. This research will greatly help to understand current state-of-the-art techniques internal workings for finding relevant papers, understand the relevant strengths and limitations, and explore previously proposed techniques targeting this area.

@article{shahid_insights_2019, abstract = {More than 50 million journal papers will have been published by the end of 2019 with 2 million more journal papers published every year. The number of conference papers is even higher, and millions of other types of scientific research are added to the knowledge base every year. Scientific databases such as Web of Science, Scopus, and PubMed index millions of scientific papers and Google Scholar indexes a huge amount of scientific knowledge across diverse domains. However, current systems provide long lists of results when users attempt to find relevant papers, leaving them with little choice other than manually skimming through the lists. This article surveys different techniques used to identify relevant research papers by knowledge-based organizations. We categorized current literature content as content, metadata, collaborative filtering, and citation based techniques and identified the strengths and limitation for each approach. Further, we evaluated the published techniques and research-based products used to identify relevant documents and identified the strengths and limitations of each approach. This research will greatly help to understand current state-of-the-art techniques internal workings for finding relevant papers, understand the relevant strengths and limitations, and explore previously proposed techniques targeting this area.}, added-at = {2019-10-14T17:29:00.000+0200}, author = {Shahid, Abdul and Afzal, Muhammad Tanvir and Abdar, Moloud and Basiri, Mohammad Ehsan and Zhou, Xujuan and Yen, Neil Y. and Chang, Jia-Wei}, biburl = {https://www.bibsonomy.org/bibtex/2e08756821ed4a130e872bdd11df99474/lepsky}, doi = {10.1007/s11227-019-03009-y}, interhash = {349cf0b9f286553e235f5dc6d56edc4c}, intrahash = {e08756821ed4a130e872bdd11df99474}, issn = {1573-0484}, journal = {The Journal of Supercomputing}, keywords = {terminologieextraktion}, language = {en}, month = oct, shorttitle = {Insights into relevant knowledge extraction techniques}, timestamp = {2019-10-14T17:29:00.000+0200}, title = {Insights into relevant knowledge extraction techniques : a comprehensive review}, url = {https://doi.org/10.1007/s11227-019-03009-y}, urldate = {2019-10-13}, year = 2019 }