%0 Conference Paper %1 Zhang_2020 %A Zhang, Jinsong %A Guo, Chun %A Liu, Xiaozhong %B Proceedings of the ACM/IEEE Joint Conference on Digital Libraries in 2020 %D 2020 %I ACM %K academic-reference jcdl2020 knowledge-graph knowledge-map %P 187-196 %R 10.1145/3383583.3398521 %T Characterize and Evaluate Scientific Domain and Domain Context Knowledge Map %U https://doi.org/10.1145%2F3383583.3398521 %X Domain knowledge map, a.k.a., scholarly network, construction as an important method can describe the significant characters of a selected domain. In this research, we will address three fundamental problems for scholarly network generation. Firstly, two different methods will be investigated to associate keywords on the graph: Co-occur Domain Distance and Citation Probability Distribution Distance. Secondly, this paper will construct domain (core journals and conference proceedings) knowledge and domain referral (domain citation) scholarly networks, and propose a novel method to integrate those graphs by optimizing the nodes and their linkage. Finally, the paper will propose an innovative method to evaluate the accuracy and coverage of scholarly networks based on training keyword oriented Labeled-LDA model and validate different domain or domain referral graphs.

@inproceedings{Zhang_2020, abstract = {Domain knowledge map, a.k.a., scholarly network, construction as an important method can describe the significant characters of a selected domain. In this research, we will address three fundamental problems for scholarly network generation. Firstly, two different methods will be investigated to associate keywords on the graph: Co-occur Domain Distance and Citation Probability Distribution Distance. Secondly, this paper will construct domain (core journals and conference proceedings) knowledge and domain referral (domain citation) scholarly networks, and propose a novel method to integrate those graphs by optimizing the nodes and their linkage. Finally, the paper will propose an innovative method to evaluate the accuracy and coverage of scholarly networks based on training keyword oriented Labeled-LDA model and validate different domain or domain referral graphs. }, added-at = {2020-11-23T03:43:05.000+0100}, author = {Zhang, Jinsong and Guo, Chun and Liu, Xiaozhong}, biburl = {https://www.bibsonomy.org/bibtex/299c8fa8e7c0e05b88b029879be750751/brusilovsky}, booktitle = {Proceedings of the {ACM}/{IEEE} Joint Conference on Digital Libraries in 2020}, description = {Characterize and Evaluate Scientific Domain and Domain Context Knowledge Map | Proceedings of the ACM/IEEE Joint Conference on Digital Libraries in 2020}, doi = {10.1145/3383583.3398521}, interhash = {c7936ec27c115ab5ca14148d432ff711}, intrahash = {99c8fa8e7c0e05b88b029879be750751}, keywords = {academic-reference jcdl2020 knowledge-graph knowledge-map}, month = aug, pages = {187-196}, publisher = {{ACM}}, timestamp = {2020-11-23T03:43:05.000+0100}, title = {Characterize and Evaluate Scientific Domain and Domain Context Knowledge Map}, url = {https://doi.org/10.1145%2F3383583.3398521}, year = 2020 }