AKMiner: Domain-Specific Knowledge Graph Mining from Academic Literatures

Existing academic search systems like Google Scholar usually return a long list of scientific articles for a given research domain or topic (e.g. “document summarization”, “information extraction”), and users need to read volumes of articles to get some ideas of the research progress for a domain, which is very tedious and time-consuming. In this paper, we propose a novel system called AKMiner (Academic Knowledge Miner) to automatically mine useful knowledge from the articles in a specific domain, and then visually present the knowledge graph to users. Our system consists of two major components: a) the extraction module which extracts academic concepts and relations jointly based on Markov Logic Network, and b) the visualization module which generates knowledge graphs, including concept-cloud graphs and concept relation graphs. Experimental results demonstrate the effectiveness of each component of our proposed system.