K3S: Knowledge-Driven Solution Support System


  • Yu Zhang University of New South Wales, Canberra
  • Morteza Saberi University of New South Wales, Canberra
  • Min Wang University of New South Wales, Canberra
  • Elizabeth Chang University of New South Wales, Canberra




As the volume of scientific papers grows rapidly in size, knowledge management for scientific publications is greatly needed. Information extraction and knowledge fusion techniques have been proposed to obtain information from scholarly publications and build knowledge repositories. However, retrieving the knowledge of problem/solution from academic papers to support users on solving specific research problems is rarely seen in the state of the art. Therefore, to remedy this gap, a knowledge-driven solution support system (K3S) is proposed in this paper to extract the information of research problems and proposed solutions from academic papers, and integrate them into knowledge maps. With the bibliometric information of the papers, K3S is capable of providing recommended solutions for any extracted problems. The subject of intrusion detection is chosen for demonstration in which required information is extracted with high accuracy, a knowledge map is constructed properly, and solutions to address intrusion problems are recommended.




How to Cite

Zhang, Y., Saberi, M., Wang, M., & Chang, E. (2019). K3S: Knowledge-Driven Solution Support System. Proceedings of the AAAI Conference on Artificial Intelligence, 33(01), 9873-9874. https://doi.org/10.1609/aaai.v33i01.33019873



Demonstration Track