Unified Workbench for Knowledge Graph Management

Unified Workbench for Knowledge Graph Management


Knowledge Graphs become essential knowledge resources for AI applications. Nevertheless, massive real-world knowledge is produced every day. Ignoring new knowledge greatly affects the outcomes of an application due to missing or inaccurate knowledge. To deal with the new knowledge and its life cycle, the holistic framework for curating and manipulating knowledge is needed. We present our solution, namely Unified Workbench for Knowledge Graph Management (UWKGM), that unifies several technologies to deal with knowledge curation and manipulation in a knowledge graph.

UWKGM consists of four main components: 1) Relation Extraction (RE), 2) Ontology Integration (OI), 3) Knowledge Verification (KV) and 4) Knowledge Completion (KC). RE and OI aim to solve the adding new knowledge problem. KV is to deal with the erroneous knowledge injection problem. KC copes with the inadequate knowledge problem.

For more information, read our published article.


Based on the concept above, we developed a knowledge graph management platform (UWKGM). Our platform enables users to integrate arbitrary functionalities as RESTful API services in order to facilitate the knowledge graph development process. It consists of three main components: the backend (API), the frontend (UI), and the system manager. The backend offers a wide range of functionalities for KG management based on the above concept, while also serves as an API endpoint for the UI and client-side extensions installed to the frontend. Our design principle is to facilitate contributions from communities of researchers and developers in the form of software packages distributed to the platform administrators and end-users. These packages are pluggable UI, API, or hybrid components operated by the platform’s package manager.

Full article will be available soon in Proceedings of the 29th ACM International Conference on Information and Knowledge Management (CIKM ’20).


Knowledge graph visualization. In the following demo, we demonstrate the walkthrough how an end-user navigate and explore the entities in KGs. The user adds four entities to the visualization tool and studies their connections.

Japanese COVID-19 case study. Japanese COVID-19 is a dataset relating to the situation of COVID-19 in Japan. We select this dataset to build a KG to organize the situation of COVID-19 in Japan with the KG. One of the prominent characteristics of this knowledge graph is a daily update of the data. To build this KG, we initialize the graph from the SIGNATE COVID-19 dataset.

Live demo. Visit our website for live demo. (Currently, we do not allow a user to create a new account on the website due to the resource management problem. Pleasecontact us, if you would like to get a tester account)


Download and install the platform from our GitHub repository.


Rungsiman Nararatwong: mail address
Natthawut Kertkeidkachorn: mail address
Ryutaro Ichise: mail address


  1. Natthawut Kertkeidkachorn, Rungsiman Nararatwong, and Ryutaro Ichise. "UWKGM: A Modular Platform for Knowledge Graph Management." In Proceedings of the 29th ACM International Conference on Information and Knowledge Management (CIKM ’20), 2020. [Demo]
  2. Natthawut Kertkeidkachorn and Ryutaro Ichise. "An Automatic Knowledge Graph Creation Framework from Natural Language Text." IEICE TRANSACTIONS on Information and Systems 101, no. 1 pp. 90-98. 2018.
  3. Ryutaro Ichise, Natthawut Kertkeidkachorn, Lihua Zhao, and Esrat Farjana Rupu. "Unified Workbench for Knowledge Graph Management." In 21st International Conference on Knowledge Engineering and Knowledge Management (EKAW; Posters & Demos), pp. 45-48. 2018. [Demo]
  4. Piyawat Lertvittayakumjorn, Natthawut Kertkeidkachorn, and Ryutaro Ichise. "Resolving range violations in DBpedia." In Proceedings of the 7th Joint International Semantic Technology Conference, pp. 121-137, LNCS, Vol. 10675, Springer, 2017.
  5. Natthawut Kertkeidkachorn and Ryutaro Ichise. "T2KG: An end-to-end system for creating knowledge graph from unstructured text." In Proceedings of AAAI Workshop on Knowledge-based Techniques for Problem Solving and Reasoning, pp. 743-749. 2017.