IR based Task-Model Learning: Automating the hierarchical structuring of tasks

Article type: Research Article

Authors: Fukazawa, Yusuke^{a; *} | Kröll, Mark^b | Strohmaier, Markus^c | Ota, Jun^a

Affiliations: [a] Research into Artifacts, Center for Engineering, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba, Japan | [b] Know-Center GmbH, Inffeldgasse 13, 8010 Graz, Austria | [c] GESIS Leibniz Institute for the Social Sciences, Cologne, Germany

Correspondence: [*] Corresponding author. E-mail: [email protected].

Abstract: Task-models concretize general requests to support users in real-world scenarios. In this paper, we present an IR based algorithm (IRTML) to automate the construction of hierarchically structured task-models. In contrast to other approaches, our algorithm is capable of assigning general tasks closer to the top and specific tasks closer to the bottom. Connections between tasks are established by extending Turney’s PMI-IR measure. To evaluate our algorithm, we manually created a ground truth in the health-care domain consisting of 14 domains. We compared the IRTML algorithm to three state-of-the-art algorithms to generate hierarchical structures, i.e. BiSection K-means, Formal Concept Analysis and Bottom-Up Clustering. Our results show that IRTML achieves a 25.9% taxonomic overlap with the ground truth, a 32.0% improvement over the compared algorithms.

Keywords: Human task, task model, taxonomic structure, pointwise mutual information, health care domain

DOI: 10.3233/WEB-160330

Journal: Web Intelligence, vol. 14, no. 1, pp. 31-41, 2016