Automatically Constructing Concept Hierarchies of Health-Related Human Goals

To realize the vision of intelligent agents on the web, agents need to be capable of understanding people's behavior. Such an understanding would enable them to better predict and support human activities on the web. If agents had access to knowledge about human goals, they could, for instance, recognize people's goals from their actions or reason about people's goals. In this work, we study to what extent it is feasible to automatically construct concept hierarchies of domain-specific human goals. This process consists of the following two steps: (1) extracting human goal instances from a search query log and (2) inferring hierarchical structures by applying clustering techniques. To compare resulting concept hierarchies, we manually construct a golden standard and calculate taxonomic overlaps. In our experiments, we achieve taxonomic overlaps of up to ~51% for the health domain and up to ~60% for individual health subdomains. In an illustration scenario, we provide a prototypical implementation to automatically complement goal concept hierarchies by means-ends relations, i.e. relating goals to actions which potentially contribute to their accomplishment. Our findings are particularly relevant for knowledge engineers interested in (i) acquiring knowledge about human goals as well as (ii) automating the process of constructing goal concept hierarchies.