Abstract. 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.

Since more and more people use the micro-blogging platform Twitter to convey their needs and desires, it has become a particularly interesting medium for the task of identifying commercial activities. Potential buyers and sellers can be contacted directly thereby opening up novel perspectives and economic possibilities. By detecting commercial intent in tweets, this work is considered a first step to bring together buyers and sellers. In this work, we present an automatic method for detecting commercial intent in tweets where we achieve reasonable precision 57 % and recall 77 % scores. In addition, we provide insights into the nature and characteristics of tweets exhibiting commercial intent thereby contributing to our understanding of how people express commercial activities on Twitter.

Hot trends are likely to bring new business opportunities. For example, “Air Pollution” might lead to a significant increase of the sales of related products, e.g., mouth mask. For e-commerce companies, it is very important to make rapid and correct response to these hot trends in order to improve product sales. In this paper, we take the initiative to study the task of how to identify trend related products. The major novelty of our work is that we au-tomatically learn commercial intents revealed from microblogs. We carefully construct a da-ta collection for this task and present quite a few insightful findings. In order to solve this problem, we further propose a graph based method, which jointly models relevance and associativity. We perform extensive experi-ments and the results showed that our methods are very effective. 1