A better understanding of what motivates humans to perform certain actions is relevant for a range of research challenges including generating action sequences that implement goals (planning). A first step in this direction is the task of acquiring knowledge about human goals. In this work, we investigate whether Search Query Logs are a viable source for extracting expressions of human goals. For this purpose, we devise an algorithm that automatically identifies queries containing explicit goals such as find home to rent in Florida. Evaluation results of our algorithm achieve useful precision/recall values. We apply the classification algorithm to two large Search Query Logs, recorded by AOL and Microsoft Research in 2006, and obtain a set of ∼110.000 queries containing explicit goals. To study the nature of human goals in Search Query Logs, we conduct qualitative, quantitative and comparative analyses. Our findings suggest that Search Query Logs (i) represent a viable source for extracting human goals, (ii) contain a great variety of human goals and (iii) contain human goals that can be employed to complement existing commonsense knowledge bases. Finally, we illustrate the potential of goal knowledge for addressing following application scenario: to refine and extend commonsense knowledge with human goals from Search Query Logs. This work is relevant for (i) knowledge engineers interested in acquiring human goals from textual corpora and constructing knowledge bases of human goals (ii) researchers interested in studying characteristics of human goals in Search Query Logs.

Access to knowledge about common human goals has been found critical for realizing the vision of intelligent agents acting upon user intent on the web. Yet, the acquisition of knowledge about common human goals represents a major challenge. In a departure from existing approaches, this paper investigates a novel resource for knowledge acquisition: The utilization of search query logs for this task. By relating goals contained in search query logs with goals contained in existing commonsense knowledge bases such as ConceptNet, we aim to shed light on the usefulness of search query logs for capturing knowledge about common human goals. The main contribution of this paper consists of insights generated from an empirical study comparing common human goals contained in two large search query logs (AOL and Microsoft Research) with goals contained in the commonsense knowledge base ConceptNet. The paper sketches ways how goals from search query logs could be used to address the goal acquisition and goal coverage problem related to commonsense knowledge bases.

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Access to knowledge about user goals represents a critical component for realizing the vision of intelligent agents acting upon user intent on the web. Yet, the acquisition of knowledge about user goals represents a major challenge. In a departure from existing approaches, this paper proposes a novel perspective for knowledge acquisition: The utilization of search query logs for this task. The research presented in this paper makes the following contributions: (a) it presents an automatic method for the acquisition of user goals from search query logs with useful precision/recall scores (b) it provides insights into the nature and some characteristics of these goals and (c) it shows that the goals acquired from query logs exhibit traits of a long tail distribution, thereby providing access to a broad range of user goals. We conclude that our work has implications for open research problems such as common sense knowledge acquisition and studies of user intent in query logs.