Acquiring Explicit User Goals from Search Query Logs

Strohmaier, Markus; Prettenhofer, Peter; Kroll, Mark W.

doi:10.1109/wiiat.2008.364

Cited by 8 publications

(14 citation statements)

References 7 publications

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“…Results from a larger human subject study corroborate the existence of these two classes and furthermore hint towards a theoretical separability (Strohmaier et al, 2008). To tap into Search Query Logs for knowledge acquisition purposes, we propose to automatically identify and extract queries which contain explicit goals.…”

Section: Queries Containing Explicit Goalsmentioning

confidence: 62%

“…We refer to the task of acquiring goals from textual resources as Goal Mining. This problem covers a broad range of interesting aspects, including the acquisition of goals from scientific articles (Hui & Yu, 2005), organizational policies (Potts et al, 1994), organizational guidelines and procedures (Liaskos et al, 2006), Search Query Logs (Strohmaier et al, 2008) and others.…”

Section: Commonsense Knowledge Acquisitionmentioning

confidence: 99%

“…A counterexample is represented by the query living on the moon. It is important to note that it would be rather difficult to completely verify this assessment solely based on data from an anonymous query log due to the inherent goal verification problem of such a task (Strohmaier et al, 2008). However, the objectives of our work are more modest: we are interested in acquiring plausible human goals for knowledge acquisition purposes.…”

Section: Rq 01: Do Queries Containing Explicit Goals Exist In Search mentioning

confidence: 99%

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Acquiring knowledge about human goals from Search Query Logs

Strohmaier

Kroll

2012

Information Processing & Management

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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.

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Section: Queries Containing Explicit Goalsmentioning

confidence: 62%

Section: Commonsense Knowledge Acquisitionmentioning

confidence: 99%

Section: Rq 01: Do Queries Containing Explicit Goals Exist In Search mentioning

confidence: 99%

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Acquiring knowledge about human goals from Search Query Logs

Strohmaier

Kroll

2012

Information Processing & Management

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“…Moreover, goal search can take advantage of previous queries that the user placed on the search engine. Some interesting work on this topic has already been done by M. Strohmaier and his colleagues [41,79].…”

Section: Discussion and Challengesmentioning

confidence: 99%

PWWM: A Personal Web Workflow Methodology

Chećhik

Simmonds

Liaskos

et al. 2013

Lecture Notes in Computer Science

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Abstract. The personal web vision promises to give users a highly personalized experience on the web. This paper proposes and describes a Personal Web Workflow Methodology, designed to elicit, operationalize and execute a personal web user's goals. Our approach relies heavily on our prior research in goal modeling and operationalization, model matching and merging, and web service monitoring and recovery. We integrate this research with the social networking concept of crowd-sourcing to create a novel methodology for allowing users to produce customized workflows in order to accomplish their unique goals.

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“…The resulting set has an estimated precision of 77% of explicit intentional queries (based on the evaluations reported in [25]) and represents our knowledge base for Intentional Query Suggestion. We call this subset of queries the Explicit Intentional Query Dataset from here on.…”

Section: Datasetsmentioning

confidence: 99%

Intentional query suggestion

Strohmaier

Kroll

Körner

2009

Proceedings of the 2009 Workshop on Web Search Click Data

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The degree to which users' make their search intent explicit can be assumed to represent an upper bound on the level of service that search engines can provide. In a departure from traditional query expansion mechanisms, we introduce Intentional Query Suggestion as a novel idea that is attempting to make users' intent more explicit during search. In this paper, we present a prototypical algorithm for Intentional Query Suggestion and we discuss corresponding data from comparative experiments with traditional query suggestion mechanisms. Our preliminary results indicate that intentional query suggestions 1) diversify search result sets (i.e. it reduces result set overlap) and 2) have the potential to yield higher click-through rates than traditional query suggestions.

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Acquiring Explicit User Goals from Search Query Logs

Cited by 8 publications

References 7 publications

Acquiring knowledge about human goals from Search Query Logs

Acquiring knowledge about human goals from Search Query Logs

PWWM: A Personal Web Workflow Methodology

Intentional query suggestion

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