Large-scale analysis of query logs to profile users for dataset search

Sharifpour, Romina; Wu, Mingfang; Zhang, Xiuzhen

doi:10.1108/jd-12-2021-0245

Cited by 7 publications

(4 citation statements)

References 51 publications

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“…Their experimental results showed that proficient searchers built task outcomes with higher quality. Similar observations were also made in [42] by J Mao et al In addition to the quality of task outcome, the relation between searchers' proficiency and other search behaviors was also investigated in [43][44] [45]. The search behaviors investigated in these studies include the amount of query words, the length of sessions, the quality of query words, and searchers' judgement on the relevance of search results.…”

Section: H Comparasions With Other Approachesmentioning

confidence: 74%

A Probabilistic Model Toward How People Search to Build Outcomes

et al. 2023

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Although increased attention is being given to understanding how people search to build task outcomes, a formal model of the relation between how people search and how people build task outcomes is still lacking. This paper proposes a unified probabilistic model of how people search to build outcomes. The model involves 3 types of searcher behaviors (i.e., query submission, document selection, and information transformation) to model the effect of the information collected during search, and uses the item response theory to capture the ternary relations between the ability to transform information, the information collected, and the probability of successfully building task outcomes. We evaluate the proposed model in the task of identifying searchers' proficiencies under the assumption that high proficiency searchers would have high abilities to transform information. The results obtained high accuracies and F1 scores, which could reflect the effectiveness of the proposed model. The model contributes to the formal understanding of how people search to build task outcomes, and provides new possibilities for personalized and session-based information retrieval research.INDEX TERMS Item response theory, searching as learning, outcomes.

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Section: H Comparasions With Other Approachesmentioning

confidence: 74%

A Probabilistic Model Toward How People Search to Build Outcomes

et al. 2023

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“…Our research methodology is user-centric and we analyze systems based on the outcomes and benefits of ELK and GR for decision makers. Understanding users' dataset behavior is essential to providing effective data discovery services [12]. From the search for predecessors, the analysis of log files has developed a variety of methods that efficiently determine contextualized answers to user needs.…”

Section: Research Methodology and Approachmentioning

confidence: 99%

Elastic Stack and GRAPHYP Knowledge Graph of Web Usage: A Win–Win Workflow for Semantic Interoperability in Decision Making

et al. 2023

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The use of Elastic Stack (ELK) solutions and Knowledge Graphs (KGs) has attracted a lot of attention lately, with promises of vastly improving business performance based on new business insights and better decisions. This allows organizations not only to reap the ultimate benefits of data governance but also to consider the widest possible range of relevant information when deciding their next steps. In this paper, we examine how data management and data visualization are used in organizations that use ELK solutions to collect integrated data from different sources in one place and visualize and analyze them in near-real time. We also present some interpretable Knowledge Graphs, GRAPHYP, which are innovative by processing an analytical information geometry and can be used together with an ELK to improve data quality and visualize the data to make informed decisions in organizations. Good decisions are the backbone of successful organizations. Ultimately, this research is about integrating a combined solution between ELK and SKG GRAPHYP and showing users the advantages in this area.

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“…We chose these query-level features based on prior findings by Kathuria et al (2010), who defined query intent using query-level features, such as query length and reformulation strategy. We also based our feature selections on work by Sharifpour et al (2022), who proposed distinct user groups by performing hierarchical clustering on query logs (2022). We selected these categories based on their relevance to differentiating user behavior and profiling users based on their web queries.…”

Section: Feature Selectionmentioning

confidence: 99%

Exploratory and directed search strategies at a social science data archive

Lafia,

Million,

Hemphill

2024

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Researchers need to be able to find, access, and use data to participate in open science. To understand how users search for research data, we analyzed textual queries issued at a large social science data archive, the Inter-university Consortium for Political and Social Research (ICPSR). We collected unique user queries from 988,475 user search sessions over four years (2012-16). Overall, we found that only 30% of site visitors entered search terms into the ICPSR website. We analyzed search strategies within these sessions by extending existing dataset search taxonomies to classify a subset of the 1,554 most popular queries. We identified five categories of commonly-issued queries: keyword-based (e.g., date, place, topic); name (e.g., study, series); identifier (e.g., study, series); author (e.g., institutional, individual); and type (e.g., file, format). While the dominant search strategy used short keywords to explore topics, directed searches for known items using study and series names were also common. We further distinguished exploratory browsing from directed search queries based on their page views, refinements, search depth, duration, and length. Directed queries were longer (i.e., they had more words), while sessions with exploratory queries had more refinements and associated page views. By comparing search interactions at ICPSR to other natural language interactions in similar web search contexts, we conclude that dataset search at ICPSR is underutilized. We envision how alternative search paradigms, such as those enabled by recommender systems, can enhance dataset search.

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Large-scale analysis of query logs to profile users for dataset search

Cited by 7 publications

References 51 publications

A Probabilistic Model Toward How People Search to Build Outcomes

A Probabilistic Model Toward How People Search to Build Outcomes

Elastic Stack and GRAPHYP Knowledge Graph of Web Usage: A Win–Win Workflow for Semantic Interoperability in Decision Making

Exploratory and directed search strategies at a social science data archive

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