A Query Log Analysis of Dataset Search

Kacprzak, Emilia; Koesten, Laura; Ibáñez, Luis‐Daniel; Simperl, Elena; Tennison, Jeni

doi:10.1007/978-3-319-60131-1_29

Cited by 31 publications

(26 citation statements)

References 17 publications

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“…Kacprzak et al [43] echo this idea, noting that existing data search applications are often based on preliminary or anecdotal evidence. Query log analyses can help to develop insights about data search practices [43], but log data cannot explain the reasoning behind search behaviours [44].…”

Section: Bibliometric Study and Analysis – Methods Imentioning

confidence: 99%

Understanding data search as a socio-technical practice

Gregory

Cousijn

Groth

et al. 2019

Journal of Information Science

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Open research data are heralded as having the potential to increase effectiveness, productivity, and reproducibility in science, but little is known about the actual practices involved in data search. The socio-technical problem of locating data for reuse is often reduced to the technological dimension of designing data search systems. We combine a bibliometric study of the current academic discourse around data search with interviews with data seekers. In this article, we explore how adopting a contextual, socio-technical perspective can help to understand user practices and behavior and ultimately help to improve the design of data discovery systems.

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Section: Bibliometric Study and Analysis – Methods Imentioning

confidence: 99%

Understanding data search as a socio-technical practice

Gregory

Cousijn

Groth

et al. 2019

Journal of Information Science

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show abstract

“…per day, week or month) [43]. Table 1: Metrics from web search studies used in this study DGU, AUS and CAN), which bases its search functionality on the Solr search platform 21 . Search functionality is provided through a search box in the portal.…”

Section: Metrics Usedmentioning

confidence: 99%

Characterising Dataset Search – An Analysis of Search Logs and Data Requests

et al. 2018

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Large amounts of data are becoming increasingly available online. In order to benefit from it we need tools to retrieve the most relevant datasets that match ones data needs. Several vocabularies have been developed to describe datasets in order to increase their discoverability, but for data publishers is costly to cumbersome to annotate them using all, leading to the question of what properties are more important. In this work we contribute with a systematic study of the patterns and specific attributes that data consumers use to search for data and how it compares with general web search. We performed a query log analysis based on logs from four national open data portals and conducted a qualitative analysis of user data requests for requests issued to one of them. Search queries issued on data portals differ from those issued to web search engines in their length, topic, and structure. Based on our findings we hypothesise that portals search functionalities are currently used in an exploratory manner, rather than to retrieve a specific resource. In our study of data requests we found that geospatial and temporal attributes, as well as information on the required granularity of the data are the most common features. The findings of both analyses suggest that these features are of higher importance in dataset retrieval in contrast to general web search, suggesting that efforts of dataset publishers should focus on generating dataset descriptions including them.

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“…For many use cases (such as for instance data journalism and fact checking) the pre-dominant need is to scope down the relevant datasets to a particular period or region. Therefore, we argue that spatio-temporal search is a crucial requirement across Open Data portals [4].…”

Section: Introductionmentioning

confidence: 99%

“…a closed list of nodes. 4 http://www.geonames.org/ 5 http://www.geonames.org/6559171/ 6 http://www.geonames.org/ontology/ontology\_v3.1. rdf 7 Here, gn: corresponds to the namespace URL http: //www.geonames.org/ontology# 8 http://www.geonames.org/ontology/documentation.…”

Section: Introductionmentioning

confidence: 99%