People searching for people

Weerkamp, Wouter; Berendsen, Richard; Kovachev, Bogomil; Meij, Edgar; Balog, Krisztian; Rijke, Maarten de

doi:10.1145/2009916.2009927

Cited by 29 publications

(5 citation statements)

References 27 publications

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“…For example, an email search has unique sets of resources for each users in addition to specific metadata such as sender and receiver addresses, topic or timestamp in order to judge the relevance [7]. Due to the specificity and limited scope of resources, vertical search engines often offer greater precision, utilize more complex schemas to match specific searching scenarios, and tend to support more complex user tasks [91,147,135].…”

Section: Information Retrievalmentioning

confidence: 99%

Dataset search: a survey

et al. 2019

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Generating value from data requires the ability to find, access and make sense of datasets. There are many efforts underway to encourage data sharing and reuse, from scientific publishers asking authors to submit data alongside manuscripts to data marketplaces, open data portals and data communities. Google recently beta released a search service for datasets, which allows users to discover data stored in various online repositories via keyword queries. These developments foreshadow an emerging research field around dataset search or retrieval that broadly encompasses frameworks, methods and tools that help match a user data need against a collection of datasets. Here, we survey the state of the art of research and commercial systems in dataset retrieval. We identify what makes dataset search a research field in its own right, with unique challenges and methods and highlight open problems. We look at approaches and implementations from related areas dataset search is drawing upon, including information retrieval, databases, entity-centric and tabular search in order to identify possible paths to resolve these open problems as well as immediate next steps that will take the field forward.

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Section: Information Retrievalmentioning

confidence: 99%

Dataset search: a survey

et al. 2019

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“…Each vertical search strategy focuses on how to best utilize the additional information and structure that is provided with each information source. There are multiple distinct verticals which have been subject of specific query log analyses, for example people search engines [48], email search [2,32], research publication search [30,50] or search over linked data [16]. For instance, for email search Ai et al [2] noticed that users know the precise attributes of a resource they are looking for.…”

Section: Web Searchmentioning

confidence: 99%

“…senders' email address, subject or timestamp) which can help both organising and searching through the results. People search [48,15] is gaining more importance with portals such as LinkedIn or Facebook. In this vertical, relevant factors are the first and last name of the person [15].…”

Section: Web Searchmentioning

confidence: 99%

“…Our main observation is that returning users view on average more pages and engage in longer sessions. Query log analysis from other verticals do not consider this metric except for [48], which reports 7% returning users out of its 7 million sessions. We believe these differences sug- 27 , reports the share of search exits at 11% and search refinements at 30%, based on logs collected over one month.…”

Section: Usersmentioning

confidence: 99%

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Characterising dataset search—An analysis of search logs and data requests

Kacprzak

Koesten

Ibáñez

et al. 2019

Journal of Web Semantics

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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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“…We also know that different information sources result in people searching and choosing results differently, as relevance depends on context. This has been shown in search verticals, for instance for scientific publications (Li et al, 2010;Yu et al, 2005), people (Weerkamp et al, 2011) or products (Gysel et al, 2016;Rowley, 2000).…”

Section: Selecting and Making Sense Of Datasetsmentioning

confidence: 87%

Everything you always wanted to know about a dataset: Studies in data summarisation

Koesten

Simperl

Blount

et al. 2020

International Journal of Human-Computer Studies

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Summarising data as text helps people make sense of it. It also improves data discovery, as search algorithms can match this text against keyword queries. In this paper, we explore the characteristics of text summaries of data in order to understand how meaningful summaries look like. We present two complementary studies: a data-search diary study with 69 students, which offers insight into the information needs of people searching for data; and a summarisation study, with a lab and a crowdsourcing component with overall 80 data-literate participants, which produced summaries for 25 datasets. In each study we carried out a qualitative analysis to identify key themes and commonly mentioned dataset attributes, which people consider when searching and making sense of data. The results helped us design a template to create more meaningful textual representations of data, alongside guidelines for improving data-search experience overall.

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People searching for people

Cited by 29 publications

References 27 publications

Dataset search: a survey

Dataset search: a survey

Characterising dataset search—An analysis of search logs and data requests

Everything you always wanted to know about a dataset: Studies in data summarisation

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