Science models for search: a study on combining scholarly information retrieval and scientometrics

Mutschke, Peter; Mayr, Philipp

doi:10.1007/s11192-014-1485-2

Cited by 12 publications

(10 citation statements)

References 49 publications

(78 reference statements)

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“…Among the many possible hits for a search query, they may want to know which are core works (articles, books) and which are rather peripheral. They may want to use different rankings [25], get some additional context information about authors or journals, or see other closely related vocabulary or works associated with a search term. On the whole, they would have less need to define a topic and a field in a bijective, univocal way.…”

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confidence: 99%

Contextualization of topics: browsing through the universe of bibliographic information

Koopman¹,

Wang²,

Scharnhorst

2017

Scientometrics

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This paper describes how semantic indexing can help to generate a contextual overview of topics and visually compare clusters of articles. The method was originally developed for an innovative information exploration tool, called Ariadne, which operates on bibliographic databases with tens of millions of records [18]. In this paper, the method behind Ariadne is further developed and applied to the research question of the special issue "Same data, different results" -the better understanding of topic (re-)construction by different bibliometric approaches. For the case of the Astro dataset of 111,616 articles in astronomy and astrophysics, a new instantiation of the interactive exploring tool, LittleAriadne, has been created. This paper contributes to the overall challenge to delineate and define topics in two different ways. First, we produce two clustering solutions based on vector representations of articles in a lexical space. These vectors are built on semantic indexing of entities associated with those articles. Second, we discuss how LittleAriadne can be used to browse through the network of topical terms, authors, journals, citations and various cluster solutions of the Astro dataset. More specifically, we treat the assignment of an article to the different clustering solutions as an additional element of its bibliographic record. Keeping the principle of semantic indexing on the level of such an extended list of entities of the bibliographic record, LittleAriadne in turn provides a visualization of the context of a specific clustering solution. It also conveys the similarity of article clusters produced by different al-

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confidence: 99%

Contextualization of topics: browsing through the universe of bibliographic information

Koopman¹,

Wang²,

Scharnhorst

2017

Scientometrics

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“…Thus, several researches such as [15], [16], [9], and [17] have utilized Solr system to deploy and investigate their proposed techniques. Additionally, Mutschke et al [18] chose Solr as basic open source IR framework for evaluating the use of ranking mechanisms and investigating the effect of using different science conceptualizations in the search ranking. Similarly, the works in [19] and [20] have used Terrier to apply and analyze their proposed techniques of TF-IDF models and Probabilistic for retrieving document in IRs.…”

Section: Related Workmentioning

confidence: 99%

Distributed Indexing: Performance Analysis of Solr, Terrier and Katta Information Retrievals

Aldailamy

Hamid

Abdulkarem

2018

MJCS

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Information Retrieval (IR) systems are currently facing a continuous challenge due to the increasing size of datasets. Extremely, large data from different aspects is gathered each day resulting in huge increase in the scale of the raw data available across the internet. Indexing, as the main function of IR systems, is becoming a time-consuming problem. Therefore, efficient indexing of a large volume of data is now a critical requirement of modern IR systems. High performance indexing is performed nowadays over the use of MapReduce programming model. MapReduce is a programming paradigm that enables massive processing and large collections distribution across multiple (hundreds or thousands) commodity computers. To shed some light on this issue, this paper presents a detailed performance analysis of distributed indexing for Solr, Katta and Terrier with the context of MapReduce. In particular, this study compares and analyzes the distributed indexing performance of three frameworks using 1GB, 3GB, 6GB, and 9GB subsets of TREC dataset as the processing power increase. The experiments measure the indexing average time, then throughput, speedup, and efficiency of indexing process. The results show that, Terrier performance is the best in the presence of large collections and scalable processing power. While, Solr performance is the best when having limited computing power and small document collections. Finally, the experimental results show that, Katta produced the worst indexing average time among the three frameworks but its speedup scales linearly with processing power and collection size.

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“…This could be further supported by interconnecting related content in a more intense way. Possible interconnections could be revealed by applying bibliometric features like bibliographic coupling or author networks [11,32]. -Extracting references and citations: A lot of research today is invested in extracting references and linking those to related documents.…”

Section: Implications For the Design Of Digital Librariesmentioning

confidence: 99%

Investigating exploratory search activities based on the stratagem level in digital libraries

et al. 2017

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In this paper we present the results of a user study on exploratory search activities in a social science digital library. We conducted a user study with 32 participants with a social sciences background -16 postdoctoral researchers and 16 students -who were asked to solve a task on searching related work to a given topic. The exploratory search task was performed in a 10-minutes time slot. The use of certain search activities is measured and compared to gaze data recorded with an eye tracking device. We use a novel tree graph representation to visualise the users' search patterns and introduce a way to combine multiple search session trees. The tree graph representation is capable to create one single tree for multiple users and to identify common search patterns. In addition, the information behaviour of students and postdoctoral researchers is being compared. The results show that search activities on the stratagem level are frequently utilised by both user groups. The most heavily used search activities were keyword search, followed by browsing through references and citations, and author searching. The eye tracking results showed an intense examination of documents metadata, especially on the level of citations and references. When comparing the group of students and postdoctoral researchers we found significant differences regarding gaze data on the area of the journal name of the seed document. In general, we found a tendency of the postdoctoral researchers to examine the metadata records more intensively with regards to dwell time and the number of fixations. By creating combined session trees and deriving subtrees from those, we were able to identify common patterns like economic (explorative) and exhaustive (navigational) behaviour. Our results show that participants utilised multiple search strategies starting from the seed document, which means, that they examined different paths to find related publications.

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Science models for search: a study on combining scholarly information retrieval and scientometrics

Cited by 12 publications

References 49 publications

Contextualization of topics: browsing through the universe of bibliographic information

Contextualization of topics: browsing through the universe of bibliographic information

Distributed Indexing: Performance Analysis of Solr, Terrier and Katta Information Retrievals

Investigating exploratory search activities based on the stratagem level in digital libraries

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