1,837 research outputs found
Bibliometric-enhanced Retrieval Models for Big Scholarly Information Systems
Bibliometric techniques are not yet widely used to enhance retrieval
processes in digital libraries, although they offer value-added effects for
users. In this paper we will explore how statistical modelling of scholarship,
such as Bradfordizing or network analysis of coauthorship network, can improve
retrieval services for specific communities, as well as for large, cross-domain
large collections. This paper aims to raise awareness of the missing link
between information retrieval (IR) and bibliometrics / scientometrics and to
create a common ground for the incorporation of bibliometric-enhanced services
into retrieval at the digital library interface.Comment: 4 pages, IEEE BigData 2013, Workshop on Scholarly Big Data:
Challenges and Idea
How can heat maps of indexing vocabularies be utilized for information seeking purposes?
The ability to browse an information space in a structured way by exploiting
similarities and dissimilarities between information objects is crucial for
knowledge discovery. Knowledge maps use visualizations to gain insights into
the structure of large-scale information spaces, but are still far away from
being applicable for searching. The paper proposes a use case for enhancing
search term recommendations by heat map visualizations of co-word
relation-ships taken from indexing vocabulary. By contrasting areas of
different "heat" the user is enabled to indicate mainstream areas of the field
in question more easily.Comment: URL workshop proceedings: http://ceur-ws.org/Vol-1311
Bibliometric-enhanced Information Retrieval: 2nd International BIR Workshop
This workshop brings together experts of communities which often have been
perceived as different once: bibliometrics / scientometrics / informetrics on
the one side and information retrieval on the other. Our motivation as
organizers of the workshop started from the observation that main discourses in
both fields are different, that communities are only partly overlapping and
from the belief that a knowledge transfer would be profitable for both sides.
Bibliometric techniques are not yet widely used to enhance retrieval processes
in digital libraries, although they offer value-added effects for users. On the
other side, more and more information professionals, working in libraries and
archives are confronted with applying bibliometric techniques in their
services. This way knowledge exchange becomes more urgent. The first workshop
set the research agenda, by introducing in each other methods, reporting about
current research problems and brainstorming about common interests. This
follow-up workshop continues the overall communication, but also puts one
problem into the focus. In particular, we will explore how statistical
modelling of scholarship can improve retrieval services for specific
communities, as well as for large, cross-domain collections like Mendeley or
ResearchGate. This second BIR workshop continues to raise awareness of the
missing link between Information Retrieval (IR) and bibliometrics and
contributes to create a common ground for the incorporation of
bibliometric-enhanced services into retrieval at the scholarly search engine
interface.Comment: 4 pages, 37th European Conference on Information Retrieval, BIR
worksho
A Bibliometric Study on Learning Analytics
Learning analytics tools and techniques are continually developed and published in scholarly discourse. This study aims at examining the intellectual structure of the Learning Analytics domain by collecting and analyzing empirical articles on Learning Analytics for the period of 2004-2018. First, bibliometric analysis and citation analyses of 2730 documents from Scopus identified the top authors, key research affiliations, leading publication sources (journals and conferences), and research themes of the learning analytics domain. Second, Domain Analysis (DA) techniques were used to investigate the intellectual structures of learning analytics research, publication, organization, and communication (Hjørland & Bourdieu 2014). The software of VOSviewer is used to analyze the relationship by publication: historical and institutional; author and institutional relationships and the dissemination of Learning Analytics knowledge. The results of this study showed that Learning Analytics had captured the attention of the global community. The United States, Spain, and the United Kingdom are among the leading countries contributing to the dissemination of learning analytics knowledge. The leading publication sources are ACM International Conference Proceeding Series, and Lecture Notes in Computer Science. The intellectual structures of the learning analytics domain are presented in this study the LA research taxonomy can be re-used by teachers, administrators, and other stakeholders to support the teaching and learning environments in a higher education institution
Enriching Existing Test Collections with OXPath
Extending TREC-style test collections by incorporating external resources is
a time consuming and challenging task. Making use of freely available web data
requires technical skills to work with APIs or to create a web scraping program
specifically tailored to the task at hand. We present a light-weight
alternative that employs the web data extraction language OXPath to harvest
data to be added to an existing test collection from web resources. We
demonstrate this by creating an extended version of GIRT4 called GIRT4-XT with
additional metadata fields harvested via OXPath from the social sciences portal
Sowiport. This allows the re-use of this collection for other evaluation
purposes like bibliometrics-enhanced retrieval. The demonstrated method can be
applied to a variety of similar scenarios and is not limited to extending
existing collections but can also be used to create completely new ones with
little effort.Comment: Experimental IR Meets Multilinguality, Multimodality, and Interaction
- 8th International Conference of the CLEF Association, CLEF 2017, Dublin,
Ireland, September 11-14, 201
Exploiting information needs and bibliographics for polyrepresentative document clustering
In this paper we explore the potential of combining the principle of polyrepresentation with document clustering. Our idea is discussed and evaluated for polyrepresentation of information needs as wells as for document-based polyrepresentation where bibliographic information is used as representation. The main idea is to present the user with the highly ranked polyrepresentative clusters to support the search process. Our evaluation suggests that our approach is capable of increasing retrieval performance, but performance varies for queries with a high or low number of relevant documents
The Symbiotic Relationship Between Information Retrieval and Informetrics
Informetrics and information retrieval (IR) represent fundamental areas of study within information science. Historically, researchers have not fully capitalized on the potential research synergies that exist between these two areas. Data sources used in traditional informetrics studies have their analogues in IR, with similar types of empirical regularities found in IR system content and use. Methods for data collection and analysis used in informetrics can help to inform IR system development and evaluation. Areas of application have included automatic indexing, index term weighting and understanding user query and session patterns through the quantitative analysis of user transaction logs. Similarly, developments in database technology have made the study of informetric phenomena less cumbersome, and recent innovations used in IR research, such as language models and ranking algorithms, provide new tools that may be applied to research problems of interest to informetricians. Building on the author’s previous work (Wolfram 2003), this paper reviews a sample of relevant literature published primarily since 2000 to highlight how each area of study may help to inform and benefit the other
Report on the 11th bibliometric-enhanced information retrieval workshop (BIR 2021)
Algorithms and the Foundations of Software technolog
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