SearcharxivSearch

arXiv subjects

Peter Mutschke

Publications and source records attributed to Peter Mutschke.

At least 19 recordsLinked to original sources

Research Knowledge Graphs in NFDI4DataScience: Key Activities, Achievements, and Future Directions

As research in Artificial Intelligence and Data Science continues to grow in volume and complexity, it becomes increasingly difficult to ensure transparency, reproducibility, and discoverability. To address these challenges, as research artifacts should be understandable and usable by machines, the NFDI4DataScience consortium is developing and providing Research Knowledge Graphs (RKGs). Building upon earlier works, this paper presents recent progress in creating semantically rich RKGs using standardized ontologies, shared vocabularies, and automated Information Extraction techniques. Key achievements include the development of the NFDI4DS ontology, metadata standards, tools, and services designed to support the FAIR principles, as well as community-led projects and various implementations of RKGs. Together, these efforts aim to capture and connect the complex relationships between datasets, models, software, and scientific publications.

cs.IR

Linking Data Citation to Repository Visibility: An Empirical Study

In today's data-driven research landscape, dataset visibility and accessibility play a crucial role in advancing scientific knowledge. At the same time, data citation is essential for maintaining academic integrity, acknowledging contributions, validating research outcomes, and fostering scientific reproducibility. As a critical link, it connects scholarly publications with the datasets that drive scientific progress. This study investigates whether repository visibility influences data citation rates. We hypothesize that repositories with higher visibility, as measured by search engine metrics, are associated with increased dataset citations. Using OpenAlex data and repository impact indicators (including the visibility index from Sistrix, the h-index of repositories, and citation metrics such as mean and median citations), we analyze datasets in Social Sciences and Economics to explore their relationship. Our findings suggest that datasets hosted on more visible web domains tend to receive more citations, with a positive correlation observed between web domain visibility and dataset citation counts, particularly for datasets with at least one citation. However, when analyzing domain-level citation metrics, such as the h-index, mean, and median citations, the correlations are inconsistent and weaker. While higher visibility domains tend to host datasets with greater citation impact, the distribution of citations across datasets varies significantly. These results suggest that while visibility plays a role in increasing citation counts, it is not the sole factor influencing dataset citation impact. Other elements, such as dataset quality, research trends, and disciplinary norms, can also contribute to citation patterns.

cs.DL

A Digital Library for Research Data and Related Information in the Social Sciences

In the social sciences, researchers search for information on the Web, but this is most often distributed on different websites, search portals, digital libraries, data archives, and databases. In this work, we present an integrated search system for social science information that allows finding information around research data in a single digital library. Users can search for research data sets, publications, survey variables, questions from questionnaires, survey instruments, and tools. Information items are linked to each other so that users can see, for example, which publications contain data citations to research data. The integration and linking of different kinds of information increase their visibility so that it is easier for researchers to find information for re-use. In a log-based usage study, we found that users search across different information types, that search sessions contain a high rate of positive signals and that link information is often explored.

cs.DL

Challenges for Measuring Usefulness of Interactive IR Systems with Log-based Approaches

The usefulness evaluation model proposed by Cole et al. in 2009 [2] focuses on the evaluation of interactive IR systems by their support towards the user's overall goal, sub goals and tasks. This is a more human focus of the IR evaluation process than with classical TREC-oriented studies and gives a more holistic view on the IR evaluation process. However, yet there is no formal framework how the usefulness model can be operationalized. Additionally, a lot of information needed for the operationalization is only available in explicit user studies where for example the overall goal and the tasks are prompted from the users or are predefined. Measuring the usefulness of IR systems outside the laboratory is a challenging task as most often only log data of user interaction is available. But, an operationalization of the usefulness model based on interaction data could be applied to diverse systems and evaluation results would be comparable. In this article we discuss the challenges for measuring the usefulness of IIR systems with log-based approaches.

cs.IR

A Usefulness-based Approach for Measuring the Local and Global Effect of IIR Services

In Interactive Information Retrieval (IIR) different services such as search term suggestion can support users in their search process. The applicability and performance of such services is either measured with different user-centered studies (like usability tests or laboratory experiments) or, in the context of IR, with their contribution to measures like precision and recall. However, each evaluation methodology has its certain disadvantages. For example, user-centered experiments are often costly and small-scaled; IR experiments rely on relevance assessments and measure only relevance of documents. In this work we operationalize the usefulness model of Cole et al. (2009) on the level of system support to measure not only the local effect of an IR service, but the impact it has on the whole search process. We therefore use a log-based evaluation approach which models user interactions within sessions with positive signals and apply it for the case of a search term suggestion service. We found that the usage of the service significantly often implicates the occurrence of positive signals during the following session steps.

cs.IR

Editorial for the Proceedings of the Workshop Knowledge Maps and Information Retrieval (KMIR2014) at Digital Libraries 2014

Knowledge maps are promising tools for visualizing the structure of large-scale information spaces, but still far away from being applicable for searching. The first international workshop on "Knowledge Maps and Information Retrieval (KMIR)", held as part of the International Conference on Digital Libraries 2014 in London, aimed at bringing together experts in Information Retrieval (IR) and knowledge mapping in order to discuss the potential of interactive knowledge maps for information seeking purposes.

cs.IR

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.

cs.IR

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.

cs.IR

Establishing an Online Access Panel for Interactive Information Retrieval Research

We propose an online access panel to support the evaluation process of Interactive Information Retrieval (IIR) systems - called IIRpanel. By maintaining an online access panel with users of IIR systems we assume that the recurring effort to recruit participants for web-based as well as for lab studies can be minimized. We target on using the online access panel not only for our own development processes but to open it for other interested researchers in the field of IIR. In this paper we present the concept of IIRpanel as well as first implementation details.

cs.IR

Knowledge Maps and Information Retrieval (KMIR)

Information systems usually show as a particular point of failure the vagueness between user search terms and the knowledge orders of the information space in question. Some kind of guided searching therefore becomes more and more important in order to precisely discover information without knowing the right search terms. Knowledge maps of digital library collections are promising navigation tools through knowledge spaces but still far away from being applicable for searching digital libraries. However, there is no continuous knowledge exchange between the "map makers" on the one hand and the Information Retrieval (IR) specialists on the other hand. Thus, there is also a lack of models that properly combine insights of the two strands. The proposed workshop aims at bringing together these two communities: experts in IR reflecting on visual enhanced search interfaces and experts in knowledge mapping reflecting on visualizations of the content of a collection that might also present a context for a search term in a visual manner. The intention of the workshop is to raise awareness of the potential of interactive knowledge maps for information seeking purposes and to create a common ground for experiments aiming at the incorporation of knowledge maps into IR models at the level of the user interface.

cs.IR

Assessing Educational Research -- An Information Service for Monitoring a Heterogeneous Research Field

The paper presents a web prototype that visualises different characteristics of research projects in the heterogeneous domain of educational research. The concept of the application derives from the project "Monitoring Educational Research" (MoBi) that aims at identifying and implementing indicators that adequately describe structural properties and dynamics of the research field. The prototype enables users to visualise data regarding different indicators, e.g. "research activity", "funding", "qualification project", "disciplinary area". Since the application is based on Semantic MediaWikitechnology it furthermore provides an easily accessible opportunity to collaboratively work on a database of research projects. Users can jointly and in a semantically controlled way enter metadata on research projects which are the basis for the computation and visualisation of indicators.

cs.DL

Editorial for the Bibliometric-enhanced Information Retrieval Workshop at ECIR 2014

This first "Bibliometric-enhanced Information Retrieval" (BIR 2014) workshop aims to engage with the IR community about possible links to bibliometrics and scholarly communication. Bibliometric techniques are not yet widely used to enhance retrieval processes in digital libraries, although they offer value-added effects for users. In this workshop we will explore how statistical modelling of scholarship, such as Bradfordizing or network analysis of co-authorship network, can improve retrieval services for specific communities, as well as for large, cross-domain collections. This workshop 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. Our interests include information retrieval, information seeking, science modelling, network analysis, and digital libraries. The goal is to apply insights from bibliometrics, scientometrics, and informetrics to concrete practical problems of information retrieval and browsing.

cs.IR

Bibliometric-enhanced Information Retrieval

Bibliometric techniques are not yet widely used to enhance retrieval processes in digital libraries, although they offer value-added effects for users. In this workshop 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 collections. This workshop 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.

cs.IR

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.

cs.DL

An OAI-PMH-based Web Service for the Generation of Co-Author Networks

We will present a new component of our technical framework that was built to provide a brought range of reusable web services for the enhancement of typical scientific retrieval processes. The proposed component computes betweenness of authors in co-authorship networks extracted from publicly available metadata that was harvested using OAI-PMH.

cs.DL

Linking Social Networking Sites to Scholarly Information Portals by ScholarLib

Online Social Networks usually provide no or limited way to access scholarly information provided by Digital Libraries (DLs) in order to share and discuss scholarly content with other online community members. The paper addresses the potentials of Social Networking sites (SNSs) for science and proposes initial use cases as well as a basic bi-directional model called ScholarLib for linking SNSs to scholarly DLs. The major aim of ScholarLib is to make scholarly information provided by DLs accessible at SNSs, and vice versa, to enhance retrieval quality at DL side by social information provided by SNSs.

cs.DL

Science Models as Value-Added Services for Scholarly Information Systems

The paper introduces scholarly Information Retrieval (IR) as a further dimension that should be considered in the science modeling debate. The IR use case is seen as a validation model of the adequacy of science models in representing and predicting structure and dynamics in science. Particular conceptualizations of scholarly activity and structures in science are used as value-added search services to improve retrieval quality: a co-word model depicting the cognitive structure of a field (used for query expansion), the Bradford law of information concentration, and a model of co-authorship networks (both used for re-ranking search results). An evaluation of the retrieval quality when science model driven services are used turned out that the models proposed actually provide beneficial effects to retrieval quality. From an IR perspective, the models studied are therefore verified as expressive conceptualizations of central phenomena in science. Thus, it could be shown that the IR perspective can significantly contribute to a better understanding of scholarly structures and activities.

cs.DL

Treatment of Semantic Heterogeneity in Information Retrieval

The first step to handle semantic heterogeneity should be the attempt to enrich the semantic information about documents, i.e. to fill up the gaps in the documents meta-data automatically. Section 2 describes a set of cascading deductive and heuristic extraction rules, which were developed in the project CARMEN for the domain of Social Sciences. The mapping between different terminologies can be done by using intellectual, statistical and/or neural network transfer modules. Intellectual transfers use cross-concordances between different classification schemes or thesauri. Section 3 describes the creation, storage and handling of such transfers.

cs.IR