Information retrieval systems have been evaluated using the Cranfield paradigm for many years. This paradigm allows a systematic, fair, and reproducible evaluation of different retrieval methods in fixed experimental environments. However, real-world retrieval systems must cope with dynamic environments and temporal changes that affect the document collection, topical trends, and the individual user's perception of what is considered relevant. Yet, the temporal dimension in IR evaluations is still understudied.To this end, this work investigates how the temporal generalizability of effectiveness evaluations can be assessed. As a conceptual model, we generalize Cranfield-type experiments to the temporal context by classifying the change in the essential components according to the create, update, and delete operations of persistent storage known from CRUD. From the different types of change different evaluation scenarios are derived and it is outlined what they imply. Based on these scenarios, renowned state-of-the-art retrieval systems are tested and it is investigated how the retrieval effectiveness changes on different levels of granularity.We show that the proposed measures can be well adapted to describe the changes in the retrieval results. The experiments conducted confirm that the retrieval effectiveness strongly depends on the evaluation scenario investigated. We find that not only the average retrieval performance of single systems but also the relative system performance are strongly affected by the components that change and to what extent these components changed.

Evaluation of text simplification currently focuses on the difference of a source text to its simplified variant. Datasets for this evaluation base on a specific topic and group of readers for which is simplified. The broad applicability of text simplification and specifics that come with intended target audiences (e.g., children compared to adult non-experts) are disregarded. An explainable assessment of the overall simplicity of text is missing. This work is BenchmArking Text Simplicity (BATS): we provide an explainable method to assess practical and concrete rules from literature describing features of simplicity and complexity of text. Our experiments on 15 datasets for text simplification highlight differences in features that are important in different domains of text and for different intended target audiences.

This special issue features the selected works of authors who have presented papers at the 2022 iteration of the Joint Conference on Digital Libraries (JCDL) in Cologne, Germany. The motto of the conference was ``Bridging Worlds'' and was run as a fully hybrid event. Ten papers covering all aspects of Digital Libraries, namely Natural Language Processing, Information Retrieval, User Behavior, Scholarly Communication, Classification, Information Extraction are included in this issue.

Simulations in various forms have been used to evaluate information access systems, like search engines, recommender systems, or conversational agents. In the form of the Cranfield paradigm, a simulation setup is well-known in the IR community, but user simulations have recently gained interest. While user simulations help to reduce the complexity of evaluation experiments and help with reproducibility, they can also contribute to a better understanding of users. Building on recent developments in methods and toolkits, the Sim4IA workshop aims to bring together researchers and practitioners to form an interactive and engaging forum for discussions on the future perspectives of the field. An additional aim is to plan an upcoming TREC/CLEF campaign.

This publication describes the motivation and generation of Qbias, a large dataset of Google and Bing search queries, a scraping tool and dataset for biased news articles, as well as language models for the investigation of bias in online search. Web search engines are a major factor and trusted source in information search, especially in the political domain. However, biased information can influence opinion formation and lead to biased opinions. To interact with search engines, users formulate search queries and interact with search query suggestions provided by the search engines. A lack of datasets on search queries inhibits research on the subject. We use Qbias to evaluate different approaches to fine-tuning transformer-based language models with the goal of producing models capable of biasing text with left and right political stance. Additionally to this work we provided datasets and language models for biasing texts that allow further research on bias in online information search.

No existing evaluation infrastructure for shared tasks currently supports both reproducible on- and offline experiments. In this work, we present an architecture that ties together both types of experiments with a focus on reproducibility. The readers are provided with a technical description of the infrastructure and details of how to contribute their own experiments to upcoming evaluation tasks.

The need for innovation in the field of academic search and IR, in general, is shown by the stagnating system performance in controlled evaluation campaigns, as demonstrated in TREC and CLEF meta-evaluation studies, as well as user studies in real systems of scientific information and digital libraries. The question of what constitutes relevance in academic search is multi-layered and a topic that drives research communities for years. The Living Labs for Academic Search (LiLAS) workshop has the goal to inspire the discussion on research and evaluation of academic search systems by strengthening the concept of living labs to the domain of academic search. We want to bring together IR researchers interested in online evaluations of academic search systems and foster knowledge on improving the search for academic resources like literature, research data, and the interlinking between these resources. The employed online evaluation approach based on a living lab infrastructure allows the direct connection to real-world academic search systems from the life sciences and the social sciences.

Academic search systems aid users in finding information covering specific topics of scientific interest and have evolved from early catalog-based library systems to modern web-scale systems. However, evaluating the performance of the underlying retrieval approaches remains a challenge. An increasing amount of requirements for producing accurate retrieval results have to be considered, e.g., close integration of the system's users. Due to these requirements, small to mid-size academic search systems cannot evaluate their retrieval system in-house. Evaluation infrastructures for shared tasks alleviate this situation. They allow researchers to experiment with retrieval approaches in specific search and recommendation scenarios without building their own infrastructure. In this paper, we elaborate on the benefits and shortcomings of four state-of-the-art evaluation infrastructures on search and recommendation tasks concerning the following requirements: support for online and offline evaluations, domain specificity of shared tasks, and reproducibility of experiments and results. In addition, we introduce an evaluation infrastructure concept design aiming at reducing the shortcomings in shared tasks for search and recommender systems.

Purpose Survey-based studies suggest that search engines are trusted more than social media or even traditional news, although cases of false information or defamation are known. The purpose of this paper is to analyze query suggestion features of three search engines to see if these features introduce some bias into the query and search process that might compromise this trust. The authors test the approach on person-related search suggestions by querying the names of politicians from the German Bundestag before the German federal election of 2017. Design/methodology/approach This study introduces a framework to systematically examine and automatically analyze the varieties in different query suggestions for person names offered by major search engines. To test the framework, the authors collected data from the Google, Bing and DuckDuckGo query suggestion APIs over a period of four months for 629 different names of German politicians. The suggestions were clustered and statistically analyzed with regards to different biases, like gender, party or age and with regards to the stability of the suggestions over time. Findings By using the framework, the authors located three semantic clusters within the data set: suggestions related to politics and economics, location information and personal and other miscellaneous topics. Among other effects, the results of the analysis show a small bias in the form that male politicians receive slightly fewer suggestions on “personal and misc” topics. The stability analysis of the suggested terms over time shows that some suggestions are prevalent most of the time, while other suggestions fluctuate more often. Originality/value This study proposes a novel framework to automatically identify biases in web search engine query suggestions for person-related searches. Applying this framework on a set of person-related query suggestions shows first insights into the influence search engines can have on the query process of users that seek out information on politicians.

Maintaining literature databases and online bibliographies is a core responsibility of metadata aggregators such as digital libraries. In the process of monitoring all the available data sources the question arises which data source should be prioritized. Based on a broad definition of information quality we are looking for different ways to find the best fitting and most promising conference candidates to harvest next. We evaluate different conference ranking features by using a pseudo-relevance assessment and a component-based evaluation of our approach.

Building up new collections for digital libraries is a demanding task. Available data sets have to be extracted which is usually done with the help of software developers as it involves custom data handlers or conversion scripts. In cases where the desired data is only available on the data provider’s website custom web scrapers are needed. This may be the case for small to medium-size publishers, research institutes or funding agencies. As data curation is a typical task that is done by people with a library and information science background, these people are usually proficient with XML technologies but are not full-stack programmers. Therefore we would like to present a web scraping tool that does not demand the digital library curators to program custom web scrapers from scratch. We present the open-source tool OXPath, an extension of XPath, that allows the user to define data to be extracted from websites in a declarative way. By taking one of our own use cases as an example, we guide you in more detail through the process of creating an OXPath wrapper for metadata harvesting. We also point out some practical things to consider when creating a web scraper (with OXPath). On top of that, we also present a syntax highlighting plugin for the popular text editor Atom that we developed to further support OXPath users and to simplify the authoring process.

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.

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 re- trieval 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 com- mon ground for the incorporation of bibliometric-enhanced services into retrieval at the digital library interface.

The combination of informetric analysis and information retrieval allows a twofold application. (1) While in-formetrics analysis is primarily used to gain insights into a scientific domain, it can be used to build recommen-dation or alternative ranking services. They are usually based on methods like co-occurrence or citation analyses. (2) Information retrieval and its decades-long tradition of rigorous evaluation using standard document corpora, predefined topics and relevance judgements can be used as a test bed for informetric analyses. We show a preliminary experiment on how both domains can be connected using the iSearch test collection, a standard information retrieval test collection derived from the open access arXiv.org preprint server. In this paper the aim is to draw a conclusion about the appropriateness of iSearch as a test bed for the evaluation of a retrieval or recommendation system that applies informetric methods to improve retrieval results for the user. Based on an interview study with physicists, bibliographic coupling and author-co-citation analysis, important authors for ten different research questions are identified. The results show that the analysed corpus includes these authors and their corresponding documents. This study is a first step towards a combination of retrieval evaluations and the evaluation of informetric analyses methods.

During the last three years we conducted several information retrieval evaluation series with more than 180 LIS students who made relevance assessments on the outcomes of three specific retrieval services. In this study we do not focus on the retrieval performance of our system but on the relevance assessments and the inter-assessor reliability. To quantify the agreement we apply Fleiss’ Kappa and Krippendorff’s Alpha. When we compare these two statistical measures on average Kappa values were 0.37 and Alpha values 0.15. We use the two agreement measures to drop too unreliable assessments from our data set. When computing the differences between the unfiltered and the filtered data set we see a root mean square error between 0.02 and 0.12. We see this as a clear indicator that disagreement affects the reliability of retrieval evaluations. We suggest not to work with unfiltered results or to clearly document the disagreement rates.

In this paper we present a novel method to connect data on the visualization level. In general, visualizations are a dead end, when it comes to reusability. Yet, users prefer to work with visualizations as evidenced by WYSIWYG editors. To enable users to work with their data in a way that is intuitive to them, we have created Vizgr. Vizgr.com offers basic visualization methods, like graphs, tag clouds, maps and time lines. But unlike normal data visualizations, these can be re-used, connected to each other and to web sites. We offer a simple opportunity to combine diverse data structures, such as geo-locations and networks, with each other by a mouse click. In an evaluation, we found that over 85 % of the participants were able to use and understand this technology without any training or explicit instructions.

This paper is about a better understanding of the structure and dynamics of science and the usage of these insights for compensating the typical problems that arises in metadata-driven Digital Libraries. Three science model driven retrieval services are presented: co-word analysis based query expansion, re-ranking via Bradfordizing and author centrality. The services are evaluated with relevance assessments from which two important implications emerge: (1) precision values of the retrieval service are the same or better than the tf-idf retrieval baseline and (2) each service retrieved a disjoint set of documents. The different services each favor quite other – but still relevant – documents than pure term-frequency based rankings. The proposed models and derived retrieval services therefore open up new viewpoints on the scientific knowledge space and provide an alternative framework to structure scholarly information systems.

This paper is about an information retrieval evaluation on three different retrieval-supporting services. All three services were designed to compensate typical problems that arise in metadata-driven Digital Libraries, which are not ade- quately handled by a simple tf-idf based retrieval. The services are: (1) a co-word analysis based query expansion mechanism and re-ranking via (2) Bradfordizing and (3) author centrality. The services are evaluated with relevance assessments conducted by 73 information science students. Since the students are neither information professionals nor domain experts the question of inter-rater agreement is taken into consideration. Two important implications emerge: (1) the inter-rater agreement rates were mainly fair to moderate and (2) after a data-cleaning step which erased the assessments with poor agreement rates the evaluation data shows that the three retrieval services returned disjoint but still relevant result sets.

Open Access Repositorien sind Online-Archive für frei im Internet zugängliche Publikationen im Volltext. Open Access Materialien oder die Open Access Repositorien selbst sind allerdings nur unzureichend in zentrale Fachportale (z.B. virtuelle Fachbibliotheken) eingebunden. Der Beitrag stellt SSOAR – Social Science Open Access Repository, einen disziplinären Open Access Volltextserver für die Sozialwissenschaften vor und zeigt wie dieser in das sozialwissenschaftliche Fachportal Sowiport integriert wird.

GESIS – Leibniz-Institut für Sozialwissenschaften betreibt mit dem Volltextserver SSOAR und der Registrierungsagentur für sozialwissenschaftliche Forschungsdaten da|ra zwei Plattformen zum Nachweis von wissenschaftlichen Ergebnissen in Form von Publikationen und Primärdaten. Beide Systeme setzen auf einen konsequenten Einsatz von Persistenten Identifikatoren (URN und DOI), was die Verknüpfung der durch da|ra registrierten Daten mit den Volltextdokumenten aus SSOAR sowie anderen Informationen aus den GESIS-Beständen ermöglicht. Zusätzlich wird durch den Einsatz von semantischen Technologien wie SKOS und RDF eine Verbindung zum Semantic Web hergestellt.

Der folgende Arbeitsbericht soll eine kurze Zusammenfassung über die perzeptorischen und kognitiven Fähigkeiten des Menschen geben. Diese Zusammenfassung ist weit davon entfernt,umfassend zu sein. Jedoch bietet sie die Möglichkeit für Informatiker und Computervisualisten,einen kurzen Einblick in kognitionspsychologische Modelle zu gewinnen.Nacheinander sollen die Wahrnehmung des Menschen (Kapitel 2), die Funktionsweise desGedächtnisses (Kapitel 3), Kognition im Allgemeinen (Kapitel 4), die Theorien der mentalenModellkonstruktion, der dualen Kodierungstheorie (Kapitel 5), die Konstruktion mentalerModelle (Kapitel 6), die Wirkungsweise und Theorie der Metapher (Kapitel 7) und letztlich dieemotiven Faktoren (Kapitel 8) betrachtet werden.Jedes Kapitel besitzt eine kleine Zusammenfassung, die die Konsequenzen der jeweiligenErkenntnisse für die Entwicklung von Softwaresystemen erklärt.