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The 11th Bibliometric-enhanced Information Retrieval Workshop (BIR 2021) was held online on April 1st, 2021, at ECIR 2021 as a virtual event. The interdisciplinary BIR workshop series aims to bring together researchers from different communities, especially Scientometrics/Bibliometrics and Information Retrieval. We report on the 11th BIR, its invited talks and accepted papers. Lessons learned from BIR 2021 are discussed and potential future research questions identified that position Bibliometric-enhanced IR as an exciting special yet important branch of IR research.

This study investigates the development of open access (OA) to journal articles from authors affiliated with German universities and non-university research institutions in the period 2010-2018. Beyond determining the overall share of openly available articles, a systematic classification of distinct categories of OA publishing allowed us to identify different patterns of adoption of OA. Taking into account the particularities of the German research landscape, variations in terms of productivity, OA uptake and approaches to OA are examined at the meso-level and possible explanations are discussed. The development of the OA uptake is analysed for the different research sectors in Germany (universities, non-university research institutes of the Helmholtz Association, Fraunhofer Society, Max Planck Society, Leibniz Association, and government research agencies). Combining several data sources (incl. Web of Science, Unpaywall, an authority file of standardised German affiliation information, the ISSN-Gold-OA 3.0 list, and OpenDOAR), the study confirms the growth of the OA share mirroring the international trend reported in related studies. We found that 45% of all considered articles during the observed period were openly available at the time of analysis. Our findings show that subject-specific repositories are the most prevalent type of OA. However, the percentages for publication in fully OA journals and OA via institutional repositories show similarly steep increases. Enabling data-driven decision-making regarding the implementation of OA in Germany at the institutional level, the results of this study furthermore can serve as a baseline to assess the impact recent transformative agreements with major publishers will likely have on scholarly communication. Open Access uptake in Germany 2010-2018: Interactive Supplement: s. https://subugoe.github.io/oauni/articles/supplement.html

A potential motivation for scientists to deposit their scientific work as preprints is to enhance its citation or social impact. In this study we assessed the citation and altmetric advantage of bioRxiv, a preprint server for the biological sciences. We retrieved metadata of all bioRxiv preprints deposited between November 2013 and December 2017, and matched them to articles that were subsequently published in peer-reviewed journals. Citation data from Scopus and altmetric data from Altmetric.com were used to compare citation and online sharing behavior of bioRxiv preprints, their related journal articles, and nondeposited articles published in the same journals. We found that bioRxiv-deposited journal articles had sizably higher citation and altmetric counts compared to nondeposited articles. Regression analysis reveals that this advantage is not explained by multiple explanatory variables related to the articles’ publication venues and authorship. Further research will be required to establish whether such an effect is causal in nature. bioRxiv preprints themselves are being directly cited in journal articles, regardless of whether the preprint has subsequently been published in a journal. bioRxiv preprints are also shared widely on Twitter and in blogs, but remain relatively scarce in mainstream media and Wikipedia articles, in comparison to peer-reviewed journal articles.

Citation metrics have value because they aim to make scientific assessment a level playing field, but urgent transparency-based adjustments are necessary to ensure that measurements yield the most accurate picture of impact and excellence. One problematic area is the handling of self-citations, which are either excluded or inappropriately accounted for when using bibliometric indicators for research evaluation. Here, in favor of openly tracking self-citations we report on self-referencing behavior among various academic disciplines as captured by the curated Clarivate Analytics Web of Science database. Specifically, we examined the behavior of 385,616 authors grouped into 15 subject areas like Biology, Chemistry, Science & Technology, Engineering, and Physics. These authors have published 3,240,973 papers that have accumulated 90,806,462 citations, roughly five percent of which are self-citations. Up until now, very little is known about the buildup of self-citations at the author-level and in field-specific contexts. Our view is that hiding self-citation data is indefensible and needlessly confuses any attempts to understand the bibliometric impact of one’s work. Instead we urge academics to embrace visibility of citation data in a community of peers, which relies on nuance and openness rather than curated scorekeeping.

International audience; The Bibliometric-enhanced Information Retrieval (BIR) workshop series at ECIR tackles issues related to academic search, at the intersection of Information Retrieval, Natural Language Processing and Bibliometrics. BIR is a hot topic investigated by both academia and the industry. In this overview paper, we summarize the 11th iteration of the workshop and present the workshop topics for 2021.

In recent years, increased stakeholder pressure to transition research to Open Access has led to many journals converting, or 'flipping', from a closed access (CA) to an open access (OA) publishing model. Changing the publishing model can influence the decision of authors to submit their papers to a journal, and increased article accessibility may influence citation behaviour. In this paper we aimed to understand how flipping a journal to an OA model influences the journal's future publication volumes and citation impact. We analysed two independent sets of journals that had flipped to an OA model, one from the Directory of Open Access Journals (DOAJ) and one from the Open Access Directory (OAD), and compared their development with two respective control groups of similar journals. For bibliometric analyses, journals were matched to the Scopus database. We assessed changes in the number of articles published over time, as well as two citation metrics at the journal and article level: the normalised impact factor (IF) and the average relative citations (ARC), respectively. Our results show that overall, journals that flipped to an OA model increased their publication output compared to journals that remained closed. Mean normalised IF and ARC also generally increased following the flip to an OA model, at a greater rate than was observed in the control groups. However, the changes appear to vary largely by scientific discipline. Overall, these results indicate that flipping to an OA publishing model can bring positive changes to a journal., 16 pages, 5 figures, Accepted in Scientometrics

The goal of this workshop is to engage the related communities in open problems in the extraction and evaluation of knowledge entities from scientific documents. This workshop entitles this cutting-edge and cross-disciplinary direction Extraction and Evaluation of Knowledge Entity (EEKE), highlighting the development of intelligent methods for identifying knowledge claims in scientific documents, and promoting the application of knowledge entities. The website of this workshop is at: https://eeke2020.github.io/.

International audience; The Bibliometric-enhanced Information Retrieval workshop series (BIR) was launched at ECIR in 2014 [Mayr et al., 2014] and it was held at ECIR each year since then. This year we organized the 10th iteration of BIR as an all-virtual workshop with a peak of 97 participants. The workshop series at ECIR and JCDL/SIGIR tackles issues related to academic search, at the crossroads between Information Retrieval, Natural Language Processing and Bibliometrics. In this report, we summarize the past workshops, present the workshop topics for 2020 [Cabanac et al., 2020] and reflect on some future steps for this workshop series.

The Bibliometric-enhanced Information Retrieval workshop series (BIR) was launched at ECIR in 2014 \cite{MayrEtAl2014} and it was held at ECIR each year since then. This year we organize the 10th iteration of BIR. The workshop series at ECIR and JCDL/SIGIR tackles issues related to academic search, at the crossroads between Information Retrieval, Natural Language Processing and Bibliometrics. In this overview paper, we summarize the past workshops, present the workshop topics for 2020 and reflect on some future steps for this workshop series.

Traditionally, Web of Science and Scopus have been the two most widely used databases for bibliometric analyses. However, during the last few years some new scholarly databases, such as Dimensions, have come up. Several previous studies have compared different databases, either through a direct comparison of article coverage or by comparing the citations across the databases. This article attempts to compare the journal coverage of the three databases: Web of Science, Scopus and Dimensions. The most recent master journal lists of the three databases have been used for the purpose of identifying the overlapping and unique journals covered in the databases. The results indicate that the databases have significantly different journal coverage, with the Web of Science being most selective and Dimensions being the most exhaustive. About 99.11% and 96.61% of the journals indexed in Web of Science are also indexed in Scopus and Dimensions, respectively. Scopus has 96.42% of its indexed journals also covered by Dimensions. Dimensions database has the most exhaustive coverage, with 82.22% more journals covered as compared to Web of Science and 48.17% more journals covered as compared to Scopus. We also analysed the research outputs for 20 highly productive countries for the 2010-2019 period, as indexed in the three databases, and identified database-induced variations in research output volume, rank and global share of different countries. In addition to variations in overall coverage of research output from different countries, the three databases appear to have differential coverage of different disciplines., Comment: Submitted to Scientometrics

The Bibliometric-enhanced Information Retrieval (BIR) workshop series has started at ECIR in 2014 and serves as the annual gathering of IR researchers who address various information-related tasks on scientific corpora and bibliometrics. We welcome contributions elaborating on dedicated IR systems, as well as studies revealing original characteristics on how scientific knowledge is created, communicated, and used. This report presents all accepted papers at the 7th BIR workshop at ECIR 2018 in Grenoble, France., Comment: 6 pages, 1 figure

In Interactive IR, researchers consider the user behaviour towards systems and search tasks in order to adapt search results and to improve the search experience of users. Analysing the users' past interactions with the system is one typical approach. In this paper, we analyse the user behaviour in retrieval sessions towards Marcia Bates' search stratagems such as Footnote Chasing, Citation Searching, Keyword Searching, Author Searching and Journal Run in a real-life academic search engine. In fact, search stratagems represent high-level search behaviour as the users go beyond simple execution of queries and investigate more of the system functionalities. We performed analyses of these five search stratagems using two datasets extracted from the social sciences search engine sowiport. A specific focus was the detection of the search phase and frequency of the usage of these stratagems. In addition, we explored the impact of these stratagems on the whole search process performance. We addressed mainly the usage patterns' observation of the stratagems, their impact on the conduct of retrieval sessions and explore whether they are used similarly in both datasets. From the observation and metrics proposed, we can conclude that the utilisation of search stratagems in real retrieval sessions leads to an improvement of the precision in terms of positive interactions. However, the difference is that Footnote Chasing, Citation Searching and Journal Run appear mostly at the end of a session while Keyword and Author Searching appear typically at the beginning. Thus, we can conclude from the log analysis that the improvement of search functionalities including personalisation and/or recommendation could be achieved by considering references, citations, and journals in the ranking process., 20 pages, 3 figures, accepted paper in Scientometrics

The large scale of scholarly publications poses a challenge for scholars in information seeking and sense-making. Bibliometrics, information retrieval (IR), text mining, and NLP techniques could help in these activities, but are not yet widely implemented in digital libraries. The 2nd joint BIRNDL workshop was held at the 40th ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2017) in Tokyo, Japan. BIRNDL 2017 intended to stimulate IR researchers and digital library professionals to elaborate on new approaches in natural language processing, information retrieval, scientometric, and recommendation techniques that can advance the state-of-the-art in scholarly document understanding, analysis, and retrieval at scale. The workshop incorporated three paper sessions and the 3rd edition of the CL-SciSumm Shared Task.

The large scale of scholarly publications poses a challenge for scholars in information seeking and sensemaking. Bibliometric, information retrieval (IR), text mining, and natural language processing techniques can assist to address this challenge, but have yet to be widely used in digital libraries (DL). This special issue on bibliometric-enhanced information retrieval and natural language processing for digital libraries (BIRNDL) was compiled after the first joint BIRNDL workshop that was held at the joint conference on digital libraries (JCDL 2016) in Newark, New Jersey, USA. It brought together IR and DL researchers and professionals to elaborate on new approaches in natural language processing, information retrieval, scientometric, and recommendation techniques that can advance the state of the art in scholarly document understanding, analysis, and retrieval at scale. This special issue includes 14 papers: four extended papers originating from the first BIRNDL workshop 2016 and the BIR workshop at ECIR 2016, four extended system reports of the CL-SciSumm Shared Task 2016 and six original research papers submitted via the open call for papers.

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-min 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 of creating one single tree for multiple users and identifying 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 regard 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.

This workshop report presents the output of the fifth Bibliometric-enhanced Information Retrieval (BIR) workshop, which has been co-located with the 39th European Conference on Information Retrieval (ECIR 2017) in Aberdeen, UK. We motivate our workshop and outline the papers (one keynote, six regular papers and five poster papers) presented at BIR 2017. Finally, we conclude with an outlook and future directions of this workshop activity.

The large scale of scholarly publications poses a challenge for scholars in information seeking and sense-making. Bibliometrics, information retrieval (IR), text mining, and NLP techniques could help in these activities, but are not yet widely implemented in digital libraries. The 2nd joint BIRNDL workshop was held at the 40th ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2017) in Tokyo, Japan. BIRNDL 2017 intended to stimulate IR researchers and digital library professionals to elaborate on new approaches in natural language processing, information retrieval, scientometric, and recommendation techniques that can advance the state-of-the-art in scholarly document understanding, analysis, and retrieval at scale. The workshop incorporated three paper sessions and the 3rd edition of the CL-SciSumm Shared Task.

This demo paper presents a generic toolchain to extract, segment and match literature references from full text PDF files in the project EXCITE. The aim of EXCITE is extracting and matching citations from social science publications and making more citation data available to researchers. Each single step in the EXCITE pipeline and the open source tools used to accomplish the tasks are explained. The public demo system which integrates all components of the toolchain under an user-friendly interface is put forward and illustrated. As a final step, a special component is introduced which is capable to ingest the extracted and matched references into the Open Citation Corpus.

International audience

In this paper, we analyze a major part of the research output of the Networked Knowledge Organization Systems (NKOS) community in the period 2000 to 2016 from a network analytical perspective. We focus on the papers presented at the European and U.S. NKOS workshops and in addition four special issues on NKOS in the last 16 years. For this purpose, we have generated an open dataset, the "NKOS bibliography" which covers the bibliographic information of the research output. We analyze the co-authorship network of this community which results in 123 papers with a sum of 256 distinct authors. We use standard network analytic measures such as degree, betweenness and closeness centrality to describe the co-authorship network of the NKOS dataset. First, we investigate global properties of the network over time. Second, we analyze the centrality of the authors in the NKOS network. Lastly, we investigate gender differences in collaboration behavior in this community. Our results show that apart from differences in centrality measures of the scholars, they have higher tendency to collaborate with those in the same institution or the same geographic proximity. We also find that homophily is higher among women in this community. Apart from small differences in closeness and clustering among men and women, we do not find any significant dissimilarities with respect to other centralities., 14 pages, 6 figures, accepted paper in International Journal on Digital Libraries (IJDL)

In this paper, three different possible inputs (reference strings, reference segments and a combination of reference strings and segments) were tested to find the best performing strategy for citation matching. Our evaluation on a manually curated gold standard showed that the input data consisting of the combination of reference segments and reference strings lead to the best result. In addition, the usage of the probabilities of the segmentation improve the result when only features based on reference segments are considered.

The Bibliometric-enhanced Information Retrieval workshop series (BIR) at ECIR tackled issues related to academic search, at the crossroads between Information Retrieval and Bibliometrics. BIR is a hot topic investigated by both academia (e.g., ArnetMiner, CiteSeerx, DocEar) and the industry (e.g., Google Scholar, Microsoft Academic Search, Semantic Scholar). This report presents the 8th iteration of the one-day BIR workshop held at ECIR 2019 in Cologne, Germany., Comment: 8 pages, report to appear in ACM SIGIR Forum

To foster collaboration and knowledge transfer between the fields of bibliometrics / scientometrics / informetrics on the one hand and information retrieval on the other hand, we successfully ran a workshop series on bibliometric-enhanced information retrieval (BIR). This workshop report presents the BIR 2016 workshop, which has been co-located with ECIR for the third time this year. We motivate our workshop and outline the papers (one keynote and seven regular papers) presented at ECIR 2016 in Padua, Italy. Finally we reflect on past BIR workshops and conclude with an outlook of future direction.

This panel brings together experts in bibliometrics and information retrieval to discuss how each of these two important areas of information science can help to inform the research of the other. There is a growing body of literature that capitalizes on the synergies created by combining methodological approaches of each to solve research problems and practical issues related to how information is created, stored, organized, retrieved and used. The session will begin with an overview of the common threads that exist between IR and metrics, followed by a summary of findings from the BIR workshops and examples of research projects that combine aspects of each area to benefit IR or metrics research areas, including search results ranking, semantic indexing and visualization. The panel will conclude with an engaging discussion with the audience to identify future areas of research and collaboration.

The aim of this experiment is to present an easy way to compare fragments of texts in order to detect (supposed) results of copy and paste operations between articles in the domain of Natural Language Processing (NLP), including Speech Processing. The search space of the comparisons is a corpus labeled as NLP4NLP, which includes 34 different conferences and journals and gathers a large part of the NLP activity over the past 50 years. This study considers the similarity between the papers of each individual event and the complete set of papers in the whole corpus, according to four different types of relationship (self-reuse, self-plagiarism, reuse and plagiarism) and in both directions: a paper borrowing a fragment of text from another paper of the corpus (that we will call the source paper), or in the reverse direction, fragments of text from the source paper being borrowed and inserted in another paper of the corpus. The results show that self-reuse is rather a common practice, but that plagiarism seems to be very unusual, and that both stay within legal and ethical limits.

The $3^{rd}$ joint BIRNDL workshop was held at the 41st ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2018) in Ann Arbor, USA. BIRNDL 2018 intended to stimulate IR researchers and digital library professionals to elaborate on new approaches in natural language processing, information retrieval, scientometrics, and recommendation techniques that can advance the state-of-the-art in scholarly document understanding, analysis, and retrieval at scale. The workshop incorporated three paper sessions and the $4^{th}$ edition of the CL-SciSumm Shared Task., 6 pages, to appear in SIGIR Forum

The large scale of scholarly publications poses a challenge for scholars in information seeking and sensemaking. Information retrieval~(IR), bibliometric and natural language processing (NLP) techniques could enhance scholarly search, retrieval and user experience but are not yet widely used. To this purpose, we propose the third iteration of the Joint Workshop on Bibliometric-enhanced Information Retrieval and Natural Language Processing for Digital Libraries (BIRNDL). The workshop is intended to stimulate IR, NLP researchers and Digital Library professionals to elaborate on new approaches in natural language processing, information retrieval, scientometrics, text mining and recommendation techniques that can advance the state-of-the-art in scholarly document understanding, analysis, and retrieval at scale. The BIRNDL workshop will incorporate multiple invited talks, paper sessions, a poster session and the 4th edition of the Computational Linguistics (CL) Scientific Summarization Shared Task.

Contextualisation has proven to be effective in tailoring \linebreak search results towards the users' information need. While this is true for a basic query search, the usage of contextual session information during exploratory search especially on the level of browsing has so far been underexposed in research. In this paper, we present two approaches that contextualise browsing on the level of structured metadata in a Digital Library (DL), (1) one variant bases on document similarity and (2) one variant utilises implicit session information, such as queries and different document metadata encountered during the session of a users. We evaluate our approaches in a living lab environment using a DL in the social sciences and compare our contextualisation approaches against a non-contextualised approach. For a period of more than three months we analysed 47,444 unique retrieval sessions that contain search activities on the level of browsing. Our results show that a contextualisation of browsing significantly outperforms our baseline in terms of the position of the first clicked item in the result set. The mean rank of the first clicked document (measured as mean first relevant - MFR) was 4.52 using a non-contextualised ranking compared to 3.04 when re-ranking the result lists based on similarity to the previously viewed document. Furthermore, we observed that both contextual approaches show a noticeably higher click-through rate. A contextualisation based on document similarity leads to almost twice as many document views compared to the non-contextualised ranking., 10 pages, 2 figures, paper accepted at JCDL 2018

When users are looking for information on the Web, they show different behavior for different task types, e.g., for fact finding vs. information gathering tasks. For example, related work in this area has investigated how this behavior can be measured and applied to distinguish between easy and difficult tasks. In this work, we look at the searcher's behavior in the domain of journalism for four different task types, and additionally, for two different topics in each task type. Search behavior is measured with a number of session variables and correlated to subjective measures such as task difficulty, task success and the usefulness of documents. We acknowledge prior results in this area that task difficulty is correlated to user effort and that easy and difficult tasks are distinguishable by session variables. However, in this work, we emphasize the role of the task topic - in and of itself - over parameters such as the search results and read content pages, dwell times, session variables and subjective measures such as task difficulty or task success. With this knowledge researchers should give more attention to the task topic as an important influence factor for user behavior., Comment: Proceedings of the 2018 Conference on Human Information Interaction & Retrieval (CHIIR 2018)

Since the Simple Knowledge Organization System (SKOS) specification and its SKOS eXtension for Labels (SKOS-XL) became formal W3C recommendations in 2009 a significant number of conventional knowledge organization systems (KOS) (including thesauri, classification schemes, name authorities, and lists of codes and terms, produced before the arrival of the ontology-wave) have made their journeys to join the Semantic Web mainstream. This paper uses "LOD KOS" as an umbrella term to refer to all of the value vocabularies and lightweight ontologies within the Semantic Web framework. The paper provides an overview of what the LOD KOS movement has brought to various communities and users. These are not limited to the colonies of the value vocabulary constructors and providers, nor the catalogers and indexers who have a long history of applying the vocabularies to their products. The LOD dataset producers and LOD service providers, the information architects and interface designers, and researchers in sciences and humanities, are also direct beneficiaries of LOD KOS. The paper examines a set of the collected cases (experimental or in real applications) and aims to find the usages of LOD KOS in order to share the practices and ideas among communities and users. Through the viewpoints of a number of different user groups, the functions of LOD KOS are examined from multiple dimensions. This paper focuses on the LOD dataset producers, vocabulary producers, and researchers (as end-users of KOS)., Comment: 31 pages, 12 figures, accepted paper in International Journal on Digital Libraries

In the area of Linked Open Data (LOD), meaningful and high-performance interlinking of different datasets has become an ongoing challenge. Necessary tasks are supported by established standards and software, e.g., for the transformation, storage, interlinking, and publication of data. Our use case Swissbib is a well-known provider for bibliographic data in Switzerland representing various libraries and library networks. In this article, a case study is presented from the project linked.swissbib.ch which focuses on the preparation and publication of the Swissbib data by means of LOD. Data available in Marc21 XML are extracted from the Swissbib system and transformed into an RDF/XML representation. From approximately 21 million monolithic records, the author information is extracted and interlinked with authority files from the Virtual International Authority File (VIAF) and DBpedia. The links are used to extract additional data from the counterpart corpora. Afterward, data are pushed into an Elasticsearch index to make the data accessible for other components. As a demonstrator, a search portal is developed which presents the additional data and the generated links to users. In addition to that, a REST interface is developed in order to enable also access by other applications. A main obstacle in this project is the amount of data and the necessity of day-to-day (partial) updates. In the current situation, the data in Swissbib and in the external corpora are too large to be processed by established linking tools. The arising memory footprint prevents the correct functioning of these tools. Also triple stores are unhandy by revealing a massive overhead for import and update operations. Hence, we have developed procedures for extracting and shaping the data into a more suitable form, e.g., data are reduced to the necessary properties and blocked. For this purpose, we used sorted N-Triples as an intermediate data format. This method proved to be very promising as our preliminary results show. Our approach could establish 30,773 links to DBpedia and 20,714 links to VIAF and both link sets show high precision values and could be generated in reasonable expenditures of time.

The objective of the OSCOSS research project on "Opening Scholarly Communication in the Social Sciences" is to build a coherent collaboration environment that facilitates scholarly communication workflows of social scientists in the roles of authors, reviewers, editors and readers. This paper presents the implementation of the core of this environment: the integration of the Fidus Writer academic word processor with the Open Journal Systems (OJS) submission and review management system., 11 pages, 2 figures, submitted to the journal IWP

The extraction of individual reference strings from the reference section of scientific publications is an important step in the citation extraction pipeline. Current approaches divide this task into two steps by first detecting the reference section areas and then grouping the text lines in such areas into reference strings. We propose a classification model that considers every line in a publication as a potential part of a reference string. By applying line-based conditional random fields rather than constructing the graphical model based on individual words, dependencies and patterns that are typical in reference sections provide strong features while the overall complexity of the model is reduced. We evaluated our novel approach RefExt against various state-of-the-art tools (CERMINE, GROBID, and ParsCit) and a gold standard which consists of 100 German language full text publications from the social sciences. The evaluation demonstrates that we are able to outperform state-of-the-art tools which rely on the identification of reference section areas.

The large scale of scholarly publications poses a challenge for scholars in information seeking and sensemaking. Bibliometrics, information retrieval (IR), text mining and NLP techniques could help in these search and look-up activities, but are not yet widely used. This workshop is intended to stimulate IR researchers and digital library professionals to elaborate on new approaches in natural language processing, information retrieval, scientometrics, text mining and recommendation techniques that can advance the state-of-the-art in scholarly document understanding, analysis, and retrieval at scale. The BIRNDL workshop at SIGIR 2017 will incorporate an invited talk, paper sessions and the third edition of the Computational Linguistics (CL) Scientific Summarization Shared Task., Comment: 2 pages, workshop paper accepted at the SIGIR 2017

In this paper, we present an open data set extracted from the transaction log of the social sciences academic search engine sowiport. The data set includes a filtered set of 484,449 retrieval sessions which have been carried out by sowiport users in the period from April 2014 to April 2015. We propose a description of interactions performed by the academic search engine users that can be used in different applications such as result ranking improvement, user modeling, query reformulation analysis, search pattern recognition.

The Thesaurus for the Social Sciences (TheSoz) is a Linked Dataset in SKOS format, which serves as a crucial instrument for information retrieval based on e.g. document indexing or search term recommendation. Thesauri and similar controlled vocabularies build a linking bridge for other datasets from the Linked Open Data cloud - even between different domains. The information and knowledge, which is exposed by such links, can be processed by Semantic Web applications. In this article the conversion process of the TheSoz to SKOS is described including the analysis of the original dataset and its structure, the mapping to adequate SKOS classes and properties, and the technical conversion. Furthermore mappings to other datasets and the appliance of the TheSoz are presented. Finally, limitations and modeling issues encountered during the creation process are discussed., to appear in Semantic Web - Interoperability, Usability, Applicability (IOS Press)

International audience; The large scale of scholarly publications poses a challenge for scholars in information-seeking and sensemaking. Bibliometric, information retrieval (IR), text mining and NLP techniques could help in these activities, but are not yet widely used in digital libraries. This workshop is intended to stimulate IR researchers and digital library professionals to elaborate on new approaches in natural language processing, information retrieval, scientometric and recommendation techniques which can advance the state-of-the-art in scholarly document understanding, analysis and retrieval at scale.

With the increasing size of digital libraries (DLs) it has become a challenge to identify author names correctly and assign publications to them. The situation becomes more critical when different persons share the same name (homonym problem) or when the names of authors are presented in several different ways (synonym problem). This paper focuses on homonym names in the computer science bibliography DBLP. The goal of this study is to implement and evaluate a method which uses co-authorship networks in order to disambiguate homonym names, especially common names. The results show that the implemented method has a good performance and can be used for author name disambiguation of sparse bibliographic records.

High-level search activities for Digital Libraries (DLs) introduced by Fuhr et al. [8] go beyond basic query searches because they include targeted and structured searches like e.g. a journal run or citation searching. In this paper, we investigate if and how typical high-level search activities are really used in current DLs. We conducted an online survey with 129 participating researchers from different fields of study that aims at getting a quantitative view on the usage of high level search activities in DLs. Although our results indicate the usefulness of high-level search activities, they are not well supported by modern DLs with regards to the users’ state of search, e.g. looking at a relevant or not relevant document. Furthermore, we identified differences in the information seeking behavior across the respondents. Respondents with a higher academic degree significantly considered journals and conference proceedings as more useful than respondents with a lower academic degree.

With the increasing size of digital libraries it has become a challenge to identify author names correctly. The situation becomes more critical when different persons share the same name (homonym problem) or when the names of authors are presented in several different ways (synonym problem). This paper focuses on homonym names in the computer science bibliography DBLP. The goal of this study is to evaluate a method which uses co-authorship networks and analyze the effect of common names on it. For this purpose we clustered the publications of authors with the same name and measured the effectiveness of the method against a gold standard of manually assigned DBLP records. The results show that despite the good performance of implemented method for most names, we should optimize for common names. Hence community detection was employed to optimize the method. Results prove that the applied method improves the performance for these names.

This special issue of the International Journal of Digital Libraries evolved from the 13th Networked Knowledge Organization Systems (NKOSs) workshop held at the joint Digital Libraries conference 2014 in London. The focus of the workshop was 'Mapping between Linked Data vocabularies of KOS' and ‘Meaningful Concept Display and Meaningful Visualization of KOS’. The issue presents six papers on the general theme on both conceptual aspects and technical implementation of NKOS. We dedicate this special issue to our long-term colleague and friend Johan De Smedt who died in June 2015 while we were editing the special issue. (author's abstract)

Users of web search engines are known to mostly focus on the top ranked results of the search engine result page. While many studies support this well known information seeking pattern only few studies concentrate on the question what users are missing by neglecting lower ranked results. To learn more about the relevance distributions in the so-called long tail we conducted a relevance assessment study with the Million Short long-tail web search engine. While we see a clear difference in the content between the head and the tail of the search engine result list we see no statistical significant differences in the binary relevance judgments and weak significant differences when using graded relevance. The tail contains different but still valuable results. We argue that the long tail can be a rich source for the diversification of web search engine result lists but it needs more evaluation to clearly describe the differences.

The BIR workshop brings together experts in Bibliometrics and Information Retrieval. While sometimes perceived as rather loosely related, these research areas share various interests and face similar challenges. Our motivation as organizers of the BIR workshop stemmed from a twofold observation. First, both communities only partly overlap, albeit sharing various interests. Second, it will be profitable for both sides to tackle some of the emerging problems that scholars face today when they have to identify relevant and high quality literature in the fast growing number of electronic publications available worldwide. Bibliometric techniques are not yet used widely to enhance retrieval processes in digital libraries, although they offer value-added effects for users. Information professionals working in libraries and archives, however, are increasingly confronted with applying bibliometric techniques in their services. The first BIR workshop in 2014 set the research agenda by introducing each group to the other, illustrating state-of-the-art methods, reporting on current research problems, and brainstorming about common interests. The second workshop in 2015 further elaborated these themes. This third BIR workshop aims to foster a common ground for the incorporation of bibliometric-enhanced services into scholarly search engine interfaces. In particular we will address specific communities, as well as studies on large, cross-domain collections like Mendeley and ResearchGate. This third BIR workshop addresses explicitly both scholarly and industrial researchers., 4 pages, 38th European Conference on IR Research, ECIR 2016, Padova, Italy. arXiv admin note: substantial text overlap with arXiv:1501.02646