-
Gabler, S.: Vergabe von DDC-Sachgruppen mittels eines Schlagwort-Thesaurus (2021)
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- Content
- Master thesis Master of Science (Library and Information Studies) (MSc), Universität Wien. Advisor: Christoph Steiner. Vgl.: https://www.researchgate.net/publication/371680244_Vergabe_von_DDC-Sachgruppen_mittels_eines_Schlagwort-Thesaurus. DOI: 10.25365/thesis.70030. Vgl. dazu die Präsentation unter: https://www.google.com/url?sa=i&rct=j&q=&esrc=s&source=web&cd=&ved=0CAIQw7AJahcKEwjwoZzzytz_AhUAAAAAHQAAAAAQAg&url=https%3A%2F%2Fwiki.dnb.de%2Fdownload%2Fattachments%2F252121510%2FDA3%2520Workshop-Gabler.pdf%3Fversion%3D1%26modificationDate%3D1671093170000%26api%3Dv2&psig=AOvVaw0szwENK1or3HevgvIDOfjx&ust=1687719410889597&opi=89978449.
- Imprint
- Wien / Library and Information Studies : Universität
-
Noever, D.; Ciolino, M.: ¬The Turing deception (2022)
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- Source
- https%3A%2F%2Farxiv.org%2Fabs%2F2212.06721&usg=AOvVaw3i_9pZm9y_dQWoHi6uv0EN
-
Petras, V.; Womser-Hacker, C.: Evaluation im Information Retrieval (2023)
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- Abstract
- Das Ziel einer Evaluation ist die Überprüfung, ob bzw. in welchem Ausmaß ein Informationssystem die an das System gestellten Anforderungen erfüllt. Informationssysteme können aus verschiedenen Perspektiven evaluiert werden. Für eine ganzheitliche Evaluation (als Synonym wird auch Evaluierung benutzt), die unterschiedliche Qualitätsaspekte betrachtet (z. B. wie gut ein System relevante Dokumente rankt, wie schnell ein System die Suche durchführt, wie die Ergebnispräsentation gestaltet ist oder wie Suchende durch das System geführt werden) und die Erfüllung mehrerer Anforderungen überprüft, empfiehlt es sich, sowohl eine perspektivische als auch methodische Triangulation (d. h. der Einsatz von mehreren Ansätzen zur Qualitätsüberprüfung) vorzunehmen. Im Information Retrieval (IR) konzentriert sich die Evaluation auf die Qualitätseinschätzung der Suchfunktion eines Information-Retrieval-Systems (IRS), wobei oft zwischen systemzentrierter und nutzerzentrierter Evaluation unterschieden wird. Dieses Kapitel setzt den Fokus auf die systemzentrierte Evaluation, während andere Kapitel dieses Handbuchs andere Evaluationsansätze diskutieren (s. Kapitel C 4 Interaktives Information Retrieval, C 7 Cross-Language Information Retrieval und D 1 Information Behavior).
-
Du, C.; Cohoon, J.; Lopez, P.; Howison, J.: Softcite dataset : a dataset of software mentions in biomedical and economic research publications (2021)
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- Abstract
- Software contributions to academic research are relatively invisible, especially to the formalized scholarly reputation system based on bibliometrics. In this article, we introduce a gold-standard dataset of software mentions from the manual annotation of 4,971 academic PDFs in biomedicine and economics. The dataset is intended to be used for automatic extraction of software mentions from PDF format research publications by supervised learning at scale. We provide a description of the dataset and an extended discussion of its creation process, including improved text conversion of academic PDFs. Finally, we reflect on our challenges and lessons learned during the dataset creation, in hope of encouraging more discussion about creating datasets for machine learning use.
- Source
- Journal of the Association for Information Science and Technology. 72(2021) no.7, S.870-884
-
Newell, B.C.: Surveillance as information practice (2023)
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- Abstract
- Surveillance, as a concept and social practice, is inextricably linked to information. It is, at its core, about information extraction and analysis conducted for some regulatory purpose. Yet, information science research only sporadically leverages surveillance studies scholarship, and we see a lack of sustained and focused attention to surveillance as an object of research within the domains of information behavior and social informatics. Surveillance, as a range of contextual and culturally based social practices defined by their connections to information seeking and use, should be framed as information practice-as that term is used within information behavior scholarship. Similarly, manifestations of surveillance in society are frequently perfect examples of information and communications technologies situated within everyday social and organizational structures-the very focus of social informatics research. The technological infrastructures and material artifacts of surveillance practice-surveillance technologies-can also be viewed as information tools. Framing surveillance as information practice and conceptualizing surveillance technologies as socially and contextually situated information tools can provide space for new avenues of research within the information sciences, especially within information disciplines that focus their attention on the social aspects of information and information technologies in society.
- Date
- 22. 3.2023 11:57:47
- Source
- Journal of the Association for Information Science and Technology. 74(2023) no.4, S.444-460
-
Zhang, Y.; Zhang, C.; Li, J.: Joint modeling of characters, words, and conversation contexts for microblog keyphrase extraction (2020)
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- Abstract
- Millions of messages are produced on microblog platforms every day, leading to the pressing need for automatic identification of key points from the massive texts. To absorb salient content from the vast bulk of microblog posts, this article focuses on the task of microblog keyphrase extraction. In previous work, most efforts treat messages as independent documents and might suffer from the data sparsity problem exhibited in short and informal microblog posts. On the contrary, we propose to enrich contexts via exploiting conversations initialized by target posts and formed by their replies, which are generally centered around relevant topics to the target posts and therefore helpful for keyphrase identification. Concretely, we present a neural keyphrase extraction framework, which has 2 modules: a conversation context encoder and a keyphrase tagger. The conversation context encoder captures indicative representation from their conversation contexts and feeds the representation into the keyphrase tagger, and the keyphrase tagger extracts salient words from target posts. The 2 modules were trained jointly to optimize the conversation context encoding and keyphrase extraction processes. In the conversation context encoder, we leverage hierarchical structures to capture the word-level indicative representation and message-level indicative representation hierarchically. In both of the modules, we apply character-level representations, which enables the model to explore morphological features and deal with the out-of-vocabulary problem caused by the informal language style of microblog messages. Extensive comparison results on real-life data sets indicate that our model outperforms state-of-the-art models from previous studies.
- Source
- Journal of the Association for Information Science and Technology. 71(2020) no.5, S.553-567
-
Zhang, Y.; Zhang, C.: Enhancing keyphrase extraction from microblogs using human reading time (2021)
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- Abstract
- The premise of manual keyphrase annotation is to read the corresponding content of an annotated object. Intuitively, when we read, more important words will occupy a longer reading time. Hence, by leveraging human reading time, we can find the salient words in the corresponding content. However, previous studies on keyphrase extraction ignore human reading features. In this article, we aim to leverage human reading time to extract keyphrases from microblog posts. There are two main tasks in this study. One is to determine how to measure the time spent by a human on reading a word. We use eye fixation durations (FDs) extracted from an open source eye-tracking corpus. Moreover, we propose strategies to make eye FD more effective on keyphrase extraction. The other task is to determine how to integrate human reading time into keyphrase extraction models. We propose two novel neural network models. The first is a model in which the human reading time is used as the ground truth of the attention mechanism. In the second model, we use human reading time as the external feature. Quantitative and qualitative experiments show that our proposed models yield better performance than the baseline models on two microblog datasets.
- Source
- Journal of the Association for Information Science and Technology. 72(2021) no.5, S.611-626
-
Qin, H.; Wang, H.; Johnson, A.: Understanding the information needs and information-seeking behaviours of new-generation engineering designers for effective knowledge management (2020)
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- Abstract
- Purpose This paper aims to explore the information needs and information-seeking behaviours of the new generation of engineering designers. A survey study is used to approach what their information needs are, how these needs change during an engineering design project and how their information-seeking behaviours have been influenced by the newly developed information technologies (ITs). Through an in-depth analysis of the survey results, the key functions have been identified for the next-generation management systems. Design/methodology/approach The paper first proposed four hypotheses on the information needs and information-seeking behaviours of young engineers. Then, a survey study was undertaken to understand their information usage in terms of the information needs and information-seeking behaviours during a complete engineering design process. Through analysing the survey results, several findings were obtained and on this basis, further comparisons were made to discuss and evaluate the hypotheses. Findings The paper has revealed that the engineering designers' information needs will evolve throughout the engineering design project; thus, they should be assisted at several different levels. Although they intend to search information and knowledge on know-what and know-how, what they really require is the know-why knowledge in order to help them complete design tasks. Also, the paper has shown how the newly developed ITs and web-based applications have influenced the engineers' information-seeking practices. Research limitations/implications The research subjects chosen in this study are engineering students in universities who, although not as experienced as engineers in companies, do go through a complete design process with the tasks similar to industrial scenarios. In addition, the focus of this study is to understand the information-seeking behaviours of a new generation of design engineers, so that the development of next-generation information and knowledge management systems can be well informed. In this sense, the results obtained do reveal some new knowledge about the information-seeking behaviours during a general design process. Practical implications This paper first identifies the information needs and information-seeking behaviours of the new generation of engineering designers. On this basis, the varied ways to meet these needs and behaviours are discussed and elaborated. This intends to provide the key characteristics for the development of the next-generation knowledge management system for engineering design projects. Originality/value This paper proposes a novel means of exploring the future engineers' information needs and information-seeking behaviours in a collaborative working environment. It also characterises the key features and functions for the next generation of knowledge management systems for engineering design.
- Date
- 20. 1.2015 18:30:22
- Source
- Aslib journal of information management. 72(2020) no.6, S.853-868
-
Sinha, A.; Kedas, S.; Kumar, R.; Malo, P.: SEntFiN 1.0 : Entity-aware sentiment analysis for financial news (2022)
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- Abstract
- Fine-grained financial sentiment analysis on news headlines is a challenging task requiring human-annotated datasets to achieve high performance. Limited studies have tried to address the sentiment extraction task in a setting where multiple entities are present in a news headline. In an effort to further research in this area, we make publicly available SEntFiN 1.0, a human-annotated dataset of 10,753 news headlines with entity-sentiment annotations, of which 2,847 headlines contain multiple entities, often with conflicting sentiments. We augment our dataset with a database of over 1,000 financial entities and their various representations in news media amounting to over 5,000 phrases. We propose a framework that enables the extraction of entity-relevant sentiments using a feature-based approach rather than an expression-based approach. For sentiment extraction, we utilize 12 different learning schemes utilizing lexicon-based and pretrained sentence representations and five classification approaches. Our experiments indicate that lexicon-based N-gram ensembles are above par with pretrained word embedding schemes such as GloVe. Overall, RoBERTa and finBERT (domain-specific BERT) achieve the highest average accuracy of 94.29% and F1-score of 93.27%. Further, using over 210,000 entity-sentiment predictions, we validate the economic effect of sentiments on aggregate market movements over a long duration.
- Source
- Journal of the Association for Information Science and Technology. 73(2022) no.9, S.1314-1335
-
Laparra, E.; Binford-Walsh, A.; Emerson, K.; Miller, M.L.; López-Hoffman, L.; Currim, F.; Bethard, S.: Addressing structural hurdles for metadata extraction from environmental impact statements (2023)
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- Abstract
- Natural language processing techniques can be used to analyze the linguistic content of a document to extract missing pieces of metadata. However, accurate metadata extraction may not depend solely on the linguistics, but also on structural problems such as extremely large documents, unordered multi-file documents, and inconsistency in manually labeled metadata. In this work, we start from two standard machine learning solutions to extract pieces of metadata from Environmental Impact Statements, environmental policy documents that are regularly produced under the US National Environmental Policy Act of 1969. We present a series of experiments where we evaluate how these standard approaches are affected by different issues derived from real-world data. We find that metadata extraction can be strongly influenced by nonlinguistic factors such as document length and volume ordering and that the standard machine learning solutions often do not scale well to long documents. We demonstrate how such solutions can be better adapted to these scenarios, and conclude with suggestions for other NLP practitioners cataloging large document collections.
- Source
- Journal of the Association for Information Science and Technology. 74(2023) no.9, S.1124-1139
-
Smith, A.: Simple Knowledge Organization System (SKOS) (2022)
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- Abstract
- SKOS (Simple Knowledge Organization System) is a recommendation from the World Wide Web Consortium (W3C) for representing controlled vocabularies, taxonomies, thesauri, classifications, and similar systems for organizing and indexing information as linked data elements in the Semantic Web, using the Resource Description Framework (RDF). The SKOS data model is centered on "concepts", which can have preferred and alternate labels in any language as well as other metadata, and which are identified by addresses on the World Wide Web (URIs). Concepts are grouped into hierarchies through "broader" and "narrower" relations, with "top concepts" at the broadest conceptual level. Concepts are also organized into "concept schemes", also identified by URIs. Other relations, mappings, and groupings are also supported. This article discusses the history of the development of SKOS and provides notes on adoption, uses, and limitations.
-
Wartena, C.; Golub, K.: Evaluierung von Verschlagwortung im Kontext des Information Retrievals (2021)
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- Abstract
- Dieser Beitrag möchte einen Überblick über die in der Literatur diskutierten Möglichkeiten, Herausforderungen und Grenzen geben, Retrieval als eine extrinsische Evaluierungsmethode für die Ergebnisse verbaler Sacherschließung zu nutzen. Die inhaltliche Erschließung im Allgemeinen und die Verschlagwortung im Besonderen können intrinsisch oder extrinsisch evaluiert werden. Die intrinsische Evaluierung bezieht sich auf Eigenschaften der Erschließung, von denen vermutet wird, dass sie geeignete Indikatoren für die Qualität der Erschließung sind, wie formale Einheitlichkeit (im Hinblick auf die Anzahl zugewiesener Deskriptoren pro Dokument, auf die Granularität usw.), Konsistenz oder Übereinstimmung der Ergebnisse verschiedener Erschließer:innen. Bei einer extrinsischen Evaluierung geht es darum, die Qualität der gewählten Deskriptoren daran zu messen, wie gut sie sich tatsächlich bei der Suche bewähren. Obwohl die extrinsische Evaluierung direktere Auskunft darüber gibt, ob die Erschließung ihren Zweck erfüllt, und daher den Vorzug verdienen sollte, ist sie kompliziert und oft problematisch. In einem Retrievalsystem greifen verschiedene Algorithmen und Datenquellen in vielschichtiger Weise ineinander und interagieren bei der Evaluierung darüber hinaus noch mit Nutzer:innen und Rechercheaufgaben. Die Evaluierung einer Komponente im System kann nicht einfach dadurch vorgenommen werden, dass man sie austauscht und mit einer anderen Komponente vergleicht, da die gleiche Ressource oder der gleiche Algorithmus sich in unterschiedlichen Umgebungen unterschiedlich verhalten kann. Wir werden relevante Evaluierungsansätze vorstellen und diskutieren, und zum Abschluss einige Empfehlungen für die Evaluierung von Verschlagwortung im Kontext von Retrieval geben.
-
Yang, T.-H.; Hsieh, Y.-L.; Liu, S.-H.; Chang, Y.-C.; Hsu, W.-L.: ¬A flexible template generation and matching method with applications for publication reference metadata extraction (2021)
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- Abstract
- Conventional rule-based approaches use exact template matching to capture linguistic information and necessarily need to enumerate all variations. We propose a novel flexible template generation and matching scheme called the principle-based approach (PBA) based on sequence alignment, and employ it for reference metadata extraction (RME) to demonstrate its effectiveness. The main contributions of this research are threefold. First, we propose an automatic template generation that can capture prominent patterns using the dominating set algorithm. Second, we devise an alignment-based template-matching technique that uses a logistic regression model, which makes it more general and flexible than pure rule-based approaches. Last, we apply PBA to RME on extensive cross-domain corpora and demonstrate its robustness and generality. Experiments reveal that the same set of templates produced by the PBA framework not only deliver consistent performance on various unseen domains, but also surpass hand-crafted knowledge (templates). We use four independent journal style test sets and one conference style test set in the experiments. When compared to renowned machine learning methods, such as conditional random fields (CRF), as well as recent deep learning methods (i.e., bi-directional long short-term memory with a CRF layer, Bi-LSTM-CRF), PBA has the best performance for all datasets.
- Source
- Journal of the Association for Information Science and Technology. 72(2021) no.1, S.32-45
-
Kempf, K.; Brantl, M.; Meiers, T.; Wolf, T.: Auf der Suche nach dem verborgenen Bild : Künstliche Intelligenz erschließt historische Bibliotheksbestände (2021)
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- Abstract
- Extraktion visueller Merkmale. Basis der Bildähnlichkeitssuche sind die unterschiedlichen visuellen Eigenschaften eines Bildes, seine spezifischen Farb- und Kanteninformationen, welche zunächst in geeigneter Weise erfasst und codiert werden müssen. Dabei kommen sogenannte Deskriptoren zum Einsatz. Die visuelle Information eines Bildes wird in sehr komprimierter Form gespeichert. In unserem Fall hat der zu einem Bild gehörende Deskriptor einen Umfang von nur 96 Byte. Der visuelle Deskriptor codiert sowohl die Farbeigenschaften als auch die spezifischen Kantenmerkmale.
- Issue
- Teil 2: Extraktion visueller Merkmale, effiziente parallele Suche, flankierende Indexierung auf der Basis textueller Metadaten.
-
Asubiaro, T.V.; Onaolapo, S.: ¬A comparative study of the coverage of African journals in Web of Science, Scopus, and CrossRef (2023)
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- Abstract
- This is the first study that evaluated the coverage of journals from Africa in Web of Science, Scopus, and CrossRef. A list of active journals published in each of the 55 African countries was compiled from Ulrich's periodicals directory and African Journals Online (AJOL) website. Journal master lists for Web of Science, Scopus, and CrossRef were searched for the African journals. A total of 2,229 unique active African journals were identified from Ulrich (N = 2,117, 95.0%) and AJOL (N = 243, 10.9%) after removing duplicates. The volume of African journals in Web of Science and Scopus databases is 7.4% (N = 166) and 7.8% (N = 174), respectively, compared to the 45.6% (N = 1,017) covered in CrossRef. While making up only 17.% of all the African journals, South African journals had the best coverage in the two most authoritative databases, accounting for 73.5% and 62.1% of all the African journals in Web of Science and Scopus, respectively. In contrast, Nigeria published 44.5% of all the African journals. The distribution of the African journals is biased in favor of Medical, Life and Health Sciences and Humanities and the Arts in the three databases. The low representation of African journals in CrossRef, a free indexing infrastructure that could be harnessed for building an African-centric research indexing database, is concerning.
- Date
- 22. 6.2023 14:09:06
- Object
- Web of Science
- Source
- Journal of the Association for Information Science and Technology. 74(2023) no.7, S.745-758
-
Breuer, T.; Tavakolpoursaleh, N.; Schaer, P.; Hienert, D.; Schaible, J.; Castro, L.J.: Online Information Retrieval Evaluation using the STELLA Framework (2022)
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- Abstract
- Involving users in early phases of software development has become a common strategy as it enables developers to consider user needs from the beginning. Once a system is in production, new opportunities to observe, evaluate and learn from users emerge as more information becomes available. Gathering information from users to continuously evaluate their behavior is a common practice for commercial software, while the Cranfield paradigm remains the preferred option for Information Retrieval (IR) and recommendation systems in the academic world. Here we introduce the Infrastructures for Living Labs STELLA project which aims to create an evaluation infrastructure allowing experimental systems to run along production web-based academic search systems with real users. STELLA combines user interactions and log files analyses to enable large-scale A/B experiments for academic search.
-
Elsweiler, D.; Kruschwitz, U.: Interaktives Information Retrieval (2023)
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- Abstract
- Interaktives Information Retrieval (IIR) zielt darauf ab, die komplexen Interaktionen zwischen Nutzer*innen und Systemen im IR zu verstehen. Es gibt umfangreiche Literatur zu Themen wie der formalen Modellierung des Suchverhaltens, der Simulation der Interaktion, den interaktiven Funktionen zur Unterstützung des Suchprozesses und der Evaluierung interaktiver Suchsysteme. Dabei ist die interaktive Unterstützung nicht allein auf die Suche beschränkt, sondern hat ebenso die Hilfe bei Navigation und Exploration zum Ziel.
-
Cheng, W.-N.; Khoo, C.S.G.: Information structures in sociology research papers : modeling cause-effect and comparison relations in research objective and result statements (2021)
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- Abstract
- When writing a research paper, the author has to select information to include in the paper to support various arguments. The information has to be organized and synthesized into a coherent whole through relationships and information structures. There is hardly any research on the information structure of research papers, and how information structure supports rhetorical and argument structures. Thus, this study is focused on information organization in the Abstract and Introduction sections of sociology research papers, analyzing the information structure of research objective, question, hypothesis, and result statements. The study is limited to research papers reporting research that investigated cause-effect relations between two concepts. Two semantic frames were developed to specify the types of information associated with cause-effect and comparison relations, and used as coding schemes to annotate the text for different information types. Six link patterns between the two frames were identified-showing how comparisons are used to support the claim that the cause-effect relation is valid. This study demonstrated how semantic frames can be incorporated in discourse analysis to identify deep structures underlying the argument structure. The results carry implications for the knowledge representation of academic research in knowledge graphs, for semantic relation extraction, and teaching of academic writing.
- Source
- Journal of the Association for Information Science and Technology. 72(2021) no.11, S.1367-1385
-
Bräu, R.; Hofmann, K.; Nilson, S.; Zwilling-Seidenstücker, C.: #EveryNameCounts : Die Crowdsourcing-Initiative der Arolsen Archives (2021)
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- Abstract
- Das Crowdsourcing-Projekt #EveryNameCounts der Arolsen Archives begann 2020 als Schulkampagne und wuchs binnen weniger Monate zu einer weltweiten Initiative. Durch die Arbeit der Freiwilligen werden Millionen von Datensätzen zu Konzentrationslagerdokumenten erfasst. Diese Datengrundlage optimiert einerseits die Suche im Online-Archiv der Arolsen Archives und macht anderseits eine auf Massendaten gestützte Forschung erst möglich. Die Arbeit der Freiwilligen geht dabei über reine Datenerhebung weit hinaus. Es entsteht ein digitales Denkmal.
- Source
- Information - Wissenschaft und Praxis. 72(2021) H.4, S.177-184
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Gil-Berrozpe, J.C.: Description, categorization, and representation of hyponymy in environmental terminology (2022)
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- Abstract
- Terminology has evolved from static and prescriptive theories to dynamic and cognitive approaches. Thanks to these approaches, there have been significant advances in the design and elaboration of terminological resources. This has resulted in the creation of tools such as terminological knowledge bases, which are able to show how concepts are interrelated through different semantic or conceptual relations. Of these relations, hyponymy is the most relevant to terminology work because it deals with concept categorization and term hierarchies. This doctoral thesis presents an enhancement of the semantic structure of EcoLexicon, a terminological knowledge base on environmental science. The aim of this research was to improve the description, categorization, and representation of hyponymy in environmental terminology. Therefore, we created HypoLexicon, a new stand-alone module for EcoLexicon in the form of a hyponymy-based terminological resource. This resource contains twelve terminological entries from four specialized domains (Biology, Chemistry, Civil Engineering, and Geology), which consist of 309 concepts and 465 terms associated with those concepts. This research was mainly based on the theoretical premises of Frame-based Terminology. This theory was combined with Cognitive Linguistics, for conceptual description and representation; Corpus Linguistics, for the extraction and processing of linguistic and terminological information; and Ontology, related to hyponymy and relevant for concept categorization. HypoLexicon was constructed from the following materials: (i) the EcoLexicon English Corpus; (ii) other specialized terminological resources, including EcoLexicon; (iii) Sketch Engine; and (iv) Lexonomy. This thesis explains the methodologies applied for corpus extraction and compilation, corpus analysis, the creation of conceptual hierarchies, and the design of the terminological template. The results of the creation of HypoLexicon are discussed by highlighting the information in the hyponymy-based terminological entries: (i) parent concept (hypernym); (ii) child concepts (hyponyms, with various hyponymy levels); (iii) terminological definitions; (iv) conceptual categories; (v) hyponymy subtypes; and (vi) hyponymic contexts. Furthermore, the features and the navigation within HypoLexicon are described from the user interface and the admin interface. In conclusion, this doctoral thesis lays the groundwork for developing a terminological resource that includes definitional, relational, ontological and contextual information about specialized hypernyms and hyponyms. All of this information on specialized knowledge is simple to follow thanks to the hierarchical structure of the terminological template used in HypoLexicon. Therefore, not only does it enhance knowledge representation, but it also facilitates its acquisition.
