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The labor market is highly dependent on vocational and academic education, training, retraining, and further education in order to master challenges such as advancing digitalization and sustainability. Further training is a key factor in ensuring a qualified workforce, the employability of all employees, and, thus, national competitiveness and innovation. In the contribution at hand, we explore an innovative way to derive knowledge about learning pathways by connecting the dots from different data sources of the German labor market. In particular, we focus on the web mining of online resources for German labor market research and education, such as online advertisements, information portals, and official government websites. A key question for working with different data sources is how to find the ground truth and common data structures that can be used to make the data interoperable. We discuss how to classify and summarize web data from different platforms and which methods can be used for extracting data, entities and relationships from online resources on the German labor market to build a network of educational pathways. Our proposed solution is based on the classification of occupations (KldB) and related document codes (DKZ), and combines natural language processing and knowledge graph technologies. Our research provides the foundation for further investigation into educational pathways and linked data for labor market research. While our work focuses on German data, it is also useful for other German-speaking countries and could easily be extended to other languages such as English.

Here we present an improved approach for automated annotation of New Testament corpora with cross-lingual semantic concordance based on Strong’s numbers. Based on already annotated texts, they provide references to the original Greek words. Since scientific editions and translations of biblical texts are often not available for scientific purposes and are rarely freely available, there is a lack of up-to-date training data. In addition, since annotation, curation, and quality control of alignments between these texts are expensive, there is a lack of available biblical resources for scholars. We present two improved approaches to the problem, based on dictionaries and already annotated biblical texts. We provide a detailed evaluation of annotated and unannotated translations. We also discuss a proof of concept based on English and German New Testament translations. The results presented in this paper are novel and, to our knowledge, unique. They show promising performance, although further research is needed., Bibliografiskt granskad

The skills and qualifications of IT professionals are constantly changing and under discussion. In particular, we see the impact of emerging technologies and tools, such as AI, on the labor market and their reflection in the broader scientific community, media and everyday life [2, 4]. Several approaches have discussed how to assess the impact of computer science education from the perspective of education and labor market research [5, 7]. However, in order to uncover the complex dynamics surrounding computer science education, we need to take a closer look at the labor demand as well as the public perception and valuation of IT professionals. Therefore, the poster presents a new method that combines the analysis of online job advertisements (OJA) for computer science occupations at different qualification levels with social media data (Twitter/X and YouTube). As OJA and social media are usually described in unstructured natural language, text mining methods are key to extract information about skills and competencies. We used the Computer Science Ontology (CSO) for annotation [1] and outline further research questions in the wider context of political economy.

Objectives The purpose of this study is to reveal the importance of informatics in continuing vocational education in Germany. The labour market is a field with diverse data structures and multiple applications, for example connecting jobseekers and trainings or jobs. The labour market heavily relies on vocational education and training and advanced vocational qualification to meet challenges, e.g. digitalization. Study Methods Since continuing vocational education and training (CVET) is a structurally important lever for the digital transformation of work, this article presents a methodological procedure for content analysis that provides information about the significance of computer science in unregulated continuing education offerings and in formal continuing education regulations. Findings The question of the extent to which continuing education programs include informaticss topics is investigated, assuming that they can be found in continuing education as cross-cutting topics in a wide variety of thematic contexts. Our results indicating the need for training in computing education. At the same time, computing education offers the highest share of unregulated CVET programs. This could reflect the fact that training and further education regulations in Germany are designed open to technology. Conclusions We present a novel and unique approach to analyze the importance of informatics and digitalization in CVET advertisements and official regulations for the same.

For economics and sociological research, lists of industries and their branches are widely used in research to categorize data and get an overview on different types of industries. However, many different taxonomies and ordering schema exist, due to different research focus but also due to different national scenarios and interests. In this paper, we will focus without loss of generality on regional data from Germany. Manual annotation of textual data is time-consuming and tedious, naturally giving rise to our initial research question, also highly inspired by questions from computational social sciences: How can we automatically categorize textual data, e.g. job advertisements or business Profiles, by industrial sectors We will present an approach towards classification using a pre-trained domain-adapted Transformer model. We find that domain-adapted models generalize better and outperform state of the art non domain-adapted Transformer models on Out-Of-Distribution data. Additionally, we open source two novel data-sets mapping textual data to WZ2008 sections and divisions, enabling further research.

The exegesis of biblical texts is usually a manual task. However, in computational linguistics, automated and artificial-intelligence-based methods are an emerging trend. This paper discusses possible intersections between computer-based natural language processing, analysis of narrative and literary texts, and narrative exegesis of biblical texts and in particular network-based methods. The problem with applying narrative criticism is its variety. The approaches differ slightly, not only due to the different original languages between OT and NT, but also between different literary approaches and schools in different countries. However, narrative approaches are a bridge to digital and network-based methods used in the humanities and computer linguistics. Thus, this paper provides an overview of already established methods and discusses a critical evaluation of possible synergies towards linked data, social network analysis, and knowledge graph approaches. Despite limitations regarding data management and accessibility several methods could be suitable for interdisciplinary research: The detection of time and space, sentiment analysis and the integration of social network analysis into narrative exegesis. Besides, the discussion with computer linguistics may give new perspectives for narrative exegesis.

There are currently several approaches to managing longitudinal data in graphs and social networks. All of them influence the output of algorithms that analyse the data. We present an overview of limitations, possible solutions and open questions for different data schemas for temporal data in social networks, based on a generic RDF-inspired approach that is equivalent to existing approaches. While restricting the algorithms to a specific time point or layer does not affect the results, applying these approaches to a network with multiple time points requires either adapted algorithms or reinterpretation. Thus, with a generic definition of temporal networks as one graph, we will answer the question of how we can analyse longitudinal social networks with centrality measures. In addition, we present two approaches to approximate the change in degree and betweenness centrality measures over time.

The German labor market relies heavily on vocational training, retraining, and continuing education. In order to match training seekers with training offers and to make the available data interoperable, we present a novel approach to automatically detect access to education and training in German training offers and advertisements and identify open research questions and areas for further research. In particular, we focus on (a) general education and school leaving certificates, (b) work experience, (c) previous apprenticeship, and (d) a list of skills provided by the German Federal Employment Agency. This novel approach combines several methods: First, we provide technical terms and classes of the education system that are used synonymously, combining different qualifications and adding obsolete terms. Second, we provide rule-based matching to identify the need for work experience or education. However, not all qualification requirements can be matched due to incompatible data schemas or non-standardized requirements such as initial tests or interviews. Although there are several shortcomings, the presented approach shows promising results for two data sets: training and retraining advertisements.

The labor market heavily relies on both vocational and academiceducation and training, re-training and advanced vocational quali-fication to meet challenges, e.g. the advancing digitalization. Continuing education is a central prerequisite for securing skilledlabor, for ensuring the employability of all employees and thus alsofor national competitiveness and innovation. From the perspective of education and labor market research, several approaches discuss how the impact of computer science education can be evaluated, see for example. Other research focuses on the needs of the labor market, by analysing job advertisements. In order to broaden the perspective on the entire range of CVET courses and to be able to gain new insights from this, our analysis is intended to provide an initial overview of the content of CVET courses in Germany. By that, we offer structured information on skills and competencies that are included in current CVET courses. In future research, this information can be compared to labor market needs, e.g. described in job advertisements, in order to identify education gaps. Since CVET courses are often described in unstructured natural language, text mining-methods are key to extract information on skills and competencies. Here, we present an analysis of 84,310 advertisements for CVET courses from 2023 that are divided into 83 different computer science (CS) related categories.

The labor market is an area with diverse data structures and multiple applications, such as matching job seekers with the right training or job. For this reason, the multilingual classification of European Skills, Competences, Qualifications and Occupations (ESCO) is a good example of the central role of ontologies in this area. However, ESCO cannot provide all the details of local labor market needs and does not provide links to other hierarchies of competences. For example, other taxonomies of occupations and skills are widely used in German-speaking countries, but they are not in a state where they are easily accessible for interoperability and reasoning. In this paper, we present a first version of a German Labor Market Ontology (GLMO) that uses ESCO as a top-level ontology for the target domain. This makes it highly interoperable and comparable to existing ontologies by providing details for the regional structures in German-speaking countries. In addition, we present a detailed evaluation of the provided data and applications, as well as an extensive discussion of future work.

There are many valuable insights on jobs and professions in different sectors of society based on their imminent and ascribed characteristics. Studying such characteristics traditionally was done by action research, surveys, questionnaires, etc. which typically take much time and resources to be concluded. In this study we examine vocational education and training data on Twitter. While we present a generic framework to retrieve, process and analyze tweets, we will discuss two research questions from computational social science: First, how can we make Twitter data interoperable to other available resources, e.g. classifications of occupations, tools and skills? Second, do we have enough data to process job collocational prestige analysis on a geographical basis? This presents a novel approach towards labor market research, making novel data interoperable which has not been considered in previous literature. Our approach and pipeline is generic and could be easily extended to other languages. It also contributes to prestige research by widening the question of ascribed prestige to the question how information on occupations is collocated and what these contextualisations tell us about how occupations are seen.