Text Reuse reveals meaningful reiterations of text in large corpora. Humanities researchers use text reuse to study, e.g., the posterior reception of influential texts or to reveal evolving publication practices of historical media. This research is often supported by interactive visualizations which highlight relations and differences between text segments. In this paper, we build on earlier work in this domain. We present impresso Text Reuse at Scale, the to our knowledge first interface which integrates text reuse data with other forms of semantic enrichment to enable a versatile and scalable exploration of intertextual relations in historical newspaper corpora. The Text Reuse at Scale interface was developed as part of the impresso project and combines powerful search and filter operations with close and distant reading perspectives. We integrate text reuse data with enrichments derived from topic modeling, named entity recognition and classification, language and document type detection as well as a rich set of newspaper metadata. We report on historical research objectives and common user tasks for the analysis of historical text reuse data and present the prototype interface together with the results of a user evaluation.

In medicine and biomedical research, sex- and gender-related aspects are ubiquitous. If not considered adequately, a lower quality of research data can be expected together with a lower generalizability of study results with real-world settings. From a translational perspective, a lack of sex- and gender-sensitivity in acquired data can have negative implications for diagnosis, treatment (outcome and side effects), and risk prediction. To establish improved recognition and reward settings we set out to develop a pilot of systemic sex and gender awareness in a German medical faculty, with actions such as implementing equality in routine clinical practice and research, as well as in scientific practice (incl. science education). We believe that the change of culture will have a positive effect on research outcomes, lead to a rethinking in the scientific domain, foster sex- and gender-related clinical studies, and influence the design of good scientific practices.

Integration of data from multiple domains can greatly enhance the quality and applicability of knowledge generated in analysis workflows. However, working with health data is challenging, requiring careful preparation in order to support meaningful interpretation and robust results. Ontologies encapsulate relationships between variables that can enrich the semantic content of health datasets to enhance interpretability and inform downstream analyses.
We developed an R package for electronic health data preparation, \"eHDPrep,\" demonstrated upon a multimodal colorectal cancer dataset (661 patients, 155 variables; Colo-661); a further demonstrator is taken from The Cancer Genome Atlas (459 patients, 94 variables; TCGA-COAD). eHDPrep offers user-friendly methods for quality control, including internal consistency checking and redundancy removal with information-theoretic variable merging. Semantic enrichment functionality is provided, enabling generation of new informative \"meta-variables\" according to ontological common ancestry between variables, demonstrated with SNOMED CT and the Gene Ontology in the current study. eHDPrep also facilitates numerical encoding, variable extraction from free text, completeness analysis, and user review of modifications to the dataset.
eHDPrep provides effective tools to assess and enhance data quality, laying the foundation for robust performance and interpretability in downstream analyses. Application to multimodal colorectal cancer datasets resulted in improved data quality, structuring, and robust encoding, as well as enhanced semantic information. We make eHDPrep available as an R package from CRAN (https://cran.r-project.org/package = eHDPrep) and GitHub (https://github.com/overton-group/eHDPrep).

This paper presents ARCA, a software system that enables semantic search and exploration over a book catalog. The main purpose of this work is twofold: to propose a general paradigm for a semantic enrichment workflow and to evaluate a visual approach to information retrieval based on extracted information and existing knowledge graphs. ARCA has been designed and implemented following a user-centered design approach. Two different releases of the system have incrementally and iteratively developed and evaluated. The first release has evaluated the quality and usefulness of the extracted data. The second release, whose design was a refinement based on the previous evaluation results, was assessed by several users. Moreover, a comparative test with other information retrieval systems was conducted in order to study the potential added-value of the system. ARCA is employed in a real editorial scenario to visually search and explore the books of a publishing house.

Code search is a common practice for developers during software implementation. The challenges of accurate code search mainly lie in the knowledge gap between source code and natural language (i.e., queries). Due to the limited code-query pairs and large code-description pairs available, the prior studies based on deep learning techniques focus on learning the semantic matching relation between source code and corresponding description texts for the task, and hypothesize that the semantic gap between descriptions and user queries is marginal. In this work, we found that the code search models trained on code-description pairs may not perform well on user queries, which indicates the semantic distance between queries and code descriptions. To mitigate the semantic distance for more effective code search, we propose QueCos, a Query-enriched Code search model. QueCos learns to generate semantic enriched queries to capture the key semantics of given queries with reinforcement learning (RL). With RL, the code search performance is considered as a reward for producing accurate semantic enriched queries. The enriched queries are finally employed for code search. Experiments on the benchmark datasets show that QueCos can significantly outperform the state-of-the-art code search models.

Cultural heritage images are among the primary media for communicating and preserving the cultural values of a society. The images represent concrete and abstract content and symbolise the social, economic, political, and cultural values of the society. However, an enormous amount of such values embedded in the images is left unexploited partly due to the absence of methodological and technical solutions to capture, represent, and exploit the latent information. With the emergence of new technologies and availability of cultural heritage images in digital formats, the methodology followed to semantically enrich and utilise such resources become a vital factor in supporting users need. This paper presents a methodology proposed to unearth the cultural information communicated via cultural digital images by applying Artificial Intelligence (AI) technologies (such as Computer Vision (CV) and semantic web technologies). To this end, the paper presents a methodology that enables efficient analysis and enrichment of a large collection of cultural images covering all the major phases and tasks. The proposed method is applied and tested using a case study on cultural image collections from the Europeana platform. The paper further presents the analysis of the case study, the challenges, the lessons learned, and promising future research areas on the topic.

This study examines whether Chinese complement coercion sentences with aspectual verbs will elicit processing difficulty during real-time comprehension. Complement coercion is a linguistic phenomenon in which certain verbs (e.g., start, enjoy), requiring an event-denoting complement, are combined with an entity-denoting complement (e.g., book), as in The author started a book. Previous studies have reported that the entity-denoting complement elicited processing difficulty following verbs that require event argument compared with verbs that do not (e.g., The author wrote a book). While the processing of complement coercion has been extensively studied in Indo-European languages such as English and German, it is relatively under-researched in Sino-Tibetan languages such as Mandarin Chinese. Given the fact that there are many linguistic elements behaving distinctly in the different language families, for instance, verbs with respect to their semantic properties and syntactic representations of the complement, it is meaningful to investigate whether or not the existing linguistic differences have any effect on the processing of complement coercion in Mandarin. With this research goal, we recorded self-paced reading time of 61 native Mandarin speakers to investigate the processing of the entity-denoting complement in sentences with three different verb types (aspectual verbs which require an event-denoting complement, preferred verbs which denote a preferred interpretation of the aspectual expressions, and non-preferred verbs which denote a non-preferred but plausible interpretation of the aspectual expressions), as exemplified in // gù-kè kāi-shǐ/tián-xiě/chá-kàn zhè-fèn wèn-juàn \"The customer started/filled in/checked the questionnaire.\" It was found that the entity noun complement (e.g., zhè-fèn wèn-juàn \"the questionnaire\") elicited significantly longer reading times in coercion sentences than non-coercion counterparts. The results are compatible with the previous findings in English that complement coercion sentences impose processing cost during real-time comprehension. The study contributes empirical evidence to coercion studies cross-linguistically.

In the time it takes a human life sciences researcher to read one research article machines can process hundreds of thousands of articles. An unco-ordinated army of bots, crawlers, and other software agents are active day and night on the Internet discovering, ingesting, and analyzing research content. Many of these agents are designed to help researchers rapidly filter the ever-expanding research record and surface the articles and findings most relevant to their work. For these software agents to be most effective, they need to understand the content they are reading in a manner similar to an expert human reader. (What are the main concepts being discussed and what are the main findings asserted? What is this research article telling us that is new and what is supporting or contradicting past findings?). This is where semantic enrichment comes into play - semantic enrichment adds structured machine-readable metadata to life science articles to assist software agents in \'reading\' the content in a manner similar to a human researcher. In the present study, I\'ll define the mechanism of semantic enrichment of life sciences content, examine the benefits it is bringing to researchers today, and preview promising avenues for future benefits.

Biological and biomedical ontologies and terminologies are used to organize and store various domain-specific knowledge to provide standardization of terminology usage and to improve interoperability. The growing number of such ontologies and terminologies and their increasing adoption in clinical, research and healthcare settings call for effective and efficient quality assurance and semantic enrichment techniques of these ontologies and terminologies. In this editorial, we provide an introductory summary of nine articles included in this supplement issue for quality assurance and enrichment of biological and biomedical ontologies and terminologies. The articles cover a range of standards including SNOMED CT, National Cancer Institute Thesaurus, Unified Medical Language System, North American Association of Central Cancer Registries and OBO Foundry Ontologies.

The study aims to explore the processing pattern of Mandarin Chinese sentences with complement coercion. Complement coercion is a known linguistic phenomenon in which some verbs, semantically requiring an event-denoting complement, are combined with an entity-denoting complement, as in Mary began the book. The combination (i.e., event-selecting verb + entity-denoting noun) has been reported to involve type mismatch, and thus elicits processing difficulty. While the phenomenon has been extensively studied in Indo-European languages, such as English and German, it is debatable if the phenomenon exists in a typologically distinct language from English (e.g., in structural complexity of words), such as Mandarin. To provide empirical evidence, the study conducted a self-paced reading experiment to compare the processing patterns of coercion sentences and non-coercion controls in Mandarin. The results showed longer reading times for the coercion sentences than the non-coercion counterparts, which supported previous findings about the processing difficulty of complement coercion.