The increasing demand for accurate and scalable semantic enrichment of scientific publications has driven the need for hybrid frameworks that combine database-centric processing with the semantic power of Large Language Models (LLMs). We present the DETEXA+LLM Semantic Enrichment Workflow Builder, an extension of the DETEXA framework, which allows users to define, execute, and visualize hybrid text analysis workflows over relational backends. Our system supports declarative construction of pipelines that integrate regex-based pattern matching, classification, and selective LLM invocation via automatically generated Python UDFs and YeSQL queries. A visual web interface enables users to configure metadata enrichment workflows with minimal programming effort. We demonstrate the utility of our approach through three diverse and essential use cases for digital libraries: (i) classification of Data and Software Availability Statements (DAS), (ii) extraction of Protein Data Bank (PDB) codes, and (iii) mining of funding source mentions. Our results show that combining rule-based logic with targeted LLM calls improves both accuracy and efficiency, making the system suitable for large-scale scholarly metadata extraction tasks.
The growing need for scalable research resource discovery in digital libraries has motivated the development of frameworks that combine traditional processing with large language models (LLMs). Building upon DETEXA, a declarative and extensible SQL-based text analytics framework, we propose a key extension: the integration of LLMs through User-Defined Functions (UDFs). This architecture preserves the scalability and declarative nature of SQL workflows while selectively invoking LLMs for tasks that require deep semantic understanding. To balance the trade-off between efficiency and precision, we explore three processing alternatives: LLM-only pipeline, pattern-based approach, and a hybrid model with selective LLM invocation. As a running example, we focus on the extraction of Data and Software Availability Statements from research publications, demonstrating the benefits of hybrid semantic enrichment. Experimental results show that the proposed approach achieves significantly improved accuracy over pattern-based methods, while maintaining runtime performance compared to full LLM-based solutions. Our framework offers a path toward efficient, extensible, and semantically enhanced text analytics pipelines.
We study the WCAG compliancy and state of the metadata of PDF documents released under the Dutch Open Government Act (Woo). The results show that, in line with previous research on WCAG compliancy of PDF documents, only a fraction (0.2
OCR of historical handwritten documents is still a challenging task and an active research field due to the relatively low recognition accuracy achieved when processing manuscripts of different writing styles. In this work, we study the use of Large Language Models (LLMs) for correcting OCR in old Greek handwritten documents. We analyze two different old Greek datasets using a Deep Network based OCR along with several well-known and easy-to-use LLMs for correcting the output. Additionally, we generate synthetic erroneous texts and modify the LLM prompts to further investigate how LLMs perform in correcting noisy old Greek text. Experimental results show the potential of LLMs for OCR correction of old Greek handwritten documents, especially in cases where the recognition results are relatively poor.
One method to aid the understanding of a document corpus is to try constructing automatically a word/term/knowledge map for that corpus by analyzing the contents of the documents. Several methods have been proposed in the literature for this task. In this paper we investigate a novel method that is based on Association Rule Mining (ARM). ARM was proposed for databases, for structured data in general, as a method for data mining, e.g. for market basket analysis. Here we investigate its application over documents. In particular, we leverage association rule mining, through the Apriori algorithm, to find pairs of terms that co-occur in documents and their association. Each rule is characterized by its confidence and support. Then we map these rules to graphical elements. A key merit of the approach is that the user can interactively change the confidence and support threshold and obtain a different visualization. The evaluation over small datasets, up to datasets with 125.654 distinct words, showed that this approach is feasible and can produce maps that show the dominating words and connections. Source code: https://github.com/EfthimisM/AssociationMaps Video: https://youtu.be/eN9VrmmS6Ls.
