Applying Large Language Models (LLMs) to whole-page semantic element detection in e-commerce is prohibitively expensive: typical product pages contain hundreds to thousands of DOM elements, and LLM costs scale linearly with input tokens. We propose a hybrid approach that uses a Graph Neural Network (GraphSAGE) as a pre-filter to reduce the candidate set before LLM processing. Our preliminary results on the Klarna Product Page Dataset show approximately 90
Open Government Data (OGD) publishing is often manual and locally heterogeneous, yielding inconsistent dataset quality and weak traceability of pre-publication decisions. SODPUB is a BPMN-orchestrated workflow-as-a-service that targets interoperability-oriented improvement before portal upload. Data providers submit CSV datasets via REST; the pipeline applies checks derived from W3C guidance, normalises tabular structure, generates CSV on the Web metadata, and produces DCAT-compatible catalog descriptions. Each proposed change is explained and versioned, and the workflow exports a machine-readable process trace aligned with PROV-O so that decisions remain auditable. The current implementation provides an executable BPMN skeleton, initial rule catalogue, and RDF trace generation; REST endpoints and portal connectors are under active development. Planned evaluation measures include acceptance rates of proposed fixes by portal staff, reduction of detectable structural and metadata issues, time per publication, and perceived transparency. The expected outcome is a reusable reference workflow that improves quality while preserving publisher release responsibility.
We introduce SmartScrape, a neuro-symbolic framework for extracting structured information from the web. The task addressed is to extract records that follow a predefined schema from semi-structured sources, such as product web pages. The system integrates a Graph Neural Network (GNN) that scores content elements with an Integer Linear Programming (ILP) constraint solver, which guarantees schema consistency, uniqueness, and additional logical and presentation constraints. We illustrate the method on a basic (product, price) extraction scenario. The implemented system outputs visual proof-carrying justifications, supports drift detection using the stability metric σ (P) , and we also include an ablation study contrasting ILP-based and greedy reasoning modes. On a set of 100 pages, SmartScrape attains 97
Various reports reveal that Web Accessibility remains one of the major concerns that still requires additional awareness. In addition to providing development support in IDEs for creating Web pages, post-development support can also be provided by using Web augmentation techniques. However, the scripts for augmenting Web pages to improve such aspects must currently be developed manually and may require continuous adaptation to the evolving Web sites. In this work, we tackle these challenges by integrating LLMs into the Web augmentation process to improve accessibility concerns on the client side. In particular, our approach first performs an LLM-based accessibility evaluation of Web pages, and based on this feedback, the LLM is instrumented to resolve reported issues.
The quality of life of aging adults has improved over time through the incorporation of technological innovations in their homes, such as voice assistants or monitoring systems. However, current systems do not detect certain patterns that indicate dementia or mild cognitive decline due to limitations in the level of detail of the collected data or in the interpretability of the information already available. In this context, IoT and IoMT devices can be used to collect information that allows automatic detection of possible patterns or alterations. Integration of collected data with artificial intelligence models can improve the interpretability of the data and the detection of behavior patterns that indicate cognitive decline. This thesis aims to design an architecture that uses data from IoT/IoMT devices to identify behavior patterns using artificial intelligence models, with the aim of preventing dementia or mild cognitive decline in aging people through early detection and monitoring strategies.
