Tematy projektów WSHOP -- wiosna 2025/2026

: Possibility of extending it to master thesis

: Quick project

: Linked to international scientific project

: Linked to JU-internal scientific project

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Namespace in the wiki: FIXME

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Namespace in the wiki: chcloud

The goal of the project: Validate the usability of Wikidata as a central point of cultural heritage data cloud

Technology: RDF/Semantic Web, some programming (Python preferred)

Description: Wikidata has many links to external sources of information (URIs/foreign keys), but are these links correct? Can additional information be extracted from these sources, or do these pages not contain any data that can be processed automatically? Previous project (chcloud) started exploration of this topic (validation of some links, basic data extraction). Now, we want to continue it and: (a) check whether this process can be automated, (b) prepare a knowledge graph that will combine this data into a single cultural heritage data cloud [for the purposes of the project, we will probably limit it to individuals associated with the University], (c) evaluate such a graph (including comparison with a graph containing only data from Wikidata)

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Namespace in the wiki: iconclass

The goal of the project: Extend the existing prototype of ICONCLASS-based classification and recommendation modules

Technology: RDF/Semantic Web, Python, ML basics

Description: Iconclass is a very complex system for classifying objects depicted in artworks, covering real objects, various scenes, and abstract concepts. It has been used for many years by experts to tag artworks. We want to help them do this, so we have created a prototype tool that first assigns tags (classification) and then recommends related artworks. As part of this project, we want to expand this tool. Directions for development: (a) Detection fine-tuning: find Iconclass-labeled datasets and fine-tune YOLO (or other object-detection model), (b) Multimodal classification: not only YOLO, but also textual metadata, iconographic labels, neural image features, and even visin-language embeddings like CLIP can help in better classification, (c) expand rule engine for abstract codes inference: rules can be mined semi-automatically from large Iconclass-labeled corpora (with frequent pattern mining over code co-occurrences), (d) recommendation module: create unified recommender (now, we have three recommenders), add explainability layer. There is also a place for your ideas :)

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Namespace in the wiki: graphrecs

The goal of the project: Extend the existing recommendation workflow to include expert knowledge, an evaluation interface with explanations for experts, and user interface for final users

Technology: RDF/Semantic Web, SPARQL, Python, recommendation systems, knowledge graph embeddings, XAI

Description: We have developed a prototypical recommendation system for cultural heritage knowledge graphs and evaluated it within CHExRISH project (see this paper). Even if the results obtained are promising, it turned out that our recommendations do not coincide with those of the experts (i.e., the experts propose different nodes in the graph than the model we prepared). Why? Are experts interested in specific relationships in the graph? Do experts focus only on the significant nodes? Or do experts use some knowledge that is not reflected in the graph? (a) Investigating this is the first goal of the project. In addition: (b) based on the results of the investigation, we want to extend the recommendation module to take them into account (this may require improving SPARQL-based filtering), © we want to prepare an interface for experts that will allow them to evaluate recommendations and enter their own recommendations (by selecting nodes in the graph), (d) the whole process may ultimately become a closed workflow that iteratively improves the model in accordance with the knowledge provided by experts. We also want to (e) prepare an interface for end users: based on the selected node, it suggests several related nodes.

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Namespace in the wiki: graphpaths

The goal of the project: Prepare a tool that visualizes connections between two or more nodes in a graph along with their relevant context

Technology: RDF/Semantic Web, some programming (Python is preffered), vizualization, LLMs

Description: At the input, we have an arbitrarily large knowledge graph in RDF format and two or more nodes, e.g., a target and its recommendation as in the project described in this paper. The goal of the project is to develop visualizations that will help the user understand the real connections between these nodes. As part of the project, we want to explore the following scenarios: (a) direct paths: find the shortest path and visualize it, (b) informed-shortest path: in the above-mentioned project, the recommendation module provides information why the following node was proposed; this information can be used in the visualization, © provide the context: check which nodes in the environment are relevant (e.g., using centrality measures) and add them to the visualization, (d) use more than two nodes: e.g., use target and more than one recommendation, (e) we also want to explore the possibility of using language models to summarize the generated paths/visualizations.