Accepted Workshops

Website: https://lm-kbc.github.io/workshop2024/

Organizers: Jan-Christoph Kalo, Sneha Singhania, Simon Razniewski and Jeff Z. Pan

Duration: Full Day

Language models such as chatGPT, BERT, and T5, have demonstrated remarkable outcomes in numerous AI applications. Research has shown that these models implicitly capture vast amounts of factual knowledge within their parameters, resulting in a remarkable performance in knowledge-intensive applications. The seminal paper “Language Models as Knowledge Bases?” sparked interest in the spectrum between language models (LMs) and knowledge graphs (KGs), leading to a diverse range of research on the usage of LMs for knowledge base construction, including (i) utilizing pre-trained LMs for knowledge base completion and construction tasks, (ii) performing information extraction tasks, like entity linking and relation extraction, and (iii) utilizing KGs to support LM based applications. The 2nd Workshop on Knowledge Base Construction from Pre-Trained Language Models (KBC-LM) workshop aims to give space to the emerging academic community that investigates these topics, host extended discussions around the LM-KBC Semantic Web challenge, and enable an informal exchange of researchers and practitioners.

Website: https://om.ontologymatching.org/2024

Organizers: Ernesto Jimenez-Ruiz, Oktie Hassanzadeh, Cassia Trojahn, Sven Hertling, Huanyu Li, Pavel Shvaiko and Jérôme Euzenat

Duration: Full Day

Ontology matching is a key interoperability enabler for the Semantic Web, as well as a useful technique in some classical data integration tasks dealing with the semantic heterogeneity problem. It takes ontologies as input and determines as output an alignment, that is, a set of correspondences between the semantically related entities of those ontologies. These correspondences can be used for various tasks, such as ontology merging, data interlinking, query answering or navigation over knowledge graphs. Thus, matching ontologies enables the knowledge and data expressed with the matched ontologies to interoperate.  The workshop encourages participation from academia, industry and user institutions with the emphasis on theoretical and practical aspects of ontology matching. On one side, we expect representatives from industry and user organizations to present business cases and their requirements for ontology matching. On the other side, we expect academic participants to present their approaches vis-a-vis those requirements. The workshop provides an informal setting for researchers and practitioners from different related initiatives to meet and benefit from each other’s work and requirements.

Website: https://sites.google.com/view/semiim-2024

Organizers: Eduard Kamburjan, Arild Waaler, Baifan Zhou and Ernesto Jimenez-Ruiz

Duration: Full Day

Industrial digitalization requires advanced information models and aims at fully computerized, software-driven, automation of production processes and enterprise-wide integration of software components. Such trends and the technological and industrial developments that come with it are an important part of Industry 4.0 and industrial Internet of Things.

The SemIIM workshop gathers researchers and practitioners who work on addressing the challenges of industrial Information Modelling (IM) with the help of semantic technologies.

We in particular invite IM experts who are excited and committed to push the frontiers of IM further and support modern industry in its current technological transformation. In our workshop we welcome novel methods, systems, solutions, experience, and practice for semantic industrial information modelling.

Website: https://sci-k.github.io/2024/

Organizers: Andrea Mannocci, Francesco Osborne, Georg Rehm, Angelo Antonio Salatino and Sonja Schimmler

Duration: Full Day

The Sci-K workshop provides a forum for researchers to collaborate on improving scientific knowledge representation, discovery, and assessment. A key focus is developing Scientific Knowledge Graphs (SKGs) that provide flexible yet structured ways to represent scholarly information. This involves creating ontologies that effectively capture the complexities of knowledge across different SKGs. Making information within SKGs easily discoverable is crucial, necessitating solutions for extracting key elements, integrating diverse sources, pinpointing connections, and resolving inconsistencies. Finally, due to the ever-growing volume of research, comprehensive systems are needed to measure the impact and value of diverse scientific outputs and researchers.

Website: https://odpa.github.io/workshop-on-ontology-design-and-patterns/2024/index.html

Organizers: Eva Blomqvist, Cogan Shimizu, Andrea Giovanni Nuzzolese, Anna Sofia Lippolis, Martin G. Skjæveland and Loris Bozzato

Duration: Full Day

The WOP workshop series covers issues related to quality in ontology design and ontology design patterns (ODPs) for data and knowledge engineering in Semantic Web. The increased attention to ODPs in recent years through their interaction with emerging trends of Semantic Web such as knowledge graphs can be attributed to their benefit for knowledge engineers and Semantic Web developers. Such benefits come in the form of a direct link to requirements, reuse, guidance, and better communication. The workshop’s aim is thus not just: 1) providing an arena for discussing patterns, pattern-based ontologies, systems, datasets, but also 2) broadening the pattern community by developing its own “discourse” for discussing and describing relevant problems and their solutions. This year, we are particularly interested in modularity and ontology engineering with LLMs.

Website: https://voila-workshop.github.io/2024/

Organizers: Bo Fu, Patrick Lambrix, Huanyu Li, Susana Nunes and Catia Pesquita

Duration: Half Day

A picture is worth a thousand words, we often say, yet many areas are in demand of sophisticated visualization techniques, and the Semantic Web is not an exception. The size and complexity of semantic data — Ontologies, Linked Data and Knowledge Graphs — constantly grows and the diverse backgrounds of the users and application areas multiply at the same time. Providing users with visual representations and intuitive interaction techniques can significantly aid the exploration and understanding of the domains and knowledge represented by semantic data.

There is no one-size-fits-all solution but different use cases demand different visualization and interaction techniques. Ultimately, providing better user interfaces, visual representations and interaction techniques will foster user engagement and likely lead to higher quality results in different applications employing semantics, and proliferate the consumption of Ontologies, Linked Data and Knowledge Graphs.

Website: https://w3id.org/soflim4kg

Organizers: David Chaves-Fraga, Oscar Corcho, Eduard Kamburjan, Coen De Roover and Paco Nathan

Duration: Half Day

The Software Lifecycle Management for KG workshop (SofLiM4KG) aims to collect experiences in successful and abandoned knowledge graph projects from this perspective to (a) carve out the specifics in knowledge graph engineering that pose challenges beyond software engineering practices, (b) to establish best practices and anti-patterns for the community, and (c) build the foundations for the systematic investigation of the connection to software engineering, as well as qualitative and quantitative studies in project management of knowledge graphs.

Website: https://2024.rage-kg.org

Organizers: Daniil Dobriy, Umair Ahmed, Marta Sabou, Axel Polleres and Francesco Osborne

Duration: Half Day

RAGE-KG explores the state of the art and goes beyond in integrating Retrieval-Augmented Generation (RAG) with Knowledge Graphs (KGs) as well as the synergies between Large Language Models (LLMs) and the Linked Open Data (LOD) ecosystem. We aim to foster innovative RAG architectures relying on Semantic Web standards and new approaches to make LOD usable by LLMs, enhancing their ability to generate reliable, verifiable, and context-aware responses based on structured, decentralized, and authoritative data sources.