Semantic Web Company (Austria)

This paper presents an exploratory study that investigates the use of various Large Language Models (LLMs) for the task of taxonomy expansion. Our objective is to enhance the taxonomical structure by querying LLMs for (1) child taxons and (2) alternative labels of existing taxons. Beginning with an incomplete taxonomy, we explore the most effective ways to prompt LLMs exploiting explicit and shared knowledge captured in manually curated taxonomies to provide context for the task at hand. We experiment with different prompting templates, well-recognized taxonomies (EuroVoc, STW, UNESCO), and popular language models (Claude, Claude3, Llama2). Our results suggest feasibility of solving of the proposed task with the modern LLMs and human oversight. Moreover, we observe certain patterns and trends in the performance of the models, noting that it was not possible to identify a single best configuration that would fit all models.

The publishing industry is – like many other industries – undergoing major changes. These changes are mainly based on technical developments and related habits of information consumption. 1 The world of the customers has dramatically changed and as an information service provider, Wolters Kluwer wanted to meet these changes with the best solutions for the customers and their work environment.

This paper presents SemMR, a semantic framework for modelling interactions between human and non-human entities and managing reusable and optimized cultural experiences, towards a shared cultural experience ecosystem that might seamlessly accommodate mixed reality experiences. The SemMR framework synthesizes and integrates interaction data into semantically rich reusable structures and facilitates the interaction between different types of entities in a symbiotic way, within a large, virtual, and fully experiential open world, promoting experience sharing at the user level, as well as data/application interoperability and low-effort implementation at the software engineering level. The proposed semantic framework introduces methods for low-effort implementation and the deployment of open and reusable cultural content, applications, and tools, around the concept of cultural experience as a semantic trajectory or simply, experience as a trajectory (eX-trajectory). The methods facilitate the collection and analysis of data regarding the behaviour of users and their interaction with other users and the environment, towards optimizing eX-trajectories via reconfiguration. The SemMR framework supports the synthesis, enhancement, and recommendation of highly complex reconfigurable eX-trajectories, while using semantically integrated disparate and heterogeneous related data. Overall, this work aims to semantically manage interactions and experiences through the eX-trajectory concept, towards delivering enriched cultural experiences.

With the spread of semantic technologies more and more companies manage their own knowledge graphs (KG), applying them, among other tasks, to text analysis. However, the proprietary KGs are by design domain specific and do not include all the different possible meanings of the words used in a corpus. In order to enable the usage of these KGs for automatic text annotations, we introduce a robust method for discriminating word senses using sense indicators found in the KG: types, synonyms and/or hypernyms. The method uses collocations to induce word senses and to discriminate the sense included in the KG from the other senses, without the need for information about the latter, or the need for manual effort. On the two datasets created specially for this task the method outperforms the baseline and shows accuracy above 80%.

The creation and the improvement of tools that cover exploratory and visualization tasks for Linked Data were one of the major goals focused in the LOD2 project. Tools that support those tasks are regarded as essential for the Web of Data, since they can act as a user-oriented starting point for data customers. During the project, several development efforts were made, whose results either facilitate the exploration and visualization directly (such as OntoWiki, the Pivot Browser) or can be used to support such tasks. In this chapter we present the three selected solutions rsine, CubeViz and Facete.