arXiv subjects

Elisavet Koutsiana

Publications and source records attributed to Elisavet Koutsiana.

5 recordsLinked to original sources

arXiv2025

Knowledge Prompting: How Knowledge Engineers Use Large Language Models

Despite many advances in knowledge engineering (KE), challenges remain in areas such as engineering knowledge graphs (KGs) at scale, keeping up with evolving domain knowledge, multilingualism, and multimodality. Recently, KE has used LLMs to support semi-automatic tasks, but the most effective use of LLMs to support knowledge engineers across the KE activites is still in its infancy. To explore the vision of LLM copilots for KE and change existing KE practices, we conducted a multimethod study during a KE hackathon. We investigated participants' views on the use of LLMs, the challenges they face, the skills they may need to integrate LLMs into their practices, and how they use LLMs responsibly. We found participants felt LLMs could contribute to improving efficiency when engineering KGs, but presented increased challenges around the already complex issues of evaluating the KE tasks. We discovered prompting to be a useful but undervalued skill for knowledge engineers working with LLMs, and note that natural language processing skills may become more relevant across more roles in KG construction. Integrating LLMs into KE tasks needs to be mindful of potential risks and harms related to responsible AI. Given the limited ethical training, most knowledge engineers receive solutions such as our suggested `KG cards' based on data cards could be a useful guide for KG construction. Our findings can support designers of KE AI copilots, KE researchers, and practitioners using advanced AI to develop trustworthy applications, propose new methodologies for KE and operate new technologies responsibly.

cs.HC↗

arXiv2025

Agreeing and Disagreeing in Collaborative Knowledge Graph Construction: An Analysis of Wikidata

In this work, we study disagreements in discussions around Wikidata, an online knowledge community that builds the data backend of Wikipedia. Discussions are essential in collaborative work as they can increase contributor performance and encourage the emergence of shared norms and practices. While disagreements can play a productive role in discussions, they can also lead to conflicts and controversies, which impact contributor' well-being and their motivation to engage. We want to understand if and when such phenomena arise in Wikidata, using a mix of quantitative and qualitative analyses to identify the types of topics people disagree about, the most common patterns of interaction, and roles people play when arguing for or against an issue. We find that decisions to create Wikidata properties are much faster than those to delete properties and that more than half of controversial discussions do not lead to consensus. Our analysis suggests that Wikidata is an inclusive community, considering different opinions when making decisions, and that conflict and vandalism are rare in discussions. At the same time, while one-fourth of the editors participating in controversial discussions contribute legitimate and insightful opinions about Wikidata's emerging issues, they respond with one or two posts and do not remain engaged in the discussions to reach consensus. Our work contributes to the analysis of collaborative KG construction with insights about communication and decision-making in projects, as well as with methodological directions and open datasets. We hope our findings will help managers and designers support community decision-making and improve discussion tools and practices.

cs.HC↗

arXiv2025

Talking Wikidata: Communication patterns and their impact on community engagement in collaborative knowledge graphs

We study collaboration patterns of Wikidata, one of the world's largest open source collaborative knowledge graph (KG) communities. Collaborative KG communities, play a key role in structuring machine-readable knowledge to support AI systems like conversational agents. However, these communities face challenges related to long-term member engagement, as a small subset of contributors often is responsible for the majority of contributions and decision-making. While prior research has explored contributors' roles and lifespans, discussions within collaborative KG communities remain understudied. To fill this gap, we investigated the behavioural patterns of contributors and factors affecting their communication and participation. We analysed all the discussions on Wikidata using a mixed methods approach, including statistical tests, network analysis, and text and graph embedding representations. Our findings reveal that the interactions between Wikidata editors form a small world network, resilient to dropouts and inclusive, where both the network topology and discussion content influence the continuity of conversations. Furthermore, the account age of Wikidata members and their conversations are significant factors in their long-term engagement with the project. Our observations and recommendations can benefit the Wikidata and semantic web communities, providing guidance on how to improve collaborative environments for sustainability, growth, and quality.

cs.SI↗

arXiv2023

Prompting Datasets: Data Discovery with Conversational Agents

Can large language models assist in data discovery? Data discovery predominantly happens via search on a data portal or the web, followed by assessment of the dataset to ensure it is fit for the intended purpose. The ability of conversational generative AI (CGAI) to support recommendations with reasoning implies it can suggest datasets to users, explain why it has done so, and provide information akin to documentation regarding the dataset in order to support a use decision. We hold 3 workshops with data users and find that, despite limitations around web capabilities, CGAIs are able to suggest relevant datasets and provide many of the required sensemaking activities, as well as support dataset analysis and manipulation. However, CGAIs may also suggest fictional datasets, and perform inaccurate analysis. We identify emerging practices in data discovery and present a model of these to inform future research directions and data prompt design.

cs.HC↗

arXiv2023

OEKG: The Open Event Knowledge Graph

Accessing and understanding contemporary and historical events of global impact such as the US elections and the Olympic Games is a major prerequisite for cross-lingual event analytics that investigate event causes, perception and consequences across country borders. In this paper, we present the Open Event Knowledge Graph (OEKG), a multilingual, event-centric, temporal knowledge graph composed of seven different data sets from multiple application domains, including question answering, entity recommendation and named entity recognition. These data sets are all integrated through an easy-to-use and robust pipeline and by linking to the event-centric knowledge graph EventKG. We describe their common schema and demonstrate the use of the OEKG at the example of three use cases: type-specific image retrieval, hybrid question answering over knowledge graphs and news articles, as well as language-specific event recommendation. The OEKG and its query endpoint are publicly available.

cs.AI↗