Knowledge Graphs and their Role in the Knowledge Engineering of the 21st Century (Dagstuhl Seminar 22372)
Bibliographic record
Abstract
This report documents the programme and outcomes of Dagstuhl Seminar 22372 "Knowledge Graphs and their Role in the Knowledge Engineering of the 21st Century" held in September 2022. The seminar aimed to gain a better understanding of the way knowledge graphs are created, maintained, and used today, and identify research challenges throughout the knowledge engineering life cycle, including tasks such as modelling, representation, reasoning, and evolution. The participants identified directions of research to answer these challenges, which will form the basis for new methodologies, methods, and tools, applicable to varied AI systems in which knowledge graphs are used, for instance, in natural language processing, or in information retrieval. The seminar brought together a snapshot of the knowledge engineering and adjacent communities, including leading experts, academics, practitioners, and rising stars in those fields. It fulfilled its aims - the participants took inventory of existing and emerging solutions, discussed open problems and practical challenges, and identified ample opportunities for novel research, technology transfer, and inter-disciplinary collaborations. Among the topics of discussion were: designing engineering methodologies for knowledge graphs, integrating large language models and structured data into knowledge engineering pipelines, neural methods for knowledge engineering, responsible use of AI in knowledge graph construction, other forms of knowledge representations, and generating user and developer buy-in. Besides a range of joint publications, hackathons, and project proposals, the participants suggested joint activities with other scientific communities, in particular those working on large language models, generative AI, FAccT (fairness, accountability, transparency), and human-AI interaction. The discussions were captured in visual summaries thanks to Catherine Allan - you can find more about her work at https://www.catherineallan.co.uk/. The summaries are arrayed throughout this report. Lastly, knowledge about the seminar is captured in Wikidata at https://www.wikidata.org/wiki/Q113961931
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.029 | 0.012 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".