Finnish Parliament on the Semantic Web
Bibliographic record
Abstract
This paper introduces the system ParliamentSampo – Parliament of Finland on the Semantic Web, a Linked Open Data (LOD) service, data infrastructure, and semantic portal for studying Finnish political culture, language, and networks of the Members of Parliament (MP). The article presents the vision behind the system, the LOD service, and explores the possibilities to utilize it in research and application development. A knowledge graph of linked data has been created based on ca. 962 000 speeches in all plenary sessions of the Parliament of Finland in 1907—2021; the data is also available in XML format, utilizing the new international Parla-CLARIN format. For the first time, the entire time series of the Finnish parliamentary speeches has been converted into data and a data service in a unified format. In addition, the speeches have been interlinked with another knowledge graph created from the database of the MPs and enriched from other data sources into a broader ontology-based data service. The paper shows how the LOD service SPARQL endpoint can be used to research parliamentary culture, the use of political language, and networks of politicians through data analysis. The service endpoint can also be used to develop applications for different user groups without programming skills, such as the ParliamentSampo semantic portal introduced in the paper, too. This application aims to make political decision making more transparent to the general public, media, politicians, and other end users.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".