Knowledge Graph Generation and Application for Unstructured Data Using Data Processing Pipeline
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
With the rapid advancement of technology and the vast volume of unstructured data available on the Internet, there is a pressing need to extract information from diverse data formats effectively. This is essential as valuable pieces of information may be lost. To address this issue, researchers are using Machine Learning (ML) and Natural Language Processing (NLP) techniques to extract information from unstructured text, including the utilization of Knowledge Graphs (KGs). This paper demonstrates end-to-end experimental studies of KG construction from unstructured text using open-source techniques and concrete real-world examples in different problem domains. The unstructured data underwent a text processing pipeline consisting of coreference resolution, named entity linking, and relationship extraction. The pipeline is designed to support automatic data storage in a graph database known as Neo4j. This storage includes the extracted entities and their relationships. Experiments were conducted on a real-world unstructured BBC News Dataset to analyze the outcome obtained from the pipeline. The experience can facilitate the adoption of KG creation for practitioners to capture valuable information from a large volume of unstructured text. The results from the relationship extraction step using two techniques were evaluated, including extracted entities, relationship types, accuracies of 61.4% with OpenNRE and 87% with REBEL, and processing time. Further, the data processing pipeline was applied to analyze the unstructured dataset from the Transportation Safety Board’s (TSB) Findings for aviation safety analysis. The results showed that structured relationships identified through the pipeline provided valuable indicators, as they captured critical aviation safety information, such as the flight, aircraft type, event, etc. This pipeline can be fine-tuned with a domain-specific knowledge base to provide higher accuracy and better entity detection.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it