APROVeD: Automatic Provenance Derivation

As interest in provenance grows among the Semantic Web community, it is recognized as a useful tool across many domains. However, existing automatic provenance collection techniques are not universally applicable. Most existing methods either rely on (low-level) observed provenance, or require that the user discloses formal work ows. In this project, we develop a new approach for automatic derivation of provenance, at multiple levels of granularity. To accomplish this, we detect entity derivations, relying on clustering algorithms, linked data and semantic similarity. The resulting derivations are structured in compliance with the Provenance Data Model (PROV-DM). While the proposed approach is purposely kept general, allowing adaptation in many use cases, we provide an implementation for one of these use cases, namely discovering the sources of news articles. With this implementation, we were able to detect 73% of the original sources of 410 news stories, at 68% precision.

For more information, we refer to the latest publication about this project:
Automatic Discovery of High-Level Provenance Using Semantic Similarity
available at SpringerLink

Do note that this application is still in its development phase, and plans to include more features of PROV in the near future.