ADOG is a system focused on leveraging the structure of a well-connected ontology graph extracted from different Knowledge Graphs to annotate structured or semi-structured data. The Semantic Web Challenge on Tabular Data to Knowledge Graph Matching provided us with the means to test the system within the more restricted scenario of annotating data with a single ontology. The code hosted in this repository was used to compute the results submitted to the Round 2 of the Challenge.

Note: ADOG is still in an early phase of development.

• pandas
• tqdm
• inpout
• python-arango
• elasticsearch
• ftfy
• python-levenshtein
• rdflib
• owlready2

• Elasticsearch
• ArangoDB

1. Load DBPedia into Elasticsearch (index_dbpedia.py)
2. Load DBPedia ontology into ArangoDB (load.py), including a file with mappings in the ontology
3. Run challenge.py with the option -c pointing to the config file

• D. Oliveira and M. D’Aquin, “ADOG - Annotating Data with Ontologies and Graphs,” in Proceedings of the Semantic Web Challenge on Tabular Data to Knowledge Graph Matching co-located with the 18th International Semantic Web Conference, 2019, vol. 2553, p. 6. [Online]. Available: http://ceur-ws.org/Vol-2553/paper1.pdf
• D. Oliveira, R. Sahay, and M. d’Aquin, “Leveraging Ontologies for Knowledge Graph Schemas,” in Proceedings of the 1st Workshop on Knowledge Graph Building co-located with ESWC 2019, Portoroz, Slovenia, 2019, vol. 2489, pp. 24–36. [Online]. Available: http://ceur-ws.org/Vol-2489/paper3.pdf