A significant number of documents, reports and Web pages – an analysis detected 233M relational tables within the Common Crawl repository – makes use of tables to convey information that cannot be easily processed by humans, and understood by computers. To address this issue, we propose a new approach that allows computers to interpret the semantics of a table, and provide humans with a more accessible representation of the data contained in a table. To achieve the objective, the general problem has been broken down into three sub-problems: (i) define a method to provide a semantic interpretation of table data; (ii) define a descriptive model that allows computers to understand and share table data; and (iii) define processes, techniques and algorithms to generate natural language representation of the table data.

Info about developed tools can be found at zoo.disco.unimib.it

1. Lucky M.N., Cremaschi M., Lodigiani B., Menolascina A., De Paoli F. (2016) Enriching API Descriptions by Adding API Profiles Through Semantic Annotation. In: Sheng Q., Stroulia E., Tata S., Bhiri S. (eds) Service-Oriented Computing. ICSOC 2016. Lecture Notes in Computer Science, vol 9936. Springer, Cham
2. Cremaschi M., De Paoli F. (2017) Toward Automatic Semantic API Descriptions to Support Services Composition. In: De Paoli F., Schulte S., Broch Johnsen E. (eds) Service-Oriented and Cloud Computing. ESOCC 2017. Lecture Notes in Computer Science, vol 10465. Springer, Cham
3. Bianchi F., Palmonari M., Cremaschi M., Fersini E. (2017) Actively Learning to Rank Semantic Associations for Personalized Contextual Exploration of Knowledge Graphs. In: Blomqvist E., Maynard D., Gangemi A., Hoekstra R., Hitzler P., Hartig O. (eds) The Semantic Web. ESWC 2017. Lecture Notes in Computer Science, vol 10249. Springer, Cham
4. Cremaschi M., De Paoli F. (2018) A Practical Approach to Services Composition Through Light Semantic Descriptions. In: Kritikos K., Plebani P., de Paoli F. (eds) Service-Oriented and Cloud Computing. ESOCC 2018. Lecture Notes in Computer Science, vol 11116. Springer, Cham
5. Cremaschi M., Rula A., Siano A., De Paoli F. (2019) MantisTable: A Tool for Creating Semantic Annotations on Tabular Data. In: Hitzler P. et al. (eds) The Semantic Web: ESWC 2019 Satellite Events. ESWC 2019. Lecture Notes in Computer Science, vol 11762. Springer, Cham
6. Cremaschi M., Rula A., Siano A., De Paoli F. (2019). Semantic Table Interpretation using MantisTable. In: Shvaiko P. et al. (eds) Proceedings of the 14th International Workshop on Ontology Matching co-located with the 18th International Semantic Web Conference (ISWC 2019). OM 2019. Vol 2553. CEUR-WS.org
7. Cremaschi M., Bianchi F., Maurino A., Pierotti A. P. (2019). Supporting journalism by combining Neural Language Generation and Knowledge Graphs. In: Bernardi R. et al. (2019) Proceedings of the Sixth Italian Conference on Computational Linguistics. CLiC-it 2019. Vol 2481. CEUR-WS.org
8. Cremaschi M., Avogadro R., Chieregato D. (2019). MantisTable: an Automatic Approach for the Semantic Table Interpretation. In: Jiménez-Ruiz E. et al. (2019) Proceedings of the Semantic Web Challenge on Tabular Data to Knowledge Graph Matching co-located with the 18th International Semantic Web Conference (ISWC 2019). SemTab 2019. Vol 2553. CEUR-WS.org
9. Cremaschi M., Siano A., Avogadro R., Jimenez-Ruiz E., Maurino A. (2020) STILTool: a Semantic Table Interpretation evaLuation Tool. In: Harth A. et al. (eds) The Semantic Web: ESWC 2020 Satellite Events. ESWC 2020.
10. Cremaschi M., De Paoli F., Rula A., Spahiu B. (2020). A fully automated approach to a complete Semantic Table Interpretation. Future Generation Computer Systems, Volume 112, 2020.