T2KG: Transforming Multimodal Document to Knowledge Graph
Santiago Galiano, Rafael Muñoz, Yoan Gutiérrez, Andrés Montoyo, Jose Ignacio Abreu, Luis Alfonso Ureña
Abstract
The large amount of information in digital format that exists today makes it unfeasible to use manual means to acquire the knowledge contained in these documents. Therefore, it is necessary to develop tools that allow us to incorporate this knowledge into a structure that is easy to use by both machines and humans. This paper presents a system that can incorporate the relevant information from a document in any format, structured or unstructured, into a semantic network that represents the existing knowledge in the document. The system independently processes from structured documents based on its annotation scheme to unstructured documents, written in natural language, for which it uses a set of sensors that identifies the relevant information and subsequently incorporates it to enrich the semantic network that is created by linking all the information based on the knowledge discovered.- Anthology ID:
- 2023.ranlp-1.43
- Volume:
- Proceedings of the 14th International Conference on Recent Advances in Natural Language Processing
- Month:
- September
- Year:
- 2023
- Address:
- Varna, Bulgaria
- Editors:
- Ruslan Mitkov, Galia Angelova
- Venue:
- RANLP
- SIG:
- Publisher:
- INCOMA Ltd., Shoumen, Bulgaria
- Note:
- Pages:
- 385–391
- Language:
- URL:
- https://aclanthology.org/2023.ranlp-1.43
- DOI:
- Cite (ACL):
- Santiago Galiano, Rafael Muñoz, Yoan Gutiérrez, Andrés Montoyo, Jose Ignacio Abreu, and Luis Alfonso Ureña. 2023. T2KG: Transforming Multimodal Document to Knowledge Graph. In Proceedings of the 14th International Conference on Recent Advances in Natural Language Processing, pages 385–391, Varna, Bulgaria. INCOMA Ltd., Shoumen, Bulgaria.
- Cite (Informal):
- T2KG: Transforming Multimodal Document to Knowledge Graph (Galiano et al., RANLP 2023)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/2023.ranlp-1.43.pdf