Recently, there has been an increasing interest in extracting and annotating tables on the Web. This activity allows the transformation of textual data into machine-readable formats to enable the execution of various artificial intelligence tasks, e.g., semantic search and dataset extension. Semantic Table Interpretation (STI) is the process of annotating elements in a table. The paper explores Semantic Table Interpretation, addressing the challenges of Entity Retrieval and Entity Disambiguation in the context of Knowledge Graphs (KGs). It introduces LamAPI, an Information Retrieval system with string/type-based filtering and s-elBat, an Entity Disambiguation technique that combines heuristic and ML-based approaches. By applying the acquired know-how in the field and extracting algorithms, techniques and components from our previous STI approaches and the state of the art, we have created a new platform capable of annotating any tabular data, ensuring a high level of quality.

Avogadro, R., D'Adda, F., Cremaschi, M. (2024). Feature/vector entity retrieval and disambiguation techniques to create a supervised and unsupervised semantic table interpretation approach. KNOWLEDGE-BASED SYSTEMS, 304(25 November 2024) [10.1016/j.knosys.2024.112447].

Feature/vector entity retrieval and disambiguation techniques to create a supervised and unsupervised semantic table interpretation approach

Avogadro R.;D'Adda F.;Cremaschi M.
2024

Abstract

Recently, there has been an increasing interest in extracting and annotating tables on the Web. This activity allows the transformation of textual data into machine-readable formats to enable the execution of various artificial intelligence tasks, e.g., semantic search and dataset extension. Semantic Table Interpretation (STI) is the process of annotating elements in a table. The paper explores Semantic Table Interpretation, addressing the challenges of Entity Retrieval and Entity Disambiguation in the context of Knowledge Graphs (KGs). It introduces LamAPI, an Information Retrieval system with string/type-based filtering and s-elBat, an Entity Disambiguation technique that combines heuristic and ML-based approaches. By applying the acquired know-how in the field and extracting algorithms, techniques and components from our previous STI approaches and the state of the art, we have created a new platform capable of annotating any tabular data, ensuring a high level of quality.
Articolo in rivista - Articolo scientifico
Data enrichment; Knowledge base; Knowledge base construction; Knowledge base extension; Knowledge graph; Semantic table interpretation; Semantic web; Table annotation; Tabular data;
English
3-set-2024
2024
304
25 November 2024
112447
open
Avogadro, R., D'Adda, F., Cremaschi, M. (2024). Feature/vector entity retrieval and disambiguation techniques to create a supervised and unsupervised semantic table interpretation approach. KNOWLEDGE-BASED SYSTEMS, 304(25 November 2024) [10.1016/j.knosys.2024.112447].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/522001
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