TY - JOUR

T1 - Tab2KG

T2 - Semantic table interpretation with lightweight semantic profiles

AU - Gottschalk, Simon

AU - Demidova, Elena

N1 - Funding Information: This work is partially funded by the DFG, German Research Foundation (“WorldKG”, 424985896), the Federal Ministry of Education and Research (BMBF), Germany (“Simple-ML”, 01IS18054) and the European Commission (EU H2020, “smashHit”, 871477).

PY - 2022/4/6

Y1 - 2022/4/6

N2 - Tabular data plays an essential role in many data analytics and machine learning tasks. Typically, tabular data does not possess any machine-readable semantics. In this context, semantic table interpretation is crucial for making data analytics workflows more robust and explainable. This article proposes Tab2KG-a novel method that targets at the interpretation of tables with previously unseen data and automatically infers their semantics to transform them into semantic data graphs. We introduce original lightweight semantic profiles that enrich a domain ontology's concepts and relations and represent domain and table characteristics. We propose a one-shot learning approach that relies on these profiles to map a tabular dataset containing previously unseen instances to a domain ontology. In contrast to the existing semantic table interpretation approaches, Tab2KG relies on the semantic profiles only and does not require any instance lookup. This property makes Tab2KG particularly suitable in the data analytics context, in which data tables typically contain new instances. Our experimental evaluation on several real-world datasets from different application domains demonstrates that Tab2KG outperforms state-of-The-Art semantic table interpretation baselines.

AB - Tabular data plays an essential role in many data analytics and machine learning tasks. Typically, tabular data does not possess any machine-readable semantics. In this context, semantic table interpretation is crucial for making data analytics workflows more robust and explainable. This article proposes Tab2KG-a novel method that targets at the interpretation of tables with previously unseen data and automatically infers their semantics to transform them into semantic data graphs. We introduce original lightweight semantic profiles that enrich a domain ontology's concepts and relations and represent domain and table characteristics. We propose a one-shot learning approach that relies on these profiles to map a tabular dataset containing previously unseen instances to a domain ontology. In contrast to the existing semantic table interpretation approaches, Tab2KG relies on the semantic profiles only and does not require any instance lookup. This property makes Tab2KG particularly suitable in the data analytics context, in which data tables typically contain new instances. Our experimental evaluation on several real-world datasets from different application domains demonstrates that Tab2KG outperforms state-of-The-Art semantic table interpretation baselines.

KW - domain knowledge graphs

KW - one-shot learning

KW - semantic profiles

KW - Semantic table interpretation

UR - http://www.scopus.com/inward/record.url?scp=85129166755&partnerID=8YFLogxK

U2 - 10.48550/arXiv.2302.01150

DO - 10.48550/arXiv.2302.01150

M3 - Article

AN - SCOPUS:85129166755

VL - 13

SP - 571

EP - 597

JO - Semantic web

JF - Semantic web

SN - 1570-0844

IS - 3

ER -