SemTab 2025 - Semantic Web Challenge on Tabular Data to Knowledge Graph Matching

About the Challenge

Tabular data in the form of CSV files is the common input format in a data analytics pipeline. However, a lack of understanding of the semantic structure and meaning of the content may hinder the data analytics process. Thus gaining this semantic understanding will be very valuable for data integration, data cleaning, data mining, machine learning and knowledge discovery tasks.

Tables on the Web may also be the source of highly valuable data. The addition of semantic information to Web tables may enhance a wide range of applications, such as web search, question answering, and Knowledge Base (KB) construction.

Tabular data to Knowledge Graph (KG) matching is the process of assigning semantic tags from KGs (e.g., Wikidata or DBpedia) to the elements of the table. This task however is often difficult in practice due to metadata (e.g., table and column names) being missing, incomplete or ambiguous.

The SemTab challenge aims at benchmarking systems dealing with the tabular data to KG matching problem, so as to facilitate their comparison on the same basis and the reproducibility of the results.

The 2025 edition of this challenge will be collocated with the International Semantic Web Conference .

Challenge Tracks

MammoTab

Participants will address the Semantic Table Interpretation challenges using a carefully selected subset of 870 tables from the new version of the MammoTab dataset , comprising a total of 84,907 cell annotations. MammoTab is a large-scale benchmark designed to provide realistic and complex scenarios, including tables affected by typical challenges of web and Wikipedia data.

Only approaches based on Large Language Models are allowed, either:

in fine-tuning settings, or
using Retrieval-Augmented Generation strategies.

The evaluation will focus on the Cell Entity Annotation (CEA) task, but will also take into account the ability of the proposed approaches to effectively deal with the following key challenges:

Disambiguation Correctly linking ambiguous mentions to the intended entities.

Homonymy Managing mentions referring to entities with identical or very similar names.

Alias resolution Recognising entities referred by alternative names, acronyms, or nicknames.

NIL Detection Correctly identifying mentions that do not correspond to any entity in the Knowledge Graph.

Noise Robustness Dealing with incomplete, noisy, or imprecise table contexts.

Collective Inference Leveraging inter-cell and inter-column signals to improve the consistency of annotations.

Participants are expected to demonstrate not only strong CEA performance, but also robustness and versatility across all these dimensions, which are critical for real-world table interpretation scenarios.

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secu-table

The secu-table dataset involved security data extracted from Common Vulnerability and Exposure (CVE) and Common Weakness Enumeration (CWE) data sources. The dataset is composed of 20% of tables without any errors and 80% of tables containing errors such as ambiguity, NIL, missing context, misspelt data. Secu-table dataset is composed of 1,554 tables, divided into 76 tables provided as ground truth and 1,478 tables for testing.

The participant are invited to use open source LLMs to address the STI tasks which are: cell entity annotation (CEA), column type annotation (CTA), and column property annotation (CPA) using the SEPSES Computer Security Knowledge Graph and the CEA task using the Wikidata KG .

The evaluation of the participants' results will consider the recall, precision, and F-score. In addition to these scores, the participants are invited to provide the evaluation of the LLMs capabilities to make a prediction or to abstain (or to say "I don't know").