[Apologies for cross-postings]
Call for Papers
First International Workshop on Extraction from Triplet
Text-Table-Knowledge Graph and associated Challenge
https://ecladatta.github.io/triplet2026/
in conjunction with the 23rd European Semantic Web Conference (ESWC 2026)
https://2026.eswc-conferences.org/, Dubrovnik, Croatia
Important dates:
- **Submission deadline**: 3 March, 2026 (11:59pm, AoE)
- **Notifications**: 31 March, 2026
- **Camera-ready deadline**: 15 April, 2026 (11:59pm, AoE)
- **Workshop**: Sunday 10 May OR Monday 11 May 2026
Motivation:
Understanding information spread across text and table is essential for
tasks such as question answering and fact checking. Existing benchmarks
primarily deal with semantic table interpretation or reasoning over
tables for question answering, leaving a gap in evaluating models that
integrate tabular and textual information, perform joint information
extraction across modalities, or can automatically detect
inconsistencies between modalities.
This workshop aims to provide a forum for exchanging ideas between the
NLP community working on open information extraction and the vibrant
Semantic Web community working on the core challenge of matching tabular
data to Knowledge Graphs, on populating knowledge graphs using texts and
on reasoning across text, tabular data and knowledge graphs. The
workshop also targets researchers focusing on the intersection of
learning over structured data and information retrieval, for example, in
retrieval augmented generation (RAG) and question answering (QA)
systems. Hence, the goal of the workshop is to connect researchers and
trigger collaboration opportunities by bringing together views from the
Semantic Web, NLP, database, and IR disciplines.
Scope:
The topics of interest include but are not limited to:
- Semantic Table Interpretation
- Automated Tabular Data Understanding
- Using Large Language Models (LLMs) for Information Extraction
- Generative Models and LLMs for Structured Data
- Knowledge Graph Construction and Completion with Tabular Data and Texts
- Analysis of Tabular Data on the Web (Web Tables)
- Benchmarking and Evaluation Frameworks for Joint Text-Table Data Analysis
- Applications (e.g. data search, fact-checking, Question-Answering, KG
alignment)
Submission Guidelines:
We invite two types of submissions:
1. Full research papers (12-15 pages) including references and appendices
2. Challenge papers (6-8 pages) including references and appendices
All submissions should be formatted in the CEUR layout format,
https://www.overleaf.com/latex/templates/template-for-submissions-to-ceur-workshop-proceedings-ceur-ws-dot-org/wqyfdgftmcfw
This workshop is double-blind and non-archival. Submissions are managed
through EasyChair at
https://easychair.org/conferences/?conf=triplet2026. All accepted papers
will be presented as posters or as oral talks.
**TRIPLET Challenge:**
In recent years, the research community has shown increasing interest in
the joint understanding of text and tabular data, often, for performing
tasks such as question answering or fact checking where evidences can be
found in texts and tables. Hence, various benchmarks have been developed
for jointly querying tabular data and textual documents in domains such
as finance, scientific publications, and open domain. While benchmarks
for triple extraction from text for Knowledge Graph construction and
semantic annotation of tabular data exist in the community, there
remains a gap in benchmarks and tasks that specifically address the
joint extraction of triples from text and tables by leveraging
complementary clues across these different modalities.
The TRIPLET 2026 challenge is proposing three sub-tasks and benchmarks
for understanding the complementarity between tables, texts, and
knowledge graphs, and in particular to propose a joint knowledge
extraction and reconciliation process.
#Sub-Task 1: Assessing the Relatedness Between Tables and Textual Passages
The goal of this task is to assess the relatedness between tables and
textual passages (within documents and across documents). For this
purpose, we have constructed LATTE (Linking Across Table and Text for
Relatedness Evaluation), a human annotated dataset comprising table–text
pairs with relatedness labels. LATTE consists of 7,674 unique tables and
41,880 unique textual paragraphs originating from 3,826 distinct
Wikipedia pages. Each text paragraph is drawn from the same or
contextually linked pages as the corresponding table, rather than being
artificially generated. LATTE provides a challenging benchmark for
cross-modal reasoning by requiring classification of related and
unrelated table–text pairs. Unlike prior resources centered on
table-to-text generation or text retrieval, LATTE emphasizes
fine-grained semantic relatedness between structured and unstructured data.
The Figure below provides an example, using a web-annotation tool we
developed, of how we identify the relatedness between the sentence
containing the entity AirPort Extreme 802.11n (highlighted in Orange)
and the data table providing information about output power and
frequency for this entity. Participants are provided with tables and
textual passages that would need to be ranked. The evaluation will use
metrics such as P@k, R@k and F1@k.
Go to https://www.codabench.org/competitions/12776/ and enroll to
participate in this Task.
#Sub-Task 2: Joint Relation Extraction Between Texts and Tables
The goal of this task is to automatically extract knowledge jointly from
tables and related texts. For this purpose, we created ReTaT, a dataset
that can be used to train and evaluate systems for extracting such
relations. This dataset is composed of (table, surrounding text) pairs
extracted from Wikipedia pages and has been manually annotated with
relation triples. ReTaT is organized in three subsets with distinct
characteristics: domain (business, telecommunication and female
celebrities), size (from 50 to 255 pairs), language (English vs French),
type of relations (data vs object properties), close vs open list of
relation, size of the surrounding text (paragraph vs full page). We then
assessed its quality and suitability for the joint table-text relation
extraction task using Large Language Models (LLMs).
Given a Wikipedia page containing texts and tables and a list of
predicates defined in Wikidata, a participant system should extract
triples composed of mentions located partly in the text and partly in
the table and disambiguated with entities and predicates identified in
the Wikidata reference knowledge graph. For example, in the Figure
below, an annotation triple <Q13567390, P2109, 24.57> is associated with
mentions highlighted in orange (subject), blue (predicate) and green
(object) to annotate the document available at
https://en.wikipedia.org/wiki/AirPort_Extreme. Similar to the
Text2KGBench evaluation
(https://link.springer.com/chapter/10.1007/978-3-031-47243-5_14), and
because the set of triples are not exhaustive for a given sentence, to
avoid false negatives, we follow a locally closed approach by only
considering the relations that are part of the ground truth. The
evaluation then uses metrics such as P, R and F1.
Go to https://www.codabench.org/competitions/12936/ and enroll to
participate in this Task.
# Sub-Task 3: Detecting Inconsistencies Between Texts, Tables and
Knowledge Graphs
The goal of this task is to check the consistency of knowledge extracted
from tables and texts with existing triples in the Wikidata knowledge
graph. Different kind of inconsistencies will be considered in this
task. Participants to this task will be able to report on their findings
in their system paper.
See the Figure at
https://ecladatta.github.io/images/triplet_annotation_tool.png
# Data & Evaluation:
For the first 2 sub-tasks, we have released a training dataset with
ground-truth annotations, enabling participant teams to develop machine
learning-based systems, and in particular for training purposes and for
hyperparameter optimizations and internal validations.
A separate blind test dataset will remain private and be used for
ranking the submissions.
Participants should register on Codabench and then enroll for each
sub-task separately (Task 1:
https://www.codabench.org/competitions/12776/ and Task 2:
https://www.codabench.org/competitions/12936/). Each team are allowed a
limited number of daily submissions, and the highest achieved accuracy
will be reported as the team's final result. We encourage participants
to develop open-source solutions, to utilise and fine-tune pre-trained
language models and to experiment with LLMs of various size in zero-shot
or few-shot settings.
# Challenge Important Dates:
- Release of training set: 13 February 2026
- Deadline for registering to the challenge: 15 March 2026
- Release of test set: 24 March 2026
- Submission of results: 10 April 2026
- System Results & Notification of Acceptance: 17 April 2026
- Submission of System Papers: 28 April 2026
- Presentations @ TRIPLET Workshop: May 2026
Workshop Organizers
- Raphael Troncy (EURECOM, France)
- Yoan Chabot (Orange, France)
- Véronique Moriceau (IRIT, France)
- Nathalie Aussenac-Gilles (IRIT, France)
- Mouna Kamel(IRIT, France)
Contact:
For discussions, please use our Google Group,
https://groups.google.com/g/triplet-challenge
The workshop is supported by the ECLADATTA project funded by the French
National Funding Agency ANR under the grant ANR-22-CE23-0020.
--
Raphaël Troncy
EURECOM, Campus SophiaTech
Data Science Department
450 route des Chappes, 06410 Biot, France.
e-mail: [email protected] & [email protected]
Tel: +33 (0)4 - 9300 8242
Fax: +33 (0)4 - 9000 8200
Web: http://www.eurecom.fr/~troncy/
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