[Event at CIG] [CFP] 1st International Workshop on Knowledge Graphs for RAG and Textual Document Analysis

[rafika boutalbi]

*Doc2KG: 1st International Workshop on Knowledge Graphs for RAG and Textual
Document Analysis*
*https://sites.google.com/view/doc2kg26-first-international-w/call-for-papers*
<https://sites.google.com/view/doc2kg26-first-international-w/call-for-papers>

*In conjonction with COMPSAC2026 <https://ieeecompsac.computer.org/2026/>-
July 8-10, 2025 •Madrid, Spain*

*https://ieeecompsac.computer.org/2026/doc2kg/
<https://ieeecompsac.computer.org/2026/doc2kg/>Context:*

In today's digital landscape, we are witnessing an unprecedented explosion
in textual data generation. From social media posts and news articles to
legal documents, academic papers, and business communications, the volume
of text is growing exponentially. Traditional methods of analyzing these
vast document collections frequently fall short in terms of scalability,
accuracy, and efficiency, creating an urgent need for more sophisticated
approaches.

The emergence of Retrieval-Augmented Generation (RAG) marked a significant
advancement by grounding Large Language Models LLM in relevant contextual
information. However, conventional RAG systems that rely primarily on
vector similarity search reveal critical limitations when handling complex,
real-world documents. They struggle with multi-hop reasoning connecting
disparate facts across multiple documents and fail to capture the rich
semantic relationships between entities such as people, organizations, and
projects. This results in incomplete answers, factual inconsistencies, and
an inability to perform genuine analytical tasks, leaving substantial
untapped potential within corporate knowledge bases. This workshop
introduces a paradigm shift: the integration of Knowledge Graphs (KGs) into
the RAG pipeline. We will explore how representing document content as a
structured, interconnected graph of entities and relationships can
dramatically enhance the accuracy, depth, and reasoning capabilities of
Large Language Models. The representation of data as graph structures has
been empirically proven to significantly improve RAG performance, enabling
more sophisticated document analysis. Doc2KG Workshop aims to bring
together experts from industry, research, and academia to exchange ideas
and discuss ongoing innovations in natural language processing and
Generative AI for textual document analysis. Participants will gain
comprehensive understanding of a cutting-edge architecture where documents
are not merely embedded but transformed into dynamic graphs of
interconnected entities. Wewill learn how this structured knowledge base
enables precise, relationship-driven retrieval, allowing LLMs to traverse
connections and deliver answers with enhanced accuracy, deeper context, and
robust reasoning capabilities previously beyond reach.

The Doc2KG workshop aims to bring together an area for experts from
industry, science, and academia to exchange ideas and discuss ongoing research
in natural language processing and GenAI for textual document analysis.

The Doc2KG workshop encourages the participation of persons with
disabilities, and underrepresented minorities in the STEM and competitive
STEM workforce. Also, it encourages original application with a significant
impact on the well-being of individuals in society. Finally, it greatly
impacts increasing partnerships between academia and industry.


Topics of interests:

   -

   ● Text to KG: Enhancing KG construction and completion with GenAI
   ● From KG to Text
   ● From Speech to text to KG
   ● Knowledge Graph Construction & Storage
   ● Document Ingestion & Pre-processing for KG construction
   ● Innovative pipeline for Knowledge Extraction
   ● Hybrid Retrieval & Querying of KGs
   ● Reducing Factual Hallucinations using RAG and KGs
   ● Prompting Engineering using KGs
   ● KG augmentation from document
   ● Triples representation for KG
   ● Specific domain KG querying
   ● Benchmark datasets relevant for tasks combining KGs and GenAI
   ● Real-world applications on scholarly data, biomedical domain, etc.
   ● Industry application and real-world scenarios application
   ● KG for legal text
   -

   And more


Submission:

Papers submitted for review should conform to IEEE specifications.
Manuscript templates can be downloaded from IEEE website
<https://www.ieee.org/conferences/publishing/templates.html>. The maximum
length of papers is 8 pages. All the papers will go through the
double-blind peer review process. Authors’ names and affiliations should
not appear in the submitted paper. Authors’ prior work should be cited in
the third person. Authors should also avoid revealing their identities
and/or institutions in the text, figures, links, etc.

Authors should also ensure that their identity is not revealed indirectly
by citing their previous work in the third person and omitting
acknowledgments until the camera-ready version. Papers have to be submitted
via the conference's EasyChair  submission page and select the track
related to Doc2KG workshop.

Please include in the paper title "Full paper: Title" or "Short paper:
Title" to precise the contribution type. At least one author of each
accepted paper must register for the workshop, in order to present the
paper.

*Important dates: *

Workshop & special session papers due: 15 April 2026
Workshop & special session papers notification: 7 May 2026
Camera Ready Paper submission: 21 May 2026

*Publication*:

Accepted papers will be submitted to IEEEXplore for possible publication.

Workshop Chairs

*Karima Boutalbi* <karima.boutalbi at cgedim.com>, Cegedim Business Services,
France

Rafika Boutalbi
<https://streaklinks.com/CBGLsQggIIcXJ_PtQgsr0IDJ/http%3A%2F%2Frafika.boutalbi%40univ-amu.fr%2F>,
Aix-Marseille University, France

Rim Hantach <http://rim.hantach%40gmail.com%20%3Crim.hantach@gmail.com%3E;/>,
Engie, France