  Even for a conservative estimate, 80% of enterprise data reside in
unstructured files, stored in data lakes that accommodate heterogeneous
formats. Classical search engines can no longer meet information seeking needs,
especially when the task is to browse and explore for insight formulation. In
other words, there are no obvious search keywords to use. Knowledge graphs, due
to their natural visual appeals that reduce the human cognitive load, become
the winning candidate for heterogeneous data integration and knowledge
representation.
  In this paper, we introduce Docs2KG, a novel framework designed to extract
multimodal information from diverse and heterogeneous unstructured documents,
including emails, web pages, PDF files, and Excel files. Dynamically generates
a unified knowledge graph that represents the extracted key information,
Docs2KG enables efficient querying and exploration of document data lakes.
Unlike existing approaches that focus on domain-specific data sources or
pre-designed schemas, Docs2KG offers a flexible and extensible solution that
can adapt to various document structures and content types. The proposed
framework unifies data processing supporting a multitude of downstream tasks
with improved domain interpretability. Docs2KG is publicly accessible at
https://docs2kg.ai4wa.com, and a demonstration video is available at
https://docs2kg.ai4wa.com/Video.
