Docs2KG: Unified Knowledge Graph Construction from Heterogeneous Documents Assisted by Large Language Models
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.
Code (1)
Tasks
Data Integrationgraph constructionKnowledge GraphsMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
DocSAM: Unified Document Image Segmentation via Query Decomposition and Heterogeneous Mixed Learning
Document image segmentation is crucial in document analysis and recognition but remains challenging due to the heterogeneity of document formats and diverse segmentation tasks. Existing methods often treat these task…
Document Layout AnalysisImage SegmentationSegmentationSemantic Segmentation+2Unified Multi-Domain Graph Pre-training for Homogeneous and Heterogeneous Graphs via Domain-Specific Expert Encoding
Graph pre-training has achieved remarkable success in recent years, delivering transferable representations for downstream adaptation. However, most existing methods are designed for either homogeneous or heterogeneous g…
API2Com: On the Improvement of Automatically Generated Code Comments Using API Documentations
Code comments can help in program comprehension and are considered as important artifacts to help developers in software maintenance. However, the comments are mostly missing or are outdated, specially in complex softwar…
Comment GenerationMachine TranslationWhat Makes Graph Unified? Principles and Generative Sliding-Window Transformer for Graph Foundation Models
Graph Foundation Models (GFMs) have recently emerged as a promising paradigm for general-purpose graph learning, aiming to learn reusable knowledge that generalizes across diverse graph domains and downstream tasks, redu…
Graph LearningGKG-LLM: A Unified Framework for Generalized Knowledge Graph Construction
The construction of Generalized Knowledge Graph (GKG), including knowledge graph, event knowledge graph and commonsense knowledge graph, is fundamental for various natural language processing tasks. Current studies typic…
graph constructionKnowledge Graphs