iText2KG: Incremental Knowledge Graphs Construction Using Large Language Models
Most available data is unstructured, making it challenging to access valuable information. Automatically building Knowledge Graphs (KGs) is crucial for structuring data and making it accessible, allowing users to search for information effectively. KGs also facilitate insights, inference, and reasoning. Traditional NLP methods, such as named entity recognition and relation extraction, are key in information retrieval but face limitations, including the use of predefined entity types and the need for supervised learning. Current research leverages large language models' capabilities, such as zero- or few-shot learning. However, unresolved and semantically duplicated entities and relations still pose challenges, leading to inconsistent graphs and requiring extensive post-processing. Additionally, most approaches are topic-dependent. In this paper, we propose iText2KG, a method for incremental, topic-independent KG construction without post-processing. This plug-and-play, zero-shot method is applicable across a wide range of KG construction scenarios and comprises four modules: Document Distiller, Incremental Entity Extractor, Incremental Relation Extractor, and Graph Integrator and Visualization. Our method demonstrates superior performance compared to baseline methods across three scenarios: converting scientific papers to graphs, websites to graphs, and CVs to graphs.
Code (1)
Tasks
Few-Shot LearningInformation RetrievalKnowledge Graphsnamed-entity-recognitionNamed Entity RecognitionRelationRelation ExtractionSimilar Papers 제목 키워드 기반
DIAL-KG: Schema-Free Incremental Knowledge Graph Construction via Dynamic Schema Induction and Evolution-Intent Assessment
Knowledge Graphs (KGs) are foundational to applications such as search, question answering, and recommendation. Conventional knowledge graph construction methods are predominantly static, rely ing on a single-step constr…
Question AnsweringEvent ExtractionKnowledge GraphsBarack's Wife Hillary: Using Knowledge-Graphs for Fact-Aware Language Modeling
Modeling human language requires the ability to not only generate fluent text but also encode factual knowledge. However, traditional language models are only capable of remembering facts seen at training time, and often…
Knowledge GraphsLanguage ModelingLanguage ModellingBarack's Wife Hillary: Using Knowledge Graphs for Fact-Aware Language Modeling
Modeling human language requires the ability to not only generate fluent text but also encode factual knowledge. However, traditional language models are only capable of remembering facts seen at training time, and often…
Knowledge GraphsLanguage ModelingLanguage ModellingGrowing and Serving Large Open-domain Knowledge Graphs
Applications of large open-domain knowledge graphs (KGs) to real-world problems pose many unique challenges. In this paper, we present extensions to Saga our platform for continuous construction and serving of knowledge …
Entity LinkingFact VerificationKnowledge Graph EmbeddingsKnowledge GraphsIncremental Graph Construction Enables Robust Spectral Clustering of Texts
Neighborhood graphs are a critical but often fragile step in spectral clustering of text embeddings. On realistic text datasets, standard $k$-NN graphs can contain many disconnected components at practical sparsity level…