GraphRAG explains why AI is shifting from isolated text to connected knowledge, and what that means for AI search ...

A secondary purpose of this repository is to provide a generalized graph API that enables implementation of a very wide range of in-memory graph algorithms including basic methods for reading, writing ...

Abstract: In industrial environments, efficient indoor transportation is a cornerstone of streamlined operations. However, the availability of high-end robotic transportation systems often poses a ...

The flattening process relies on sampling a sequence of random trail segments with neighborhood information (i.e. a SENT), by traversing the graph through a strategy similar to depth-first search.

Retrieval-augmented generation (RAG) allows AI systems to provide additional information and context to a large language model (LLM) when generating a response to a user query. However, traditional ...

The concept of knowledge graphs arose from scientific advances in a variety of research fields, including the semantic web, databases, natural language processing, and machine learning. According to ...

Abstract: Graph edit distance (GED) is a measure for quantifying the similarity between two graphs. Because of its flexibility and versatility, GED is widely used in many real applications. However, ...

Research on specific domain question-answering technology has become important with the increasing demand for intelligent question-answering systems. This paper proposes a domain question-answering ...