Knowledge graphs have been shown to play an important role in recent knowledge mining and discovery, for example in the field of life sciences or bioinformatics. Although a lot of research has been done on the field of query optimization, query transformation and of course in storing and retrieving large scale knowledge graphs the field of algorithmic optimization is still a major challenge and a vital factor in using graph databases. Few researchers have addressed the problem of optimizing algorithms on large scale labeled property graphs. Here, we present two optimization approaches and compare them with a naive approach of directly querying the graph database. The aim of our work is to determine limiting factors of graph databases like Neo4j and we describe a novel solution to tackle these challenges. For this, we suggest a classification schema to differ between the complexity of a problem on a graph database. We evaluate our optimization approaches on a test system containing a knowledge graph derived biomedical publication data enriched with text mining data. This dense graph has more than 71M nodes and 850M relationships. The results are very encouraging and - depending on the problem - we were able to show a speedup of a factor between 44 and 3839.
MemeLogos: Knowledge-Driven Retrieval-Augmented Reasoning for Harmful Meme Detection
Beijing University of Posts and Telecommunications（北京邮电大学）；School of Artificial Intelligence, Beijing Normal University（北京师范大学人工智能学院）；College of Electrical Engineering, Zhejiang University（浙江大学电气工程学院）；Zhongguancun Academy（中关村学院）；China Telecommunications Corporation（中国电信集团有限公司）
HyperGraphRAG: Retrieval-Augmented Generation via Hypergraph-Structured Knowledge Representation
Beijing University of Posts and Telecommunications（北京邮电大学）；Nanyang Technological University（南洋理工大学）；Beijing Institute of Computer Technology and Application（北京计算机技术与应用研究所）；National University of Singapore（新加坡国立大学）；China Mobile Research Institute（中国移动研究院）；Beijing Anzhen Hospital, Capital Medical University（首都医科大学北京安贞医院）
Evaluating Knowledge Graph Based Retrieval Augmented Generation Methods under Knowledge Incompleteness
University of Oslo（奥斯陆大学）；Bosch Center for AI（博世人工智能中心）；University of Stuttgart（斯图加特大学）；University of Oxford（牛津大学）；The University of Manchester（曼彻斯特大学）；University of Southampton（南安普顿大学）
DO-RAG: A Domain-Specific QA Framework Using Knowledge Graph-Enhanced Retrieval-Augmented Generation
Department of Computer Science and Technology, Tsinghua University（清华大学计算机科学与技术系）；Beijing National Research Center for Information Science and Technology - Tsinghua University（北京信息科学与技术国家研究中心-清华大学）
