Tootfinder

In his keynote, Raphael Troncy is asking whether we should keep building knowledge graphs....taking into account 20 years of experience in building knowledge graphs
https://2025.eswc-conferences.org/keynotes-announcement-raphael-troncy/

This https://arxiv.org/abs/2505.24226 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csAI_…

E^2GraphRAG: Streamlining Graph-based RAG for High Efficiency and Effectiveness
Graph-based RAG methods like GraphRAG have shown promising global understanding of the knowledge base by constructing hierarchical entity graphs. However, they often suffer from inefficiency and rely on manually pre-defined query modes, limiting practical use. In this paper, we propose E^2GraphRAG, a streamlined graph-based RAG framework that improves both Efficiency and Effectiveness. During the indexing stage, E^2GraphRAG constructs a summary tree with large language models and an entity grap…

iQUEST: An Iterative Question-Guided Framework for Knowledge Base Question Answering
Shuai Wang, Yinan Yu
https://arxiv.org/abs/2506.01784 https://

iQUEST: An Iterative Question-Guided Framework for Knowledge Base Question Answering
While Large Language Models (LLMs) excel at many natural language processing tasks, they often suffer from factual inaccuracies in knowledge-intensive scenarios. Integrating external knowledge resources, particularly knowledge graphs (KGs), provides a transparent and updatable foundation for more reliable reasoning. Knowledge Base Question Answering (KBQA), which queries and reasons over KGs, is central to this effort, especially for complex, multi-hop queries. However, multi-hop reasoning pose…

This https://arxiv.org/abs/2209.01847 has been replaced.
link: https://scholar.google.com/scholar?q=a

Conflict-Aware Pseudo Labeling via Optimal Transport for Entity Alignment
Entity alignment aims to discover unique equivalent entity pairs with the same meaning across different knowledge graphs (KGs). Existing models have focused on projecting KGs into a latent embedding space so that inherent semantics between entities can be captured for entity alignment. However, the adverse impacts of alignment conflicts have been largely overlooked during training, thereby limiting the entity alignment performance. To address this issue, we propose a novel Conflict-aware Pseudo…

Concentration of the maximum size of an induced subtree in moderately sparse random graphs
J. C. Buitrago Oropeza
https://arxiv.org/abs/2506.02801 https://…

Concentration of the maximum size of an induced subtree in moderately sparse random graphs
Kamaldinov, Skorkin, and Zhukovskii proved that the maximum size of an induced subtree in the binomial random graph $G(n,p)$ is concentrated at two consecutive points, whenever $p\in(0,1)$ is a constant. Using improved bounds on the second moment of the number of induced subtrees, we show that the same result holds when $n^{-\frac{e-2}{3e-2}+\varepsilon}\leq p=o(1)$.

Understanding Underrepresented Groups in Open Source Software
Reydne Santos, Rafa Prado, Ana Paula de Holanda Silva, Kiev Gama, Fernando Castor, Ronnie de Souza Santos
https://arxiv.org/abs/2506.00142

Understanding Underrepresented Groups in Open Source Software
Context: Diversity can impact team communication, productivity, cohesiveness, and creativity. Analyzing the existing knowledge about diversity in open source software (OSS) projects can provide directions for future research and raise awareness about barriers and biases against underrepresented groups in OSS. Objective: This study aims to analyze the knowledge about minority groups in OSS projects. We investigated which groups were studied in the OSS literature, the study methods used, their im…

This https://arxiv.org/abs/2411.06256 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

Annotative Indexing
This paper introduces annotative indexing, a novel framework that unifies and generalizes traditional inverted indexes, column stores, object stores, and graph databases. As a result, annotative indexing can provide the underlying indexing framework for databases that support retrieval augmented generation, knowledge graphs, entity retrieval, semi-structured data, and ranked retrieval. While we primarily focus on human language data in the form of text, annotative indexing is sufficiently gener…

LLMs are starving for knowledge graphs. Raphael Troncy was pointing out that many LLM company crawlers are constantly visiting their KGs. Some crawlers even perform explicit SPARQL queries on the KGs.
#knowledgegraphs #eswc2025

DrKGC: Dynamic Subgraph Retrieval-Augmented LLMs for Knowledge Graph Completion across General and Biomedical Domains
Yongkang Xiao, Sinian Zhang, Yi Dai, Huixue Zhou, Jue Hou, Jie Ding, Rui Zhang
https://arxiv.org/abs/2506.00708

DrKGC: Dynamic Subgraph Retrieval-Augmented LLMs for Knowledge Graph Completion across General and Biomedical Domains
Knowledge graph completion (KGC) aims to predict missing triples in knowledge graphs (KGs) by leveraging existing triples and textual information. Recently, generative large language models (LLMs) have been increasingly employed for graph tasks. However, current approaches typically encode graph context in textual form, which fails to fully exploit the potential of LLMs for perceiving and reasoning about graph structures. To address this limitation, we propose DrKGC (Dynamic Subgraph Retrieval-…

This https://arxiv.org/abs/2506.00261 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

GPR: Empowering Generation with Graph-Pretrained Retriever
Graph retrieval-augmented generation (GRAG) places high demands on graph-specific retrievers. However, existing retrievers often rely on language models pretrained on plain text, limiting their effectiveness due to domain misalignment and structure ignorance. To address these challenges, we propose GPR, a graph-based retriever pretrained directly on knowledge graphs. GPR aligns natural language questions with relevant subgraphs through LLM-guided graph augmentation and employs a structure-aware…