Tootfinder

Bio-KGvec2go: Serving up-to-date Dynamic Biomedical Knowledge Graph Embeddings
Hamid Ahmad, Heiko Paulheim, Rita T. Sousa
https://arxiv.org/abs/2509.07905 https://

Bio-KGvec2go: Serving up-to-date Dynamic Biomedical Knowledge Graph Embeddings
Knowledge graphs and ontologies represent entities and their relationships in a structured way, having gained significance in the development of modern AI applications. Integrating these semantic resources with machine learning models often relies on knowledge graph embedding models to transform graph data into numerical representations. Therefore, pre-trained models for popular knowledge graphs and ontologies are increasingly valuable, as they spare the need to retrain models for different tas…

Context Pooling: Query-specific Graph Pooling for Generic Inductive Link Prediction in Knowledge Graphs
Zhixiang Su, Di Wang, Chunyan Miao
https://arxiv.org/abs/2507.07595

Context Pooling: Query-specific Graph Pooling for Generic Inductive Link Prediction in Knowledge Graphs
Recent investigations on the effectiveness of Graph Neural Network (GNN)-based models for link prediction in Knowledge Graphs (KGs) show that vanilla aggregation does not significantly impact the model performance. In this paper, we introduce a novel method, named Context Pooling, to enhance GNN-based models' efficacy for link predictions in KGs. To our best of knowledge, Context Pooling is the first methodology that applies graph pooling in KGs. Additionally, Context Pooling is first-of-its-ki…

BALI: Enhancing Biomedical Language Representations through Knowledge Graph and Language Model Alignment
Andrey Sakhovskiy, Elena Tutubalina
https://arxiv.org/abs/2509.07588 htt…

BALI: Enhancing Biomedical Language Representations through Knowledge Graph and Language Model Alignment
In recent years, there has been substantial progress in using pretrained Language Models (LMs) on a range of tasks aimed at improving the understanding of biomedical texts. Nonetheless, existing biomedical LLMs show limited comprehension of complex, domain-specific concept structures and the factual information encoded in biomedical Knowledge Graphs (KGs). In this work, we propose BALI (Biomedical Knowledge Graph and Language Model Alignment), a novel joint LM and KG pre-training method that au…

Avoiding Over-Personalization with Rule-Guided Knowledge Graph Adaptation for LLM Recommendations
Fernando Spadea, Oshani Seneviratne
https://arxiv.org/abs/2509.07133 https://…

Avoiding Over-Personalization with Rule-Guided Knowledge Graph Adaptation for LLM Recommendations
We present a lightweight neuro-symbolic framework to mitigate over-personalization in LLM-based recommender systems by adapting user-side Knowledge Graphs (KGs) at inference time. Instead of retraining models or relying on opaque heuristics, our method restructures a user's Personalized Knowledge Graph (PKG) to suppress feature co-occurrence patterns that reinforce Personalized Information Environments (PIEs), i.e., algorithmically induced filter bubbles that constrain content diversity. These …

Empirical Evaluation of AI-Assisted Software Package Selection: A Knowledge Graph Approach
Siamak Farshidi, Amir Saberhabibi, Behbod Eskafi, Niloofar Nikfarjam, Sadegh Eskandari, Slinger Jansen, Michel Chaudron, Bedir Tekinerdogan
https://arxiv.org/abs/2508.05693

Empirical Evaluation of AI-Assisted Software Package Selection: A Knowledge Graph Approach
Selecting third-party software packages in open-source ecosystems like Python is challenging due to the large number of alternatives and limited transparent evidence for comparison. Generative AI tools are increasingly used in development workflows, but their suggestions often overlook dependency evaluation, emphasize popularity over suitability, and lack reproducibility. This creates risks for projects that require transparency, long-term reliability, maintainability, and informed architectura…

Automated Creation of the Legal Knowledge Graph Addressing Legislation on Violence Against Women: Resource, Methodology and Lessons Learned
Claudia dAmato, Giuseppe Rubini, Francesco Didio, Donato Francioso, Fatima Zahra Amara, Nicola Fanizzi
https://arxiv.org/abs/2508.06368

Automated Creation of the Legal Knowledge Graph Addressing Legislation on Violence Against Women: Resource, Methodology and Lessons Learned
Legal decision-making process requires the availability of comprehensive and detailed legislative background knowledge and up-to-date information on legal cases and related sentences/decisions. Legal Knowledge Graphs (KGs) would be a valuable tool to facilitate access to legal information, to be queried and exploited for the purpose, and to enable advanced reasoning and machine learning applications. Indeed, legal KGs may act as knowledge intensive component to be used by pre-dictive machine le…

Mitigating Undesired Conditions in Flexible Production with Product-Process-Resource Asset Knowledge Graphs
Petr Novak, Stefan Biffl, Marek Obitko, Petr Kadera
https://arxiv.org/abs/2508.06278

Mitigating Undesired Conditions in Flexible Production with Product-Process-Resource Asset Knowledge Graphs
Contemporary industrial cyber-physical production systems (CPPS) composed of robotic workcells face significant challenges in the analysis of undesired conditions due to the flexibility of Industry 4.0 that disrupts traditional quality assurance mechanisms. This paper presents a novel industry-oriented semantic model called Product-Process-Resource Asset Knowledge Graph (PPR-AKG), which is designed to analyze and mitigate undesired conditions in flexible CPPS. Built on top of the well-proven Pr…

The Role of Exploration Modules in Small Language Models for Knowledge Graph Question Answering
Yi-Jie Cheng, Oscar Chew, Yun-Nung Chen
https://arxiv.org/abs/2509.07399 https://…

The Role of Exploration Modules in Small Language Models for Knowledge Graph Question Answering
Integrating knowledge graphs (KGs) into the reasoning processes of large language models (LLMs) has emerged as a promising approach to mitigate hallucination. However, existing work in this area often relies on proprietary or extremely large models, limiting accessibility and scalability. In this study, we investigate the capabilities of existing integration methods for small language models (SLMs) in KG-based question answering and observe that their performance is often constrained by their l…

MolGA: Molecular Graph Adaptation with Pre-trained 2D Graph Encoder
Xingtong Yu, Chang Zhou, Xinming Zhang, Yuan Fang
https://arxiv.org/abs/2510.07289 https://

MolGA: Molecular Graph Adaptation with Pre-trained 2D Graph Encoder
Molecular graph representation learning is widely used in chemical and biomedical research. While pre-trained 2D graph encoders have demonstrated strong performance, they overlook the rich molecular domain knowledge associated with submolecular instances (atoms and bonds). While molecular pre-training approaches incorporate such knowledge into their pre-training objectives, they typically employ designs tailored to a specific type of knowledge, lacking the flexibility to integrate diverse knowl…

KLIPA: A Knowledge Graph and LLM-Driven QA Framework for IP Analysis
Guanzhi Deng, Yi Xie, Yu-Keung Ng, Mingyang Liu, Peijun Zheng, Jie Liu, Dapeng Wu, Yinqiao Li, Linqi Song
https://arxiv.org/abs/2509.07860

KLIPA: A Knowledge Graph and LLM-Driven QA Framework for IP Analysis
Effectively managing intellectual property is a significant challenge. Traditional methods for patent analysis depend on labor-intensive manual searches and rigid keyword matching. These approaches are often inefficient and struggle to reveal the complex relationships hidden within large patent datasets, hindering strategic decision-making. To overcome these limitations, we introduce KLIPA, a novel framework that leverages a knowledge graph and a large language model (LLM) to significantly adva…

KGRAG-SC: Knowledge Graph RAG-Assisted Semantic Communication
Dayu Fan, Rui Meng, Song Gao, Xiaodong Xu
https://arxiv.org/abs/2509.04801 https://arxiv.org/…

KGRAG-SC: Knowledge Graph RAG-Assisted Semantic Communication
The state-of-the-art semantic communication (SC) schemes typically rely on end-to-end deep learning frameworks that lack interpretability and struggle with robust semantic selection and reconstruction under noisy conditions. To address this issue, this paper presents KGRAG-SC, a knowledge graph-assisted SC framework that leverages retrieval-augmented generation principles. KGRAG-SC employs a multi-dimensional knowledge graph, enabling efficient semantic extraction through community-guided entit…

Spectral gap of $G(n, \alpha n, \alpha^2 n)$ graphs and the giant component theorem
M. Koshelev, A. Raigorodskii
https://arxiv.org/abs/2510.06722 https://a…

Spectral gap of $G(n, αn, α^2 n)$ graphs and the giant component theorem
The spectrum of a graph $G$ is the set of the eigenvalues of its adjacency matrix. It turns out that one can say a lot about a graph with the only knowledge being the spectrum of this graph. In this paper we obtain new results about the spectrum of $G(n, αn, α^2 n)$ graphs. We then apply these results to get a giant component theorem for them.

FloodVision: Urban Flood Depth Estimation Using Foundation Vision-Language Models and Domain Knowledge Graph
Zhangding Liu, Neda Mohammadi, John E. Taylor
https://arxiv.org/abs/2509.04772

FloodVision: Urban Flood Depth Estimation Using Foundation Vision-Language Models and Domain Knowledge Graph
Timely and accurate floodwater depth estimation is critical for road accessibility and emergency response. While recent computer vision methods have enabled flood detection, they suffer from both accuracy limitations and poor generalization due to dependence on fixed object detectors and task-specific training. To enable accurate depth estimation that can generalize across diverse flood scenarios, this paper presents FloodVision, a zero-shot framework that combines the semantic reasoning abilit…

Relational Database Distillation: From Structured Tables to Condensed Graph Data
Xinyi Gao, Jingxi Zhang, Lijian Chen, Tong Chen, Lizhen Cui, Hongzhi Yin
https://arxiv.org/abs/2510.06980

Relational Database Distillation: From Structured Tables to Condensed Graph Data
Relational databases (RDBs) underpin the majority of global data management systems, where information is structured into multiple interdependent tables. To effectively use the knowledge within RDBs for predictive tasks, recent advances leverage graph representation learning to capture complex inter-table relations as multi-hop dependencies. Despite achieving state-of-the-art performance, these methods remain hindered by the prohibitive storage overhead and excessive training time, due to the m…

Climate Policy Radar is developing an open-source knowledge graph for climate policy. At Berlin Buzzwords, Harrison Pim and Fred O'Loughlin discussed how they combine in-house expertise with a scalable data infrastructure in order to identify key concepts within thousands of global climate policy documents.
Watch the full session: https://youtu.be/H6BhF6zSvp4?si=Jzioutp8n__2XY2c
Berlin Buzzwords returns on 7-9 June 2026! Get 36% off with our Trust Us Ticket: https://tickets.plainschwarz.com/bbuzz26/c/8Hvk0ZvJA/

Query-Aware Graph Neural Networks for Enhanced Retrieval-Augmented Generation
Vibhor Agrawal, Fay Wang, Rishi Puri
https://arxiv.org/abs/2508.05647 https://

Query-Aware Graph Neural Networks for Enhanced Retrieval-Augmented Generation
We present a novel graph neural network (GNN) architecture for retrieval-augmented generation (RAG) that leverages query-aware attention mechanisms and learned scoring heads to improve retrieval accuracy on complex, multi-hop questions. Unlike traditional dense retrieval methods that treat documents as independent entities, our approach constructs per-episode knowledge graphs that capture both sequential and semantic relationships between text chunks. We introduce an Enhanced Graph Attention Ne…

Flavonoid Fusion: Creating a Knowledge Graph to Unveil the Interplay Between Food and Health
Aryan Singh Dalal, Yinglun Zhang, Duru Do\u{g}an, Atalay Mert \.Ileri, Hande K\"u\c{c}\"uk McGinty
https://arxiv.org/abs/2510.06433

Flavonoid Fusion: Creating a Knowledge Graph to Unveil the Interplay Between Food and Health
The focus on "food as medicine" is gaining traction in the field of health and several studies conducted in the past few years discussed this aspect of food in the literature. However, very little research has been done on representing the relationship between food and health in a standardized, machine-readable format using a semantic web that can help us leverage this knowledge effectively. To address this gap, this study aims to create a knowledge graph to link food and health through the kno…

Improving Knowledge Graph Understanding with Contextual Views
Antrea Christou, Cogan Shimizu
https://arxiv.org/abs/2508.02413 https://arxiv.org/pdf/2508.02…

Improving Knowledge Graph Understanding with Contextual Views
Navigating, visualizing, and discovery in graph data is frequently a difficult prospect. This is especially true for knowledge graphs (KGs), due to high number of possible labeled connections to other data. However, KGs are frequently equipped with an ontology as a schema. That is, it informs how the relationships between data may be constrained. This additional information can be leveraged to improve how (knowledge) graph data can be navigated, visualized, or otherwise utilized in a discover…

Data Skeleton Learning: Scalable Active Clustering with Sparse Graph Structures
Wen-Bo Xie, Xun Fu, Bin Chen, Yan-Li Lee, Tao Deng, Tian Zou, Xin Wang, Zhen Liu, Jaideep Srivastavad
https://arxiv.org/abs/2509.08530

Data Skeleton Learning: Scalable Active Clustering with Sparse Graph Structures
In this work, we focus on the efficiency and scalability of pairwise constraint-based active clustering, crucial for processing large-scale data in applications such as data mining, knowledge annotation, and AI model pre-training. Our goals are threefold: (1) to reduce computational costs for iterative clustering updates; (2) to enhance the impact of user-provided constraints to minimize annotation requirements for precise clustering; and (3) to cut down memory usage in practical deployments. T…

SLiNT: Structure-aware Language Model with Injection and Contrastive Training for Knowledge Graph Completion
Mengxue Yang, Chun Yang, Jiaqi Zhu, Jiafan Li, Jingqi Zhang, Yuyang Li, Ying Li
https://arxiv.org/abs/2509.06531

SLiNT: Structure-aware Language Model with Injection and Contrastive Training for Knowledge Graph Completion
Link prediction in knowledge graphs requires integrating structural information and semantic context to infer missing entities. While large language models offer strong generative reasoning capabilities, their limited exploitation of structural signals often results in structural sparsity and semantic ambiguity, especially under incomplete or zero-shot settings. To address these challenges, we propose SLiNT (Structure-aware Language model with Injection and coNtrastive Training), a modular fram…

Aligning LLMs for the Classroom with Knowledge-Based Retrieval -- A Comparative RAG Study
Amay Jain, Liu Cui, Si Chen
https://arxiv.org/abs/2509.07846 https://

Aligning LLMs for the Classroom with Knowledge-Based Retrieval -- A Comparative RAG Study
Large language models like ChatGPT are increasingly used in classrooms, but they often provide outdated or fabricated information that can mislead students. Retrieval Augmented Generation (RAG) improves reliability of LLMs by grounding responses in external resources. We investigate two accessible RAG paradigms, vector-based retrieval and graph-based retrieval to identify best practices for classroom question answering (QA). Existing comparative studies fail to account for pedagogical factors s…

REMOTE: A Unified Multimodal Relation Extraction Framework with Multilevel Optimal Transport and Mixture-of-Experts
Xinkui Lin, Yongxiu Xu, Minghao Tang, Shilong Zhang, Hongbo Xu, Hao Xu, Yubin Wang
https://arxiv.org/abs/2509.04844

REMOTE: A Unified Multimodal Relation Extraction Framework with Multilevel Optimal Transport and Mixture-of-Experts
Multimodal relation extraction (MRE) is a crucial task in the fields of Knowledge Graph and Multimedia, playing a pivotal role in multimodal knowledge graph construction. However, existing methods are typically limited to extracting a single type of relational triplet, which restricts their ability to extract triplets beyond the specified types. Directly combining these methods fails to capture dynamic cross-modal interactions and introduces significant computational redundancy. Therefore, we p…

R2GenKG: Hierarchical Multi-modal Knowledge Graph for LLM-based Radiology Report Generation
Futian Wang, Yuhan Qiao, Xiao Wang, Fuling Wang, Yuxiang Zhang, Dengdi Sun
https://arxiv.org/abs/2508.03426

R2GenKG: Hierarchical Multi-modal Knowledge Graph for LLM-based Radiology Report Generation
X-ray medical report generation is one of the important applications of artificial intelligence in healthcare. With the support of large foundation models, the quality of medical report generation has significantly improved. However, challenges such as hallucination and weak disease diagnostic capability still persist. In this paper, we first construct a large-scale multi-modal medical knowledge graph (termed M3KG) based on the ground truth medical report using the GPT-4o. It contains 2477 enti…

MultiCNKG: Integrating Cognitive Neuroscience, Gene, and Disease Knowledge Graphs Using Large Language Models
Ali Sarabadani, Kheirolah Rahsepar Fard
https://arxiv.org/abs/2510.06742

MultiCNKG: Integrating Cognitive Neuroscience, Gene, and Disease Knowledge Graphs Using Large Language Models
The advent of large language models (LLMs) has revolutionized the integration of knowledge graphs (KGs) in biomedical and cognitive sciences, overcoming limitations in traditional machine learning methods for capturing intricate semantic links among genes, diseases, and cognitive processes. We introduce MultiCNKG, an innovative framework that merges three key knowledge sources: the Cognitive Neuroscience Knowledge Graph (CNKG) with 2.9K nodes and 4.3K edges across 9 node types and 20 edge types…

KERAG: Knowledge-Enhanced Retrieval-Augmented Generation for Advanced Question Answering
Yushi Sun, Kai Sun, Yifan Ethan Xu, Xiao Yang, Xin Luna Dong, Nan Tang, Lei Chen
https://arxiv.org/abs/2509.04716

KERAG: Knowledge-Enhanced Retrieval-Augmented Generation for Advanced Question Answering
Retrieval-Augmented Generation (RAG) mitigates hallucination in Large Language Models (LLMs) by incorporating external data, with Knowledge Graphs (KGs) offering crucial information for question answering. Traditional Knowledge Graph Question Answering (KGQA) methods rely on semantic parsing, which typically retrieves knowledge strictly necessary for answer generation, thus often suffer from low coverage due to rigid schema requirements and semantic ambiguity. We present KERAG, a novel KG-based…

In our last #ISE2025 lecture last week, we were discussing what makes a node "important" in a knowledge graph. A simple heuristics can be borrowed from graph theory or communication theory: Degree Centrality
Interestingly, in Wikidata In-degree centrality states Jane Austen to be to most "important" female author, while Out-degree centrality claims J.K. Rowling as being…

KG-RAG: Enhancing GUI Agent Decision-Making via Knowledge Graph-Driven Retrieval-Augmented Generation
Ziyi Guan, Jason Chun Lok Li, Zhijian Hou, Pingping Zhang, Donglai Xu, Yuzhi Zhao, Mengyang Wu, Jinpeng Chen, Thanh-Toan Nguyen, Pengfei Xian, Wenao Ma, Shengchao Qin, Graziano Chesi, Ngai Wong
https://arxiv.org/abs/2509.00366

KG-RAG: Enhancing GUI Agent Decision-Making via Knowledge Graph-Driven Retrieval-Augmented Generation
Despite recent progress, Graphic User Interface (GUI) agents powered by Large Language Models (LLMs) struggle with complex mobile tasks due to limited app-specific knowledge. While UI Transition Graphs (UTGs) offer structured navigation representations, they are underutilized due to poor extraction and inefficient integration. We introduce KG-RAG, a Knowledge Graph-driven Retrieval-Augmented Generation framework that transforms fragmented UTGs into structured vector databases for efficient real…

Deterministic Legal Retrieval: An Action API for Querying the SAT-Graph RAG
Hudson de Martim
https://arxiv.org/abs/2510.06002 https://arxiv.org/pdf/2510.06…

Deterministic Legal Retrieval: An Action API for Querying the SAT-Graph RAG
The Structure-Aware Temporal Graph RAG (SAT-Graph RAG) addresses core limitations of standard Retrieval-Augmented Generation in the legal domain by providing a verifiable knowledge graph that models hierarchical structure, temporal evolution, and causal events of legal norms. However, a critical gap remains: how to reliably query this structured knowledge without sacrificing its deterministic properties. This paper introduces the SAT-Graph API, a formal query execution layer centered on canonic…

Exposing Privacy Risks in Graph Retrieval-Augmented Generation
Jiale Liu, Jiahao Zhang, Suhang Wang
https://arxiv.org/abs/2508.17222 https://arxiv.org/pdf/…

Exposing Privacy Risks in Graph Retrieval-Augmented Generation
Retrieval-Augmented Generation (RAG) is a powerful technique for enhancing Large Language Models (LLMs) with external, up-to-date knowledge. Graph RAG has emerged as an advanced paradigm that leverages graph-based knowledge structures to provide more coherent and contextually rich answers. However, the move from plain document retrieval to structured graph traversal introduces new, under-explored privacy risks. This paper investigates the data extraction vulnerabilities of the Graph RAG systems…

Interactive Graph Visualization and TeamingRecommendation in an Interdisciplinary Project'sTalent Knowledge Graph
Jiawei Xu, Juichien Chen, Yilin Ye, Zhandos Sembay, Swathi Thaker, Pamela Payne-Foster, Jake Chen, Ying Ding
https://arxiv.org/abs/2508.19489

Interactive Graph Visualization and TeamingRecommendation in an Interdisciplinary Project'sTalent Knowledge Graph
Interactive visualization of large scholarly knowledge graphs combined with LLM reasoning shows promise butremains under-explored. We address this gap by developing an interactive visualization system for the Cell Map forAI Talent Knowledge Graph (28,000 experts and 1,179 biomedical datasets). Our approach integrates WebGLvisualization with LLM agents to overcome limitations of traditional tools such as Gephi, particularly for large-scaleinteractive node handling. Key functionalities include re…

Perfect Graph Modification Problems: An Integer Programming Approach
Burak Nur Erdem, T{\i}naz Ekim, Zeki Caner Ta\c{s}k{\i}n
https://arxiv.org/abs/2507.21987 https://

Perfect Graph Modification Problems: An Integer Programming Approach
Graph modification problems, which aim to find a small set of modifications to a graph so that it satisfies a desired property, have been studied for several special graph classes. The literature is rather rich in NP-completeness results and polynomial time solvable cases. However, to the best of our knowledge, only a few exact algorithms have been suggested to address NP-hard cases. In this work, we propose exact solution methods based on integer programming for three perfect graph modificatio…

On Monday I started a new GitHub repo, to implement a distributed knowledge graph that knows about stuff in houses and how they are related. I used the Claude AI tools and Claude-flow agent framework to write all the code. Got a simplified thing running and cleaned up on Tuesday, built the full functionality on Wednesday and after a bit more testing and documentation wrapped up work before lunch on Thursday. It’s a lot of well tested and documented functionality.

Enabling Down Syndrome Research through a Knowledge Graph-Driven Analytical Framework
Madan Krishnamurthy, Surya Saha, Pierrette Lo, Patricia L. Whetzel, Tursynay Issabekova, Jamed Ferreris Vargas, Jack DiGiovanna, Melissa A Haendel
https://arxiv.org/abs/2509.01565

Enabling Down Syndrome Research through a Knowledge Graph-Driven Analytical Framework
Trisomy 21 results in Down syndrome, a multifaceted genetic disorder with diverse clinical phenotypes, including heart defects, immune dysfunction, neurodevelopmental differences, and early-onset dementia risk. Heterogeneity and fragmented data across studies challenge comprehensive research and translational discovery. The NIH INCLUDE (INvestigation of Co-occurring conditions across the Lifespan to Understand Down syndromE) initiative has assembled harmonized participant-level datasets, yet re…

Crosslisted article(s) found for cs.DB. https://arxiv.org/list/cs.DB/new
[1/1]:
- Knowledge Graph-Guided Multi-Agent Distillation for Reliable Industrial Question Answering with D...
Jiqun Pan, Zhenke Duan, Jiani Tu, Anzhi Cheng, Yanqing Wang

HySemRAG: A Hybrid Semantic Retrieval-Augmented Generation Framework for Automated Literature Synthesis and Methodological Gap Analysis
Alejandro Godinez
https://arxiv.org/abs/2508.05666

HySemRAG: A Hybrid Semantic Retrieval-Augmented Generation Framework for Automated Literature Synthesis and Methodological Gap Analysis
We present HySemRAG, a framework that combines Extract, Transform, Load (ETL) pipelines with Retrieval-Augmented Generation (RAG) to automate large-scale literature synthesis and identify methodological research gaps. The system addresses limitations in existing RAG architectures through a multi-layered approach: hybrid retrieval combining semantic search, keyword filtering, and knowledge graph traversal; an agentic self-correction framework with iterative quality assurance; and post-hoc citati…

Hello from #CoRDI2025 👋 !
🛠️ This year @… is presenting how our Culture Information Portal, the Metadata API, and the Culture Knowledge Graph work together to make #culturalheritage

Sparse Graph Reconstruction and Seriation for Large-Scale Image Stacks
Fuming Yang, Yaron Meirovitch, Jeff W. Lichtman
https://arxiv.org/abs/2509.23084 https://

Sparse Graph Reconstruction and Seriation for Large-Scale Image Stacks
We study recovering a 1D order from a noisy, locally sampled pairwise comparison matrix under a tight query budget. We recast the task as reconstructing a sparse, noisy line graph and present, to our knowledge, the first method that provably builds a sparse graph containing all edges needed for exact seriation using only O(N(log N + K)) oracle queries, which is near-linear in N for fixed window K. The approach is parallelizable and supports both binary and bounded-noise distance oracles. Our fi…

KEEP: Integrating Medical Ontologies with Clinical Data for Robust Code Embeddings
Ahmed Elhussein, Paul Meddeb, Abigail Newbury, Jeanne Mirone, Martin Stoll, Gamze Gursoy
https://arxiv.org/abs/2510.05049

KEEP: Integrating Medical Ontologies with Clinical Data for Robust Code Embeddings
Machine learning in healthcare requires effective representation of structured medical codes, but current methods face a trade off: knowledge graph based approaches capture formal relationships but miss real world patterns, while data driven methods learn empirical associations but often overlook structured knowledge in medical terminologies. We present KEEP (Knowledge preserving and Empirically refined Embedding Process), an efficient framework that bridges this gap by combining knowledge grap…

Hybrid Graph Embeddings and Louvain Algorithm for Unsupervised Community Detection
Dalila Khettaf, Djamel Djenouri, Zeinab Rezaeifar, Youcef Djenouri
https://arxiv.org/abs/2509.23411

Hybrid Graph Embeddings and Louvain Algorithm for Unsupervised Community Detection
This paper proposes a novel community detection method that integrates the Louvain algorithm with Graph Neural Networks (GNNs), enabling the discovery of communities without prior knowledge. Compared to most existing solutions, the proposed method does not require prior knowledge of the number of communities. It enhances the Louvain algorithm using node embeddings generated by a GNN to capture richer structural and feature information. Furthermore, it introduces a merging algorithm to refine th…

Pi-SAGE: Permutation-invariant surface-aware graph encoder for binding affinity prediction
Sharmi Banerjee, Mostafa Karimi, Melih Yilmaz, Tommi Jaakkola, Bella Dubrov, Shang Shang, Ron Benson
https://arxiv.org/abs/2508.01924

Pi-SAGE: Permutation-invariant surface-aware graph encoder for binding affinity prediction
Protein surface fingerprint encodes chemical and geometric features that govern protein-protein interactions and can be used to predict changes in binding affinity between two protein complexes. Current state-of-the-art models for predicting binding affinity change, such as GearBind, are all-atom based geometric models derived from protein structures. Although surface properties can be implicitly learned from the protein structure, we hypothesize that explicit knowledge of protein surfaces can …

Difference Views for Visual Graph Query Building
Benedikt Kantz, Stefan Lengauer, Peter Waldert, Tobias Schreck
https://arxiv.org/abs/2508.05314 https://ar…

Difference Views for Visual Graph Query Building
Knowledge Graphs (KGs) contain vast amounts of linked resources that encode knowledge in various domains, which can be queried and searched for using specialized languages like SPARQL, a query language developed to query KGs. Existing visual query builders enable non-expert users to construct SPARQL queries and utilize the knowledge contained in these graphs. Query building is, however, an iterative and, often, visual process where the question of the user can change and differ throughout the p…

A Knowledge Graph Informing Soil Carbon Modeling
Nasim Shirvani-Mahdavi, Devin Wingfield, Juan Guajardo Gutierrez, Mai Tran, Zhengyuan Zhu, Zeyu Zhang, Haiqi Zhang, Abhishek Divakar Goudar, Chengkai Li, Virginia Jin, Timothy Propst, Dan Roberts, Catherine Stewart, Jianzhong Su, Jennifer Woodward-Greene
https://arxiv.org/abs/2508.10965

A Knowledge Graph Informing Soil Carbon Modeling
Soil organic carbon is crucial for climate change mitigation and agricultural sustainability. However, understanding its dynamics requires integrating complex, heterogeneous data from multiple sources. This paper introduces the Soil Organic Carbon Knowledge Graph (SOCKG), a semantic infrastructure designed to transform agricultural research data into a queryable knowledge representation. SOCKG features a robust ontological model of agricultural experimental data, enabling precise mapping of dat…

In our last #ISE2025 lecture last week, we were discussing what makes a node "important" in a knowledge graph. A simple heuristics can be borrowed from graph theory or communication theory: Degree Centrality
Interestingly, in Wikidata In-degree centrality states Jane Austen to be to most "important" female author, while Out-degree centrality claims J.K. Rowling as being…

VL-KnG: Visual Scene Understanding for Navigation Goal Identification using Spatiotemporal Knowledge Graphs
Mohamad Al Mdfaa, Svetlana Lukina, Timur Akhtyamov, Arthur Nigmatzyanov, Dmitrii Nalberskii, Sergey Zagoruyko, Gonzalo Ferrer
https://arxiv.org/abs/2510.01483

VL-KnG: Visual Scene Understanding for Navigation Goal Identification using Spatiotemporal Knowledge Graphs
Vision-language models (VLMs) have shown potential for robot navigation but encounter fundamental limitations: they lack persistent scene memory, offer limited spatial reasoning, and do not scale effectively with video duration for real-time application. We present VL-KnG, a Visual Scene Understanding system that tackles these challenges using spatiotemporal knowledge graph construction and computationally efficient query processing for navigation goal identification. Our approach processes vid…

RTQA : Recursive Thinking for Complex Temporal Knowledge Graph Question Answering with Large Language Models
Zhaoyan Gong, Juan Li, Zhiqiang Liu, Lei Liang, Huajun Chen, Wen Zhang
https://arxiv.org/abs/2509.03995

RTQA : Recursive Thinking for Complex Temporal Knowledge Graph Question Answering with Large Language Models
Current temporal knowledge graph question answering (TKGQA) methods primarily focus on implicit temporal constraints, lacking the capability of handling more complex temporal queries, and struggle with limited reasoning abilities and error propagation in decomposition frameworks. We propose RTQA, a novel framework to address these challenges by enhancing reasoning over TKGs without requiring training. Following recursive thinking, RTQA recursively decomposes questions into sub-problems, solves …

Data Overdose? Time for a Quadruple Shot: Knowledge Graph Construction using Enhanced Triple Extraction
Taine J. Elliott, Stephen P. Levitt, Ken Nixon, Martin Bekker
https://arxiv.org/abs/2508.03438

Data Overdose? Time for a Quadruple Shot: Knowledge Graph Construction using Enhanced Triple Extraction
The rapid expansion of publicly-available medical data presents a challenge for clinicians and researchers alike, increasing the gap between the volume of scientific literature and its applications. The steady growth of studies and findings overwhelms medical professionals at large, hindering their ability to systematically review and understand the latest knowledge. This paper presents an approach to information extraction and automatic knowledge graph (KG) generation to identify and connect b…

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[3/4]:
- Low-Dimensional Federated Knowledge Graph Embedding via Knowledge Distillation
Xiaoxiong Zhang, Zhiwei Zeng, Xin Zhou, Zhiqi Shen

Improving Factuality in LLMs via Inference-Time Knowledge Graph Construction
Shanglin Wu, Lihui Liu, Jinho D. Choi, Kai Shu
https://arxiv.org/abs/2509.03540 https://

Improving Factuality in LLMs via Inference-Time Knowledge Graph Construction
Large Language Models (LLMs) often struggle with producing factually consistent answers due to limitations in their parametric memory. Retrieval-Augmented Generation (RAG) methods address this issue by incorporating external knowledge from trusted sources at inference time. However, such methods typically treat knowledge as unstructured text, which limits their ability to support compositional reasoning and identify factual inconsistencies. To overcome these limitations, we propose a novel fram…

The KG-ER Conceptual Schema Language
Enrico Franconi, Beno\^it Groz, Jan Hidders, Nina Pardal, S{\l}awek Staworko, Jan Van den Bussche, Piotr Wieczorek
https://arxiv.org/abs/2508.02548

The KG-ER Conceptual Schema Language
We propose KG-ER, a conceptual schema language for knowledge graphs that describes the structure of knowledge graphs independently of their representation (relational databases, property graphs, RDF) while helping to capture the semantics of the information stored in a knowledge graph.

Dark Side of Modalities: Reinforced Multimodal Distillation for Multimodal Knowledge Graph Reasoning
Yu Zhao, Ying Zhang, Xuhui Sui, Baohang Zhou, Haoze Zhu, Jeff Z. Pan, Xiaojie Yuan
https://arxiv.org/abs/2507.20738

Dark Side of Modalities: Reinforced Multimodal Distillation for Multimodal Knowledge Graph Reasoning
The multimodal knowledge graph reasoning (MKGR) task aims to predict the missing facts in the incomplete MKGs by leveraging auxiliary images and descriptions of entities. Existing approaches are trained with single-target objectives, which neglect the probabilistic correlations of entity labels, especially in non-target entities. Moreover, previous studies incorporate all modalities statically or adaptively, overlooking the negative impacts of irrelevant or misleading information in the incompe…

VDGraph: A Graph-Theoretic Approach to Unlock Insights from SBOM and SCA Data
Howell Xia, Jonah Gluck, Sevval Simsek, David Sastre Medina, David Starobinski
https://arxiv.org/abs/2507.20502

VDGraph: A Graph-Theoretic Approach to Unlock Insights from SBOM and SCA Data
The high complexity of modern software supply chains necessitates tools such as Software Bill of Materials (SBOMs) to manage component dependencies, and Software Composition Analysis (SCA) tools to identify vulnerabilities. While there exists limited integration between SBOMs and SCA tools, a unified view of complex dependency-vulnerability relationships remains elusive. In this paper, we introduce VDGraph, a novel knowledge graph-based methodology for integrating vulnerability and dependency d…

Knowledge-Graph Based RAG System Evaluation Framework
Sicheng Dong, Vahid Zolfaghari, Nenad Petrovic, Alois Knoll
https://arxiv.org/abs/2510.02549 https://…

Knowledge-Graph Based RAG System Evaluation Framework
Large language models (LLMs) has become a significant research focus and is utilized in various fields, such as text generation and dialog systems. One of the most essential applications of LLM is Retrieval Augmented Generation (RAG), which greatly enhances generated content's reliability and relevance. However, evaluating RAG systems remains a challenging task. Traditional evaluation metrics struggle to effectively capture the key features of modern LLM-generated content that often exhibits hi…

Explainable Knowledge Graph Retrieval-Augmented Generation (KG-RAG) with KG-SMILE
Zahra Zehtabi Sabeti Moghaddam, Zeinab Dehghani, Maneeha Rani, Koorosh Aslansefat, Bhupesh Kumar Mishra, Rameez Raja Kureshi, Dhavalkumar Thakker
https://arxiv.org/abs/2509.03626

Explainable Knowledge Graph Retrieval-Augmented Generation (KG-RAG) with KG-SMILE
Generative AI, such as Large Language Models (LLMs), has achieved impressive progress but still produces hallucinations and unverifiable claims, limiting reliability in sensitive domains. Retrieval-Augmented Generation (RAG) improves accuracy by grounding outputs in external knowledge, especially in domains like healthcare, where precision is vital. However, RAG remains opaque and essentially a black box, heavily dependent on data quality. We developed a method-agnostic, perturbation-based fram…

You are an expert in ontologies and kn owledge graphs? You have an interest (or even better expertise) in materials science engineering? Then you should apply! Open PostDoc/Senior Researcher position at FIZ Karlsruhe ISE Research Team in the Knowledge Graph department. More info at https://www.fiz-k…

Systematic Evaluation of Knowledge Graph Repair with Large Language Models
Tung-Wei Lin, Gabe Fierro, Han Li, Tianzhen Hong, Pierluigi Nuzzo, Alberto Sangiovanni-Vinentelli
https://arxiv.org/abs/2507.22419

Systematic Evaluation of Knowledge Graph Repair with Large Language Models
We present a systematic approach for evaluating the quality of knowledge graph repairs with respect to constraint violations defined in shapes constraint language (SHACL). Current evaluation methods rely on \emph{ad hoc} datasets, which limits the rigorous analysis of repair systems in more general settings. Our method addresses this gap by systematically generating violations using a novel mechanism, termed violation-inducing operations (VIOs). We use the proposed evaluation framework to asses…

Efficient and Transferable Agentic Knowledge Graph RAG via Reinforcement Learning
Jinyeop Song, Song Wang, Julian Shun, Yada Zhu
https://arxiv.org/abs/2509.26383 https://…

Efficient and Transferable Agentic Knowledge Graph RAG via Reinforcement Learning
Knowledge-graph retrieval-augmented generation (KG-RAG) couples large language models (LLMs) with structured, verifiable knowledge graphs (KGs) to reduce hallucinations and expose reasoning traces. However, many KG-RAG systems compose multiple LLM modules (e.g planning, reasoning, and responding), inflating inference cost and binding behavior to a specific target KG. To address this, we introduce KG-R1, an agentic KG retrieval-augmented generation (KG-RAG) framework through reinforcement learni…

OntoAligner Meets Knowledge Graph Embedding Aligners
Hamed Babaei Giglou, Jennifer D'Souza, S\"oren Auer, Mahsa Sanaei
https://arxiv.org/abs/2509.26417 https://

OntoAligner Meets Knowledge Graph Embedding Aligners
Ontology Alignment (OA) is essential for enabling semantic interoperability across heterogeneous knowledge systems. While recent advances have focused on large language models (LLMs) for capturing contextual semantics, this work revisits the underexplored potential of Knowledge Graph Embedding (KGE) models, which offer scalable, structure-aware representations well-suited to ontology-based tasks. Despite their effectiveness in link prediction, KGE methods remain underutilized in OA, with most p…

Replaced article(s) found for cs.IR. https://arxiv.org/list/cs.IR/new
[1/1]:
- Soft Reasoning Paths for Knowledge Graph Completion
Hou, Zhou, Liang, Meng, Chen, Xu, Wang, Liu, Huang

One of our final topics in the #ISE2025 lecture were Knowledge Graph Embeddings. How to vectorise KG structures while preserving their inherent semantics?
#AI #KGE

ARUQULA -- An LLM based Text2SPARQL Approach using ReAct and Knowledge Graph Exploration Utilities
Felix Brei, Lorenz B\"uhmann, Johannes Frey, Daniel Gerber, Lars-Peter Meyer, Claus Stadler, Kirill Bulert
https://arxiv.org/abs/2510.02200

ARUQULA -- An LLM based Text2SPARQL Approach using ReAct and Knowledge Graph Exploration Utilities
Interacting with knowledge graphs can be a daunting task for people without a background in computer science since the query language that is used (SPARQL) has a high barrier of entry. Large language models (LLMs) can lower that barrier by providing support in the form of Text2SPARQL translation. In this paper we introduce a generalized method based on SPINACH, an LLM backed agent that translates natural language questions to SPARQL queries not in a single shot, but as an iterative process of e…

Continuous Monitoring of Large-Scale Generative AI via Deterministic Knowledge Graph Structures
Kishor Datta Gupta, Mohd Ariful Haque, Hasmot Ali, Marufa Kamal, Syed Bahauddin Alam, Mohammad Ashiqur Rahman
https://arxiv.org/abs/2509.03857

Continuous Monitoring of Large-Scale Generative AI via Deterministic Knowledge Graph Structures
Generative AI (GEN AI) models have revolutionized diverse application domains but present substantial challenges due to reliability concerns, including hallucinations, semantic drift, and inherent biases. These models typically operate as black-boxes, complicating transparent and objective evaluation. Current evaluation methods primarily depend on subjective human assessment, limiting scalability, transparency, and effectiveness. This research proposes a systematic methodology using determinist…

Explaining Fine Tuned LLMs via Counterfactuals A Knowledge Graph Driven Framework
Yucheng Wang, Ziyang Chen, Md Faisal Kabir
https://arxiv.org/abs/2509.21241 https://

Explaining Fine Tuned LLMs via Counterfactuals A Knowledge Graph Driven Framework
The widespread adoption of Low-Rank Adaptation (LoRA) has enabled large language models (LLMs) to acquire domain-specific knowledge with remarkable efficiency. However, understanding how such a fine-tuning mechanism alters a model's structural reasoning and semantic behavior remains an open challenge. This work introduces a novel framework that explains fine-tuned LLMs via counterfactuals grounded in knowledge graphs. Specifically, we construct BioToolKG, a domain-specific heterogeneous knowled…

OpenZL: A Graph-Based Model for Compression
Yann Collet, Nick Terrell, W. Felix Handte, Danielle Rozenblit, Victor Zhang, Kevin Zhang, Yaelle Goldschlag, Jennifer Lee, Daniel Riegel, Stan Angelov, Nadav Rotem
https://arxiv.org/abs/2510.03203

OpenZL: A Graph-Based Model for Compression
Research in general-purpose lossless compression over the last decade has largely found improvements in compression ratio that come at great cost to resource utilization and processing throughput. However, most production workloads require high throughput and low resource utilization, so most research systems have seen little adoption. Instead, real world improvements in compression are increasingly often realized by building application-specific compressors which can exploit knowledge about th…

Is SHACL Suitable for Data Quality Assessment?
Carolina Cort\'es Lasalle, Lisa Ehrlinger, Lorena Etcheverry, Felix Naumann
https://arxiv.org/abs/2507.22305 https://

Is SHACL Suitable for Data Quality Assessment?
Knowledge graphs have been widely adopted in both enterprises, such as the Google Knowledge Graph, and open platforms like Wikidata to represent domain knowledge and support analysis with artificial intelligence. They model real-world information as nodes and edges. To embrace flexibility, knowledge graphs often lack enforced schemas (i.e., ontologies), leading to potential data quality issues, such as semantically overlapping nodes. Therefore, ensuring their quality is essential, as issues in …

Exploring Network-Knowledge Graph Duality: A Case Study in Agentic Supply Chain Risk Analysis
Evan Heus, Rick Bookstaber, Dhruv Sharma
https://arxiv.org/abs/2510.01115 https://

Exploring Network-Knowledge Graph Duality: A Case Study in Agentic Supply Chain Risk Analysis
Large Language Models (LLMs) struggle with the complex, multi-modal, and network-native data underlying financial risk. Standard Retrieval-Augmented Generation (RAG) oversimplifies relationships, while specialist models are costly and static. We address this gap with an LLM-centric agent framework for supply chain risk analysis. Our core contribution is to exploit the inherent duality between networks and knowledge graphs (KG). We treat the supply chain network as a KG, allowing us to use struc…

DBLPLink 2.0 -- An Entity Linker for the DBLP Scholarly Knowledge Graph
Debayan Banerjee, Tilahun Abedissa Taffa, Ricardo Usbeck
https://arxiv.org/abs/2507.22811 https://…

DBLPLink 2.0 -- An Entity Linker for the DBLP Scholarly Knowledge Graph
In this work we present an entity linker for DBLP's 2025 version of RDF-based Knowledge Graph. Compared to the 2022 version, DBLP now considers publication venues as a new entity type called dblp:Stream. In the earlier version of DBLPLink, we trained KG-embeddings and re-rankers on a dataset to produce entity linkings. In contrast, in this work, we develop a zero-shot entity linker using LLMs using a novel method, where we re-rank candidate entities based on the log-probabilities of the "yes" t…

RareAgent: Self-Evolving Reasoning for Drug Repurposing in Rare Diseases
Lang Qin, Zijian Gan, Xu Cao, Pengcheng Jiang, Yankai Jiang, Jiawei Han, Kaishun Wu, Jintai Chen
https://arxiv.org/abs/2510.05764

RareAgent: Self-Evolving Reasoning for Drug Repurposing in Rare Diseases
Computational drug repurposing for rare diseases is especially challenging when no prior associations exist between drugs and target diseases. Therefore, knowledge graph completion and message-passing GNNs have little reliable signal to learn and propagate, resulting in poor performance. We present RareAgent, a self-evolving multi-agent system that reframes this task from passive pattern recognition to active evidence-seeking reasoning. RareAgent organizes task-specific adversarial debates in w…

AI4DiTraRe: Building the BFO-Compliant Chemotion Knowledge Graph
Ebrahim Norouzi, Nicole Jung, Anna M. Jacyszyn, J\"org Waitelonis, Harald Sack
https://arxiv.org/abs/2509.01536

AI4DiTraRe: Building the BFO-Compliant Chemotion Knowledge Graph
Chemistry is an example of a discipline where the advancements of technology have led to multi-level and often tangled and tricky processes ongoing in the lab. The repeatedly complex workflows are combined with information from chemical structures, which are essential to understand the scientific process. An important tool for many chemists is Chemotion, which consists of an electronic lab notebook and a repository. This paper introduces a semantic pipeline for constructing the BFO-compliant Ch…

Exploring Network-Knowledge Graph Duality: A Case Study in Agentic Supply Chain Risk Analysis
Evan Heus, Rick Bookstaber, Dhruv Sharma
https://arxiv.org/abs/2510.01115 https://

Exploring Network-Knowledge Graph Duality: A Case Study in Agentic Supply Chain Risk Analysis
Large Language Models (LLMs) struggle with the complex, multi-modal, and network-native data underlying financial risk. Standard Retrieval-Augmented Generation (RAG) oversimplifies relationships, while specialist models are costly and static. We address this gap with an LLM-centric agent framework for supply chain risk analysis. Our core contribution is to exploit the inherent duality between networks and knowledge graphs (KG). We treat the supply chain network as a KG, allowing us to use struc…

Rule2Text: Natural Language Explanation of Logical Rules in Knowledge Graphs
Nasim Shirvani-Mahdavi, Devin Wingfield, Amin Ghasemi, Chengkai Li
https://arxiv.org/abs/2507.23740 …

Rule2Text: Natural Language Explanation of Logical Rules in Knowledge Graphs
Knowledge graphs (KGs) often contain sufficient information to support the inference of new facts. Identifying logical rules not only improves the completeness of a knowledge graph but also enables the detection of potential errors, reveals subtle data patterns, and enhances the overall capacity for reasoning and interpretation. However, the complexity of such rules, combined with the unique labeling conventions of each KG, can make them difficult for humans to understand. In this paper, we exp…

Towards Temporal Knowledge Graph Alignment in the Wild
Runhao Zhao, Weixin Zeng, Wentao Zhang, Xiang Zhao, Jiuyang Tang, Lei Chen
https://arxiv.org/abs/2507.14475

Towards Temporal Knowledge Graph Alignment in the Wild
Temporal Knowledge Graph Alignment (TKGA) seeks to identify equivalent entities across heterogeneous temporal knowledge graphs (TKGs) for fusion to improve their completeness. Although some approaches have been proposed to tackle this task, most assume unified temporal element standards and simplified temporal structures across different TKGs. They cannot deal with TKGA in the wild (TKGA-Wild), where multi-scale temporal element entanglement and cross-source temporal structural imbalances are c…

Efficient Knowledge Graph Unlearning with Zeroth-order Information
Yang Xiao, Ruimeng Ye, Bohan Liu, Xiaolong Ma, Bo Hui
https://arxiv.org/abs/2508.14013 https://

Efficient Knowledge Graph Unlearning with Zeroth-order Information
Due to regulations like the Right to be Forgotten, there is growing demand for removing training data and its influence from models. Since full retraining is costly, various machine unlearning methods have been proposed. In this paper, we firstly present an efficient knowledge graph (KG) unlearning algorithm. We remark that KG unlearning is nontrivial due to the distinctive structure of KG and the semantic relations between entities. Also, unlearning by estimating the influence of removed compo…

An Epidemiological Knowledge Graph extracted from the World Health Organization's Disease Outbreak News
Sergio Consoli, Pietro Coletti, Peter V. Markov, Lia Orfei, Indaco Biazzo, Lea Schuh, Nicolas Stefanovitch, Lorenzo Bertolini, Mario Ceresa, Nikolaos I. Stilianakis
https://arxiv.org/abs/2509.02258

An Epidemiological Knowledge Graph extracted from the World Health Organization's Disease Outbreak News
The rapid evolution of artificial intelligence (AI), together with the increased availability of social media and news for epidemiological surveillance, are marking a pivotal moment in epidemiology and public health research. Leveraging the power of generative AI, we use an ensemble approach which incorporates multiple Large Language Models (LLMs) to extract valuable actionable epidemiological information from the World Health Organization (WHO) Disease Outbreak News (DONs). DONs is a collectio…

Beyond Interactions: Node-Level Graph Generation for Knowledge-Free Augmentation in Recommender Systems
Zhaoyan Wang, Hyunjun Ahn, In-Young Ko
https://arxiv.org/abs/2507.20578 h…

Beyond Interactions: Node-Level Graph Generation for Knowledge-Free Augmentation in Recommender Systems
Recent advances in recommender systems rely on external resources such as knowledge graphs or large language models to enhance recommendations, which limit applicability in real-world settings due to data dependency and computational overhead. Although knowledge-free models are able to bolster recommendations by direct edge operations as well, the absence of augmentation primitives drives them to fall short in bridging semantic and structural gaps as high-quality paradigm substitutes. Unlike ex…

Knowledge Graph-Infused Fine-Tuning for Structured Reasoning in Large Language Models
Wuyang Zhang, Yexin Tian, Xiandong Meng, Mengjie Wang, Junliang Du
https://arxiv.org/abs/2508.14427

Knowledge Graph-Infused Fine-Tuning for Structured Reasoning in Large Language Models
This paper addresses the problems of missing reasoning chains and insufficient entity-level semantic understanding in large language models when dealing with tasks that require structured knowledge. It proposes a fine-tuning algorithm framework based on knowledge graph injection. The method builds on pretrained language models and introduces structured graph information for auxiliary learning. A graph neural network is used to encode entities and their relations, constructing a graph-based sema…

PG-Agent: An Agent Powered by Page Graph
Weizhi Chen, Ziwei Wang, Leyang Yang, Sheng Zhou, Xiaoxuan Tang, Jiajun Bu, Yong Li, Wei Jiang
https://arxiv.org/abs/2509.03536 https://…

PG-Agent: An Agent Powered by Page Graph
Graphical User Interface (GUI) agents possess significant commercial and social value, and GUI agents powered by advanced multimodal large language models (MLLMs) have demonstrated remarkable potential. Currently, existing GUI agents usually utilize sequential episodes of multi-step operations across pages as the prior GUI knowledge, which fails to capture the complex transition relationship between pages, making it challenging for the agents to deeply perceive the GUI environment and generaliz…

ARK-V1: An LLM-Agent for Knowledge Graph Question Answering Requiring Commonsense Reasoning
Jan-Felix Klein, Lars Ohnemus
https://arxiv.org/abs/2509.18063 https://

ARK-V1: An LLM-Agent for Knowledge Graph Question Answering Requiring Commonsense Reasoning
Large Language Models (LLMs) show strong reasoning abilities but rely on internalized knowledge that is often insufficient, outdated, or incorrect when trying to answer a question that requires specific domain knowledge. Knowledge Graphs (KGs) provide structured external knowledge, yet their complexity and multi-hop reasoning requirements make integration challenging. We present ARK-V1, a simple KG-agent that iteratively explores graphs to answer natural language queries. We evaluate several no…

Beyond Chunks and Graphs: Retrieval-Augmented Generation through Triplet-Driven Thinking
Shengbo Gong, Xianfeng Tang, Carl Yang, Wei jin
https://arxiv.org/abs/2508.02435 https:/…

Beyond Chunks and Graphs: Retrieval-Augmented Generation through Triplet-Driven Thinking
Retrieval-augmented generation (RAG) is critical for reducing hallucinations and incorporating external knowledge into Large Language Models (LLMs). However, advanced RAG systems face a trade-off between performance and efficiency. Multi-round RAG approaches achieve strong reasoning but incur excessive LLM calls and token costs, while Graph RAG methods suffer from computationally expensive, error-prone graph construction and retrieval redundancy. To address these challenges, we propose T$^2$RAG…

Generalize across Homophily and Heterophily: Hybrid Spectral Graph Pre-Training and Prompt Tuning
Haitong Luo, Suhang Wang, Weiyao Zhang, Ruiqi Meng, Xuying Meng, Yujun Zhang
https://arxiv.org/abs/2508.11328

Generalize across Homophily and Heterophily: Hybrid Spectral Graph Pre-Training and Prompt Tuning
Graph ``pre-training and prompt-tuning'' aligns downstream tasks with pre-trained objectives to enable efficient knowledge transfer under limited supervision. However, existing methods rely on homophily-based low-frequency knowledge, failing to handle diverse spectral distributions in real-world graphs with varying homophily. Our theoretical analysis reveals a spectral specificity principle: optimal knowledge transfer requires alignment between pre-trained spectral filters and the intrinsic spe…

Dynamically Adaptive Reasoning via LLM-Guided MCTS for Efficient and Context-Aware KGQA
Yingxu Wang, Shiqi Fan, Mengzhu Wang, Siwei Liu
https://arxiv.org/abs/2508.00719 https://…

Dynamically Adaptive Reasoning via LLM-Guided MCTS for Efficient and Context-Aware KGQA
Knowledge Graph Question Answering (KGQA) aims to interpret natural language queries and perform structured reasoning over knowledge graphs by leveraging their relational and semantic structures to retrieve accurate answers. Recent KGQA methods primarily follow either retrieve-then-reason paradigm, relying on GNNs or heuristic rules for static paths extraction, or dynamic path generation strategies that use large language models (LLMs) with prompting to jointly perform retrieval and reasoning. …

Youtu-GraphRAG: Vertically Unified Agents for Graph Retrieval-Augmented Complex Reasoning
Junnan Dong, Siyu An, Yifei Yu, Qian-Wen Zhang, Linhao Luo, Xiao Huang, Yunsheng Wu, Di Yin, Xing Sun
https://arxiv.org/abs/2508.19855

Youtu-GraphRAG: Vertically Unified Agents for Graph Retrieval-Augmented Complex Reasoning
Graph retrieval-augmented generation (GraphRAG) has effectively enhanced large language models in complex reasoning by organizing fragmented knowledge into explicitly structured graphs. Prior efforts have been made to improve either graph construction or graph retrieval in isolation, yielding suboptimal performance, especially when domain shifts occur. In this paper, we propose a vertically unified agentic paradigm, Youtu-GraphRAG, to jointly connect the entire framework as an intricate integra…

DynaSearcher: Dynamic Knowledge Graph Augmented Search Agent via Multi-Reward Reinforcement Learning
Chuzhan Hao, Wenfeng Feng, Yuewei Zhang, Hao Wang
https://arxiv.org/abs/2507.17365

DynaSearcher: Dynamic Knowledge Graph Augmented Search Agent via Multi-Reward Reinforcement Learning
Multi-step agentic retrieval systems based on large language models (LLMs) have demonstrated remarkable performance in complex information search tasks. However, these systems still face significant challenges in practical applications, particularly in generating factually inconsistent intermediate queries and inefficient search trajectories, which can lead to reasoning deviations or redundant computations. To address these issues, we propose DynaSearcher, an innovative search agent enhanced by…

Graph-R1: Towards Agentic GraphRAG Framework via End-to-end Reinforcement Learning
Haoran Luo, Haihong E, Guanting Chen, Qika Lin, Yikai Guo, Fangzhi Xu, Zemin Kuang, Meina Song, Xiaobao Wu, Yifan Zhu, Luu Anh Tuan
https://arxiv.org/abs/2507.21892

Graph-R1: Towards Agentic GraphRAG Framework via End-to-end Reinforcement Learning
Retrieval-Augmented Generation (RAG) mitigates hallucination in LLMs by incorporating external knowledge, but relies on chunk-based retrieval that lacks structural semantics. GraphRAG methods improve RAG by modeling knowledge as entity-relation graphs, but still face challenges in high construction cost, fixed one-time retrieval, and reliance on long-context reasoning and prompt design. To address these challenges, we propose Graph-R1, an agentic GraphRAG framework via end-to-end reinforcement …

Items Proxy Bridging: Enabling Frictionless Critiquing in Knowledge Graph Recommendations
Huanyu Zhang, Xiaoxuan Shen, Yu Lei, Baolin Yi, Jianfang Liu, Yinao xie
https://arxiv.org/abs/2509.26107

Items Proxy Bridging: Enabling Frictionless Critiquing in Knowledge Graph Recommendations
Modern recommender systems place great inclination towards facilitating user experience, as more applications enabling users to critique and then refine recommendations immediately. Considering the real-time requirements, critique-able recommender systems typically straight modify the model parameters and update the recommend list through analyzing the user critiquing keyphrases in the inference phase. Current critiquing methods require first constructing a specially designated model which esta…

Addressing accuracy and hallucination of LLMs in Alzheimer's disease research through knowledge graphs
Tingxuan Xu, Jiarui Feng, Justin Melendez, Kaleigh Roberts, Donghong Cai, Mingfang Zhu, Donald Elbert, Yixin Chen, Randall J. Bateman
https://arxiv.org/abs/2508.21238

Addressing accuracy and hallucination of LLMs in Alzheimer's disease research through knowledge graphs
In the past two years, large language model (LLM)-based chatbots, such as ChatGPT, have revolutionized various domains by enabling diverse task completion and question-answering capabilities. However, their application in scientific research remains constrained by challenges such as hallucinations, limited domain-specific knowledge, and lack of explainability or traceability for the response. Graph-based Retrieval-Augmented Generation (GraphRAG) has emerged as a promising approach to improving …

PGMEL: Policy Gradient-based Generative Adversarial Network for Multimodal Entity Linking
KM Pooja, Cheng Long, Aixin Sun
https://arxiv.org/abs/2510.02726 https://

PGMEL: Policy Gradient-based Generative Adversarial Network for Multimodal Entity Linking
The task of entity linking, which involves associating mentions with their respective entities in a knowledge graph, has received significant attention due to its numerous potential applications. Recently, various multimodal entity linking (MEL) techniques have been proposed, targeted to learn comprehensive embeddings by leveraging both text and vision modalities. The selection of high-quality negative samples can potentially play a crucial role in metric/representation learning. However, to th…

Crosslisted article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[4/7]:
- Knowledge-Graph Based RAG System Evaluation Framework
Sicheng Dong, Vahid Zolfaghari, Nenad Petrovic, Alois Knoll

GeoOutageKG: A Multimodal Geospatiotemporal Knowledge Graph for Multiresolution Power Outage Analysis
Ethan Frakes, Yinghui Wu, Roger H. French, Mengjie Li
https://arxiv.org/abs/2507.22878

GeoOutageKG: A Multimodal Geospatiotemporal Knowledge Graph for Multiresolution Power Outage Analysis
Detecting, analyzing, and predicting power outages is crucial for grid risk assessment and disaster mitigation. Numerous outages occur each year, exacerbated by extreme weather events such as hurricanes. Existing outage data are typically reported at the county level, limiting their spatial resolution and making it difficult to capture localized patterns. However, it offers excellent temporal granularity. In contrast, nighttime light satellite image data provides significantly higher spatial re…

SAGE: Scale-Aware Gradual Evolution for Continual Knowledge Graph Embedding
Yifei Li, Lingling Zhang, Hang Yan, Tianzhe Zhao, Zihan Ma, Muye Huang, Jun Liu
https://arxiv.org/abs/2508.11347

SAGE: Scale-Aware Gradual Evolution for Continual Knowledge Graph Embedding
Traditional knowledge graph (KG) embedding methods aim to represent entities and relations in a low-dimensional space, primarily focusing on static graphs. However, real-world KGs are dynamically evolving with the constant addition of entities, relations and facts. To address such dynamic nature of KGs, several continual knowledge graph embedding (CKGE) methods have been developed to efficiently update KG embeddings to accommodate new facts while maintaining learned knowledge. As KGs grow at di…

KG-EDAS: A Meta-Metric Framework for Evaluating Knowledge Graph Completion Models
Haji Gul, Abul Ghani Naim, Ajaz Ahmad Bhat
https://arxiv.org/abs/2508.15357 https://

KG-EDAS: A Meta-Metric Framework for Evaluating Knowledge Graph Completion Models
Knowledge Graphs (KGs) enable applications in various domains such as semantic search, recommendation systems, and natural language processing. KGs are often incomplete, missing entities and relations, an issue addressed by Knowledge Graph Completion (KGC) methods that predict missing elements. Different evaluation metrics, such as Mean Reciprocal Rank (MRR), Mean Rank (MR), and Hit@k, are commonly used to assess the performance of such KGC models. A major challenge in evaluating KGC models, ho…

Knowledge Graph Completion for Action Prediction on Situational Graphs -- A Case Study on Household Tasks
Mariam Arustashvili, J\"org Deigm\"oller, Heiko Paulheim
https://arxiv.org/abs/2508.13675

Knowledge Graph Completion for Action Prediction on Situational Graphs -- A Case Study on Household Tasks
Knowledge Graphs are used for various purposes, including business applications, biomedical analyses, or digital twins in industry 4.0. In this paper, we investigate knowledge graphs describing household actions, which are beneficial for controlling household robots and analyzing video footage. In the latter case, the information extracted from videos is notoriously incomplete, and completing the knowledge graph for enhancing the situational picture is essential. In this paper, we show that, wh…

A Knowledge Graph and a Tripartite Evaluation Framework Make Retrieval-Augmented Generation Scalable and Transparent
Olalekan K. Akindele, Bhupesh Kumar Mishra, Kenneth Y. Wertheim
https://arxiv.org/abs/2509.19209

A Knowledge Graph and a Tripartite Evaluation Framework Make Retrieval-Augmented Generation Scalable and Transparent
Large Language Models (LLMs) have significantly enhanced conversational Artificial Intelligence(AI) chatbots; however, domain-specific accuracy and the avoidance of factual inconsistencies remain pressing challenges, particularly for large datasets. Designing an effective chatbot with appropriate methods and evaluating its effectiveness is among the challenges in this domain. This study presents a Retrieval Augmented Generation (RAG) chatbot that harnesses a knowledge graph and vector search re…

Query-Centric Graph Retrieval Augmented Generation
Yaxiong Wu, Jianyuan Bo, Yongyue Zhang, Sheng Liang, Yong Liu
https://arxiv.org/abs/2509.21237 https://a…

Query-Centric Graph Retrieval Augmented Generation
Graph-based retrieval-augmented generation (RAG) enriches large language models (LLMs) with external knowledge for long-context understanding and multi-hop reasoning, but existing methods face a granularity dilemma: fine-grained entity-level graphs incur high token costs and lose context, while coarse document-level graphs fail to capture nuanced relations. We introduce QCG-RAG, a query-centric graph RAG framework that enables query-granular indexing and multi-hop chunk retrieval. Our query-cen…

What Breaks Knowledge Graph based RAG? Empirical Insights into Reasoning under Incomplete Knowledge
Dongzhuoran Zhou, Yuqicheng Zhu, Xiaxia Wang, Hongkuan Zhou, Yuan He, Jiaoyan Chen, Evgeny Kharlamov, Steffen Staab
https://arxiv.org/abs/2508.08344

What Breaks Knowledge Graph based RAG? Empirical Insights into Reasoning under Incomplete Knowledge
Knowledge Graph-based Retrieval-Augmented Generation (KG-RAG) is an increasingly explored approach for combining the reasoning capabilities of large language models with the structured evidence of knowledge graphs. However, current evaluation practices fall short: existing benchmarks often include questions that can be directly answered using existing triples in KG, making it unclear whether models perform reasoning or simply retrieve answers directly. Moreover, inconsistent evaluation metrics …

A Query-Aware Multi-Path Knowledge Graph Fusion Approach for Enhancing Retrieval-Augmented Generation in Large Language Models
Qikai Wei, Huansheng Ning, Chunlong Han, Jianguo Ding
https://arxiv.org/abs/2507.16826

A Query-Aware Multi-Path Knowledge Graph Fusion Approach for Enhancing Retrieval-Augmented Generation in Large Language Models
Retrieval Augmented Generation (RAG) has gradually emerged as a promising paradigm for enhancing the accuracy and factual consistency of content generated by large language models (LLMs). However, existing RAG studies primarily focus on retrieving isolated segments using similarity-based matching methods, while overlooking the intrinsic connections between them. This limitation hampers performance in RAG tasks. To address this, we propose QMKGF, a Query-Aware Multi-Path Knowledge Graph Fusion A…

LeanRAG: Knowledge-Graph-Based Generation with Semantic Aggregation and Hierarchical Retrieval
Yaoze Zhang, Rong Wu, Pinlong Cai, Xiaoman Wang, Guohang Yan, Song Mao, Ding Wang, Botian Shi
https://arxiv.org/abs/2508.10391

LeanRAG: Knowledge-Graph-Based Generation with Semantic Aggregation and Hierarchical Retrieval
Retrieval-Augmented Generation (RAG) plays a crucial role in grounding Large Language Models by leveraging external knowledge, whereas the effectiveness is often compromised by the retrieval of contextually flawed or incomplete information. To address this, knowledge graph-based RAG methods have evolved towards hierarchical structures, organizing knowledge into multi-level summaries. However, these approaches still suffer from two critical, unaddressed challenges: high-level conceptual summarie…

Bottom-up Domain-specific Superintelligence: A Reliable Knowledge Graph is What We Need
Bhishma Dedhia, Yuval Kansal, Niraj K. Jha
https://arxiv.org/abs/2507.13966

Bottom-up Domain-specific Superintelligence: A Reliable Knowledge Graph is What We Need
Language models traditionally used for cross-domain generalization have recently demonstrated task-specific reasoning. However, their top-down training approach on general corpora is insufficient for acquiring abstractions needed for deep domain expertise. This may require a bottom-up approach that acquires expertise by learning to compose simple domain concepts into more complex ones. A knowledge graph (KG) provides this compositional structure, where domain primitives are represented as head-…

ReviewGraph: A Knowledge Graph Embedding Based Framework for Review Rating Prediction with Sentiment Features
A. J. W. de Vink, Natalia Amat-Lefort, Lifeng Han
https://arxiv.org/abs/2508.13953

ReviewGraph: A Knowledge Graph Embedding Based Framework for Review Rating Prediction with Sentiment Features
In the hospitality industry, understanding the factors that drive customer review ratings is critical for improving guest satisfaction and business performance. This work proposes ReviewGraph for Review Rating Prediction (RRP), a novel framework that transforms textual customer reviews into knowledge graphs by extracting (subject, predicate, object) triples and associating sentiment scores. Using graph embeddings (Node2Vec) and sentiment features, the framework predicts review rating scores thr…

KG-Attention: Knowledge Graph-Guided Attention at Test-Time via Bidirectional Information Aggregation
Songlin Zhai, Guilin Qi, Yuan Meng
https://arxiv.org/abs/2507.08704

KG-Attention: Knowledge Graph-Guided Attention at Test-Time via Bidirectional Information Aggregation
Knowledge graphs (KGs) play a critical role in enhancing large language models (LLMs) by introducing structured and grounded knowledge into the learning process. However, most existing KG-enhanced approaches rely on parameter-intensive fine-tuning, which risks catastrophic forgetting and degrades the pretrained model's generalization. Moreover, they exhibit limited adaptability to real-time knowledge updates due to their static integration frameworks. To address these issues, we introduce the f…

Fusing Knowledge and Language: A Comparative Study of Knowledge Graph-Based Question Answering with LLMs
Vaibhav Chaudhary, Neha Soni, Narotam Singh, Amita Kapoor
https://arxiv.org/abs/2509.09272

Fusing Knowledge and Language: A Comparative Study of Knowledge Graph-Based Question Answering with LLMs
Knowledge graphs, a powerful tool for structuring information through relational triplets, have recently become the new front-runner in enhancing question-answering systems. While traditional Retrieval Augmented Generation (RAG) approaches are proficient in fact-based and local context-based extraction from concise texts, they encounter limitations when addressing the thematic and holistic understanding of complex, extensive texts, requiring a deeper analysis of both text and context. This pape…