Frontier large language models (LLMs) have demonstrated remarkable performance across various knowledge-intensive enterprise tasks. However, these models are primarily trained on unstructured, general knowledge, which limits their effectiveness in domain-specific applications-particularly when tasks involve structured data sources or sensitive enterprise information. We propose the first Structured Knowledge for Large Language Models Workshop - SKnow-LLM, which aims to bridge this gap by promoting research on innovative methodologies and practical applications in this area. Through keynote talks, panel discussions and paper presentations, the workshop will foster in-depth discussions on recent advances, identify existing challenges, and explore promising directions for integrating structured knowledge into LLMs.
论文检索
输入标题、作者或关键词，从 844 篇学术成果中精准定位
In today's competitive and fast-evolving business environment, it is a critical time for organizations to rethink how to deal with talent and management-related tasks in a quantitative manner. Indeed, thanks to the era of big data, the availability of large-scale talent data provides unparalleled opportunities for leaders to deliver intelligence for effective management for organizations. In the past few years, talent and management computing have increasingly attracted attention from KDD communities, and a number of research/applied data science efforts have been devoted. To this end, the purpose of this workshop, i.e., the 6th International Workshop on Talent and Management Computing (TMC 2025), is to bring together researchers and practitioners to discuss critical problems faced by talent and management-related domains and potential data-driven solutions.
Uncertainty reasoning and quantification play a critical role in decision making across various domains, prompting increased attention from both academia and industry. As real-world applications become more complex and data-driven, effectively handling uncertainty becomes paramount for accurate and reliable decision making. This workshop focuses on the critical topics of uncertainty reasoning and quantification in decision making. It provides a platform for experts and researchers from diverse backgrounds to exchange ideas on cutting-edge techniques and challenges in this field. The interdisciplinary nature of uncertainty reasoning and quantification, spanning artificial intelligence, machine learning, statistics, risk analysis, and decision science, will be explored. The workshop aims to address the need for robust and interpretable methods for modeling and quantifying uncertainty, fostering reasoning decision-making in various domains. Participants will have the opportunity to share research findings and practical experiences, promoting collaboration and advancing decision-making practices under uncertainty.
Nowadays, while most machine learning research on customer journey optimization has focused on short-term success metrics such as click-through rates and optimal ad placement, there has been little consideration given to developing a coherent system for end-to-end customer journey optimization. Such a system would encompass all aspects of the customer experience, from presenting the right product value to the right users, to understanding a user's likelihood of conversion and long-term value to the platform, as well as their propensity for cross-selling and risk of churning. Currently, models and algorithms for customer journey optimization are often developed in isolation, leading to inefficiencies in modeling and data pipelines. Furthermore, the customer is often viewed as a collection of different entities by different organizational departments (such as marketing, sales, and finance), which can lead to additional friction in the customer experience. This workshop seeks to bridge the gap between academic researchers and industrial practitioners who are interested in building holistic solutions for end-to-end customer journey optimization. In addition, with the rising popularity of generative AI and LLM, we want to use this venue to exchange ideas regarding their applications in different stages of customer journey, and how the new technologies could help businesses achieve their objectives.
As computers increasingly make decisions about who gets a loan, a job, or even bail, the expansion of AI algorithms has provoked public concern about ethical issues, and the need to understand what constitutes AI algorithms and how they make decisions becomes ever more pressing. For example, an increasing number of high-profile news reports that widely-used algorithms have unfairly discriminated against some groups of people (e.g., by gender and race) in parole decisions and other major life events. Focusing more attention on ethical bias in learning algorithms is key to unlocking the potential of automated decision systems while ensuring fairness and accountability so that everyone can advance equally in society. Ethical AI has become increasingly important and it has been attracting attention from academia and industry, due to its increased popularity in real-world applications with fairness concerns. It also places fundamental importance on ethical considerations in determining legitimate and illegitimate uses of AI. Organizations that apply ethical AI have clearly stated well-defined review processes to ensure adherence to legal guidelines. Therefore, the wave of research at the intersection of ethical AI in data mining and machine learning has also influenced other fields of science, including computer vision, natural language processing, reinforcement learning, and social science.
RelKD 2025: The Third International Workshop on Resource-Efficient Learning for Knowledge Discovery
PDF ↗Modern machine learning techniques, particularly deep learning, have showcased remarkable efficacy across numerous knowledge discovery and data mining applications. However, the advancement of these methods is frequently impeded by resource constraint challenges in many scenarios, such as limited labeled data (data-level), small model size requirements in real-world computing platforms (model-level), and efficient mapping of the computations to heterogeneous target hardware (system-level). Addressing all these factors is crucial for effectively and efficiently deploying developed models across a broad spectrum of real-world systems, including large-scale social network analysis, recommendation systems, and real-time anomaly detection. Therefore, there is a critical need to develop efficient learning techniques to address the challenges posed by resource limitations, whether from data, model/algorithm, or system/hardware perspectives. The proposed third international workshop on ''Resource-Efficient Learning for Knowledge Discovery (RelKD 2025)'' will provide a great venue for academic researchers and industrial practitioners to share challenges, solutions, and future opportunities for resource-efficient learning.
FedKDD 2025: The 2025 International Joint Workshop on Federated Learning for Data Mining and Graph Analytics
PDF ↗Deep Learning has facilitated various high-stakes applications such as crime detection, urban planning, drug discovery, and healthcare. Its continuous success hinges on learning from massive data in miscellaneous sources, ranging from data with independent distributions to graph-structured data capturing intricate inter-sample relationships. Scaling up the data access requires global collaboration from distributed data owners. Yet, centralizing all data sources to an untrustworthy centralized server will put users' data at risk of privacy leakage or regulation violation. Federated Learning (FL) is a de facto decentralized learning framework that enables knowledge aggregation from distributed users without exposing private data. Though promising advances are witnessed for FL, new challenges are emerging when integrating FL with the rising needs and opportunities in data mining, graph analytics, foundation models, generative AI, and new interdisciplinary applications in science. By hosting this workshop, we aim to attract a broad range of audiences, including researchers and practitioners from academia and industry interested in the emergent challenges in FL. As an effort to advance the fundamental development of FL, this workshop will encourage ideas exchange on the trustworthiness, scalability, and robustness of distributed data mining and graph analytics and their emergent challenges.
The goal of the 24th International Workshop on Data Mining in Bioinformatics (BIOKDD 2025) is to encourage KDD researchers to solve the numerous problems and challenges in Bioinformatics using Data Mining technologies. Based on the organizers' expertise and communities, BIOKDD 2025 features the theme ''Generative AI in Biomolecular Designs''. This theme encourages using large language models to design and optimize proteins or other biomolecules such as compounds, natural products, and RNAs with generative AI techniques. We also welcome broader research applying data mining to address biomedical problems. The key goal is to accelerate the convergence between Data Mining and Bioinformatics communities to expedite discoveries in basic biology, medicine and healthcare.
The demand for efficient Large Language Model (LLM) inference has surged with the rising adoption of Generative AI (GenAI) applications, particularly in areas such as agents and retrieval-augmented generation. Efficient inference serves two crucial purposes: it enables the deployment of LLM-centered applications that address critical business needs, while also facilitating rapid experimentation for researchers to extract valuable insights and new understandings. However, despite the field's rapid advancement and interdisciplinary nature, there remains a limited exchange of ideas and methodologies between production-facing practitioners and researchers seeking to experiment with new GenAI concepts quickly. To bridge this gap, we are introducing the first KDD workshop on Inference Optimization for Generative AI. Our goal is to create a collaborative platform where researchers and practitioners working across various use cases and stacks of efficient inference can come together to exchange research ideas, establish connections between different disciplines, and identify challenges and research questions that will shape future work.
Large language models (LLMs) have revolutionized natural language processing, enabling unprecedented capabilities in reasoning, planning, and tool utilization. Enterprises are increasingly adopting LLM-powered agents to automate complex workflows, from meeting summarization (e.g., Microsoft Copilot) to supply chain optimization and customer service orchestration. However, deploying agentic AI systems in enterprise settings introduces unique challenges, including decision making under uncertainty, multi-agent collaboration, security vulnerabilities, and trust gaps in mission-critical applications. This workshop aims to bridge the gap between academia and industry to explore LLM-driven agentic systems tailored for enterprise needs. We focus on three pillars: 1) emerging architectures that enable dynamic task decomposition and tool invocation; 2) domain-specific applications such as case studies in supply chain and employee productivity domain; 3) evaluation and governance such as the AAEF (Agentic Application Evaluation Framework) and security strategies.
The field of information retrieval has significantly transformed with the integration of AI technologies. AI agents, especially those leveraging LLMs and vast computational power, have revolutionized in- formation retrieval, processing, and presentation. LLM agents, with advanced memory, reasoning, and planning capabilities, can perform complex tasks, engage in coherent conversations, and provide personalized responses. Despite these advancements, challenges such as ensuring relevance and accuracy, mitigating biases, providing real-time responses, and maintaining data security remain. This workshop aims to explore these challenges, share innovative solutions, and discuss future directions. It will provide a platform to bring together researchers and practitioners to discuss the latest theoretical advancements and practical implementations of AI agents in information retrieval. Topics include AI in search, recommendation, and personalization systems. By gathering a diverse group of experts, the workshop seeks to deepen the understanding of AI agents in information retrieval, advance the field, and enhance its societal impact. Participants will gain insights into cutting-edge research and emerging trends, and foster knowledge exchange and collaboration within the community.
Graphs, which encode pairwise relations between entities, serve as a fundamental data structure across real-world domains. Many critical applications can be formulated as graph-based tasks, and graph machine learning (GML), from the shallow embedding models to graph neural networks and further advanced to the most powerful graph transformers, has been well-established to automate knowledge discovery and decision-making on graphs. In parallel, the recent emergence of large foundational models has driven machine learning into a new era of Generative Artificial Intelligence (Gen-AI), and this revolution presents both unprecedented opportunities and profound challenges for the well-established GML paradigms. However, few investigations have analyzed and envisioned how GML should evolve to harness these opportunities, address these challenges, and embrace this new Gen-AI era. To fill in this gap, we organize the first international Workshop on Machine Learning on Graphs in the Era of Generative Artificial Intelligence (MLoG-GenAI), held in connection with the 31st ACM Conference on Knowledge Discovery and Data Mining, which provides a venue to gather academic researchers and industry practitioners to discuss and picture the development of GML in the new Gen-AI era.
Building personalized recommender systems is a cornerstone of the modern data mining and applied machine learning (ML) community. Modern online platforms have a confluence of data including user-item interaction graphs, user and item-associated semantics (text, visual content, etc.), and metadata. Recent advancements in generative models and semantic encoders via large language models (LLMs), visual and audio encoders have significantly impacted research in relevant domains, enabling new directions in knowledge discovery and ability of models to better incorporate semantic context. This workshop bridges the research gap between the use of generative models and recommendation for personalized systems. We will focus on topics spanning the interplay between such models and conventional personalized systems.
Time series data is now pervasive across domains such as healthcare, finance, entertainment, and transportation, driven by advances in sensing technologies that enable continuous data collection. The resulting increase in data volume and complexity poses significant challenges to traditional analysis methods, calling for the development of advanced, interdisciplinary approaches to temporal data mining. This workshop aims to: (1) identify key challenges in learning from time series data, including irregular sampling, spatiotemporal dependencies, and uncertainty quantification; (2) explore recent advances in algorithmic, statistical, theoretical, and systems-based solutions-ranging from classical methods to emerging techniques involving large language models (LLMs); and (3) foster collaboration by highlighting open problems and novel research directions in time series analysis. Bridging theory and practice, the workshop provides a platform for researchers and practitioners from academia, industry, and government to exchange ideas, discuss technical challenges, and showcase practical applications. Contributions from related areas such as AI, machine learning, data science, and statistics are strongly encouraged.
The 3rd Workshop on Causal Inference and Machine Learning in Practice at KDD 2025 aims to bring together researchers, industry professionals, and practitioners to explore the application of causal inference within machine learning models. As causal machine learning techniques gain traction across industries, practical challenges related to trustworthiness, robustness, and fairness remain at the forefront. This workshop will provide a forum to discuss methodologies for evaluating causal models in real-world scenarios and explore innovative applications that integrate causal inference with generative AI (GenAI) and large language models (LLMs). Topics of interest include using GenAI and LLMs to facilitate causal inference tasks and leveraging causal inference techniques for evaluating and improving GenAI/LLM models. Building on the success of the previous workshop editions at KDD 2023 and KDD 2024, which attracted over 200 and 250 participants, respectively, this workshop will continue fostering collaboration between academia and industry. Through invited talks, contributed papers, and interactive discussions, we will address key challenges and opportunities at the intersection of causal inference and machine learning. As the field continues to evolve, this workshop serves as a crucial platform for knowledge exchange and innovation, driving forward the application of causal techniques in machine learning and AI.
The financial industry leverages machine learning in more ways than just finding the right alpha signal. It grapples with supply chains, business processes, marketing, churn, fraud, and money laundering, all while maintaining compliance with the various regulatory frameworks it is beholden to. Due to the sheer volume of wealth being handled by the financial industry and its critical role in everyday life, it has been a lucrative target for a wide spectrum of ever-evolving bad actors. With each successive iteration of this workshop, we have attempted to capture the breadth of these actors - fraudsters, money launderers, market manipulators, and potentially nation-state-level risks. The emerging advances in Generative AI make this a particularly exciting time to host this workshop. GenAI offers groundbreaking approaches to handling the various data types prevalent in the financial sector. From a security point of view, bad actors are actively using Generative AI creatively to thwart conventional defenses (e.g. voice cloning, better synthetic identities), and this workshop's audience would benefit from commonly applicable defenses & best practices against such threats. Last but not the least, there is now an increasing willingness from the financial industry towards deeper engagement and data sharing with academia.
The rapid deployment of Generative and Agentic AI systems-ranging from large language models to autonomous agents-has created a critical need for rigorous and trustworthy evaluation methodologies. As these models influence real-world decision-making, traditional performance metrics alone fall short in capturing issues of safety, ethical alignment, misinformation, and human-centered usability. This workshop addresses these challenges by fostering interdisciplinary discussions and innovations in evaluation strategies that go beyond conventional benchmarks. Topics include holistic and multi-perspective assessments, scalable evaluation pipelines, reasoning and goal alignment in agentic behavior, misinformation detection, cross-modal generation, and trust calibration. By advancing robust, user-centric, and societally grounded evaluation practices, this workshop contributes to expanding KDD's methodological frontier into the emerging domain of responsible AI systems.
The swift advancement of urbanization has resulted in the growth of numerous large cities, which have enhanced the lives of many individuals but have also created significant challenges, such as air pollution, higher energy consumption, and traffic congestion. Addressing these issues was nearly unfeasible in the past due to the intricate and ever-changing nature of urban environments. Today, however, advancements in sensing technologies and extensive computing infrastructures have generated vast amounts of big data related to urban areas, including information on human mobility, air quality, traffic patterns, and geographic data. Inspired by the potential for creating smarter cities, we developed a vision for urban computing that seeks to harness insights from diverse and extensive data collected in urban settings, using this valuable information to tackle the critical problems our cities currently encounter.
The proposed ''SciSoc LLM Workshop: Large Language Models for Scientific and Societal Advances'' aims to explore the profound implications and potential of Large Language Models (LLMs) in driving forward scientific inquiry and addressing critical societal challenges. As LLMs such as GPT-4 continue to redefine boundaries in both complexity and capability, their integration into the scientific and societal domains is not just beneficial but essential. In particular, LLMs have demonstrated substantial value in improving our understanding of complex datasets and generating insights across various fields such as healthcare, environmental science, education, and public policy. By bringing together experts and enthusiasts from diverse fields, the workshop aims to foster a comprehensive understanding of how LLMs can redefine traditional research methodologies. Participants will explore innovative ways to harness the power of LLMs for greater efficiency and innovation in their respective fields, potentially catalyzing a new era of scientific and societal advancement.
The Temporal Graph Learning (TGL) workshop, now in its third edition at KDD 2025, offers an interdisciplinary platform for researchers to explore the evolving applications of temporal networks in various domains, including recommender systems, social network analysis, traffic analytics, and epidemiological data analysis. The workshop aims to facilitate the exchange of ideas across disciplines, highlight successes and challenges in TGL, and outline future research directions. The workshop welcomes diverse contributions, offers keynote talks from academic and industry experts, and is complemented by a panel discussion on emerging aspects of TGL.