1 views collected around this technical thread.
This article details Ant Group's research on integrating large language models into recommendation pipelines, covering background challenges, knowledge extraction, teacher‑student distillation, experimental results, and practical Q&A for improving bias, efficiency, and cold‑start performance.
This article reviews the rapid progress of large language models and their complementary relationship with knowledge graphs, covering comparative strengths, knowledge extraction and completion, evaluation benchmarks, deployment benefits, complex reasoning support, and prospects for interactive fusion toward more reliable and explainable AI systems.
OneKE, an open‑source bilingual (Chinese‑English) knowledge extraction framework jointly developed by Ant Group and Zhejiang University, enables efficient extraction of entities, relations, and events to build domain knowledge graphs that enhance large language models’ reasoning, reduce hallucinations, and support applications in medical, financial, and governmental sectors.
This article introduces Kuaipedia, Kuaishou's multimodal short‑video encyclopedia, detailing its background, system architecture, knowledge‑video recognition pipeline, multimodal entity linking techniques, and downstream applications, while also providing implementation insights and a brief Q&A.
This article describes a practical approach to automatically extract and visualize business system knowledge from code by abstracting workflow control logic into a generalized design pattern using the FSM‑X state‑machine framework, discussing its background, methodology, implementation details, and current limitations.
DeepKE-LLM is an open‑source, extensible knowledge‑graph extraction framework that leverages large language models for entity, relation, and attribute extraction, supports multiple LLMs, provides installation scripts, various usage modes, fine‑tuning pipelines, and integrates with the KnowLM project for advanced instruction‑following capabilities.
This article examines the debate over whether large language models truly possess intelligence, arguing that next‑token prediction functions as a form of lossless data compression whose efficiency reflects intelligence, and it surveys research on knowledge extraction, neuron semantics, circuit competition, scaling effects, and the broader philosophical implications of GPT as a mirror of the world’s parameters.
This report presents AliMe MKG, a multimodal knowledge graph designed for live e‑commerce, detailing its business background, construction and application, the three types of multimodal knowledge (triples, sentences, and visual media), the underlying extraction techniques, and its deployment in digital‑human anchors and intelligent live‑room assistants.
The DataFunCon Knowledge Graph Forum on December 18 gathers leading experts from academia and industry to discuss large‑scale knowledge graph construction, storage, applications, and challenges, offering attendees insights into cutting‑edge AI techniques, graph databases, and practical deployment strategies across multiple domains.
This article explores the challenges of enterprise knowledge management and presents a comprehensive OpenIE-based approach for building vertical domain knowledge graphs, covering data extraction, SPO triple generation, case studies, and applications such as chatbots, semantic search, and intelligent QA.
The article surveys the evolution and core components of knowledge graphs—from early Linked Data concepts to modern semantic networks—detailing the end‑to‑end pipeline of data acquisition, cleaning, extraction, and fusion, and showcases Tencent Cloud’s Merak framework and encyclopedia KG, highlighting model choices, performance benchmarks, and real‑world applications such as recommendation and intelligent Q&A.
This article presents a comprehensive overview of financial knowledge graphs, covering their historical evolution, theoretical foundations, technical stack, implementation steps, and real‑world case studies in banking, regulatory technology, and securities, while highlighting community resources for AI and big‑data practitioners.