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 report details the design, construction, and deployment of AliMe MKG, a multimodal knowledge graph that powers digital‑human anchors and smart assistants in live‑stream e‑commerce by integrating triple‑level, sentence‑level, and visual knowledge through advanced AI techniques such as image‑text matching, video grounding, multimodal NER, and entity linking.
The article presents Ctrip's travel AI team's end‑to‑end entity linking solution built on a large‑scale tourism knowledge graph, detailing its background, technical architecture, core modules—including mention detection, candidate generation, and disambiguation using BERT and prefix‑tree techniques—and real‑world applications such as search, intelligent客服, and POI data maintenance.
This article provides a comprehensive overview of entity linking, detailing its definition, the two-stage pipeline of entity recognition and disambiguation, common methodologies such as candidate generation and ranking, advanced approaches, challenges like unlinkable mentions, and various applications in knowledge graphs, text mining, and question answering.
This article reviews the fundamentals of knowledge graphs, their practical uses in question answering, search and recommendation, and surveys recent research on entity linking—including dual‑encoder retrieval, BERT‑based models, multilingual approaches, and zero‑shot methods—while also outlining modern knowledge‑graph construction pipelines and open challenges.
This article provides a comprehensive overview of knowledge graphs and entity linking, covering their definitions, practical uses in question answering, search and recommendation, the standard pipeline of mention detection, candidate generation and scoring, challenges such as scalability and multilinguality, and recent research advances including dual‑encoder, RELIC, deep retrieval, and multilingual BERT‑based models, followed by a discussion of modern knowledge‑graph construction methods.
Meituan’s To‑Store Integrated data team built an end‑to‑end supply‑standardization pipeline for the rapidly growing script‑murder market by extending the GENE knowledge graph to mine merchant supply, construct a unified script library through rule‑based, semantic, and multimodal clustering, and link products and user‑generated content to standardized scripts, enabling a dedicated category, personalized recommendations, filter tags, and improved ranking.
This article presents an overview of Xiaomi's knowledge graph platform, introduces text‑augmented knowledge representation learning methods such as Jointly and DKRL, and details their practical applications in entity linking, entity recommendation, and knowledge graph completion within AI‑driven services.
The paper describes Meituan’s retail product knowledge graph—a multi‑layered, multi‑modal system that structures billions of SKUs, attributes, and user insights using hierarchical categories, graph‑enhanced NER, semi‑supervised learning, and expert‑in‑the‑loop validation, enabling precise search, ranking, recommendation, and real‑time merchant optimization.
This article introduces Xiaomi's knowledge graph architecture, presents text‑graph joint representation learning methods, and demonstrates their practical use in entity linking, entity recommendation, and knowledge completion, highlighting model designs, feature engineering, and experimental results.
The paper describes Meituan’s common‑sense concept knowledge graph, detailing a multi‑stage construction pipeline—concept, hierarchy, attribute, bridging, and POI/SPU linking—using BERT, XGBoost, and graph neural networks, and demonstrates its deployment in category‑word enrichment, search suggestions, and medical‑beauty tagging, achieving over two million concepts, three million relations, and roughly 90 % accuracy.
This article presents a comprehensive approach to Chinese short‑text entity linking, describing the Qianyan dataset, pipeline and end‑to‑end task formulations, sample construction, a multitask model that jointly performs entity ranking and NIL classification, various optimization techniques including confidence learning and adversarial training, and detailed experimental analysis showing state‑of‑the‑art performance.
The article presents an in‑depth overview of Xiaomi's knowledge graph, describing its evolution, core technologies such as entity linking, knowledge fusion and concept mining, and illustrating how it powers diverse AI‑driven services like smart Q&A, virtual assistants, e‑commerce and financial applications.
This article provides a detailed introduction to knowledge graphs, covering their definition, why they are needed, the four basic triple types, construction pipelines (including data sources, crowdsourced vs automated methods, and schema versus data layers), storage and query techniques using graph and relational databases, and their practical applications such as enhancing precision, diversity, and explainability in recommendation systems through models like DKN, RippleNet, and graph neural networks.
Meituan Brain builds a massive multi‑modal knowledge graph of billions of entities and triples across food, entertainment, and travel, using advanced extraction, validation, fusion, and reasoning techniques to empower search, recommendation, merchant tools, and fraud detection while addressing scalability and schema‑evolution challenges.
