Baidu's ERNIE 3.0 introduces a 100‑billion‑parameter, knowledge‑graph‑augmented language model that breaks 54 Chinese NLP benchmarks, achieves human‑level performance on SuperGLUE, and demonstrates strong generation and zero‑shot capabilities, now available for public demo and research.
This article reviews how knowledge graphs can be integrated into recommender systems to address data sparsity and cold‑start problems, covering collaborative filtering limitations, KG embeddings (TransE, TransH, TransR), deep knowledge‑aware networks, multi‑task feature learning, RippleNet, KGCN, experimental results, and a comparative analysis of performance, scalability, and interpretability.
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 systematically introduces the architecture, iterative improvements, modeling techniques, and practical applications of Meituan's food knowledge graph, covering category taxonomy, standard dish names, basic and thematic attributes, health‑meal detection, dish entity alignment, and downstream recommendation and search use cases.
This article explains why the financial asset‑management industry needs a knowledge‑graph system, describes the AI‑driven intelligent investment‑research workflow, and details the cloud‑native big‑data ingestion platform, core graph‑construction technologies such as tag‑embedding, FinBERT‑based matching, extraction, entity linking, and knowledge fusion, culminating in a practical intelligent research assistant.
The Meituan Takeaway Food Knowledge Graph iteratively builds a hierarchical tag taxonomy, standardizes dish names, extracts basic and theme attributes, aligns online‑offline entities using CNN‑CRF, BERT and hybrid models, and powers combo, interactive and search recommendations while planning scene‑specific tags and graph‑based personalization.
Meituan Waimai tackles long ordering decisions and low merchant combo creation by deploying an offline‑real‑time hybrid system that generates high‑quality meal combos using graph‑label induction, encoder‑decoder, and attention models, reinforced with quality classification and constraint pruning, boosting combo coverage and user experience.
The paper outlines an end‑to‑end pipeline that standardizes highly personalized food‑delivery dish names by combining rule‑based and BERT‑DSSM text synonym detection with EfficientNet image classification, constructing a multi‑level taxonomy that improves aggregation, supply‑demand analysis, recall ranking and merchant tagging.
This document outlines a comprehensive knowledge graph curriculum covering fundamentals, representation methods, storage solutions, extraction techniques, reasoning, fusion, question answering, graph algorithms, and emerging research directions across nine detailed chapters.
This teaching plan outlines a comprehensive Knowledge Graph course covering fundamentals, representation, storage, extraction, reasoning, fusion, question answering, graph algorithms, and emerging technologies across nine detailed chapters, including language integration, ontology matching, and multimodal extensions.
This presentation details Beike Real Estate's development of entity and reasoning knowledge graphs, their integration into intelligent training, the methods for knowledge acquisition, organization, and operation, and future directions for industry-wide knowledge standards, illustrating how AI-driven data pipelines enhance real‑estate services.
Alibaba Entertainment’s AI‑driven workflow combines perception, cognition, and decision layers—leveraging knowledge graphs, few‑shot learning, and real‑time sentiment analysis across casting, production, and distribution—to predict content demand with sparse data, boosting prediction accuracy by 10%, quality by 20% and cutting low‑traffic titles.
The CCKS 2021 Resource Track invites submissions of open knowledge‑graph and semantic‑computing resources—including datasets, tools, ontologies, benchmarks, workflows, and crowdsourcing designs—by May 20, with detailed guidelines, evaluation criteria, and a best‑resource award announced at the August conference in Guangzhou.
This presentation by Baidu senior R&D engineer Wang Quan outlines the evolution, architecture, and recent breakthroughs of Baidu's knowledge graph, covering universal, event, video, and industry-specific graphs, key technologies such as open knowledge mining, self‑learning, multi‑source fusion, and their applications in search, recommendation, dialogue, and intelligent services.
In an InfoQ interview, JD Technology senior algorithm scientist Wu Youzheng reviews the rapid advances of natural language processing in 2020—including GPT‑3, multimodal dialogue, knowledge‑enhanced pre‑training, and knowledge graphs—while outlining the most promising research directions and practical challenges for the coming year.
The article explains how EpiK uses blockchain‑based storage, incentive mechanisms, and DAO governance to build a decentralized, open‑source knowledge‑graph platform that addresses AI data needs, coordination challenges, and encourages participation from experts, miners, and bounty hunters.
This article explores the challenges of enterprise knowledge management and presents vertical‑domain knowledge graph construction techniques based on OpenIE, covering data analysis, extraction methods, SPO modeling, both closed‑ and open‑domain approaches, and practical applications such as chatbots, search and intelligent QA.
This article surveys recent research on extracting and expanding hot topics from short texts by constructing user‑behavior graphs, applying graph‑embedding techniques, and leveraging multi‑task learning to improve recommendation relevance, timeliness, and cold‑start handling in large‑scale platforms.
This article introduces the typical applications of intelligent question answering, compares chat‑type, knowledge‑type and task‑type bots, and then details the end‑to‑end architecture, knowledge‑base construction, semantic‑equivalence modeling with BERT‑BIMPM, knowledge‑graph QA pipelines, and NL2SQL techniques, concluding with practical deployment insights.
This article reviews the rapid progress of knowledge graph completion, covering its background, formal problem definition, major technical approaches—including representation learning, path‑based search, reinforcement learning, logical reasoning, and meta‑learning—while discussing their challenges, recent improvements, and promising future research directions.
This article explains how financial institutions can leverage large amounts of structured and unstructured data to build and apply financial knowledge graphs, covering AI key technologies, schema design, data extraction, graph construction, storage solutions, and real-world use cases such as intelligent tagging, recommendation, policy analysis, and executive relationship mining.
This article outlines five key big‑data trends for 2021—including the rise of augmented analytics, the convergence of big data with blockchain, the growing importance of knowledge graphs, data‑driven health innovations, and climate‑focused analytics—highlighting their impact on organizations and future technological landscapes.
This article describes Meituan's intelligent question answering system, detailing its three core capabilities—Document QA, Community QA, and Knowledge‑Graph QA—along with the underlying machine‑reading comprehension models, multi‑task learning, answer ranking, and real‑world deployment scenarios across travel, hotel, and retail services.
This article explains how financial institutions can leverage massive structured and unstructured data by building a financial knowledge graph, detailing AI core technologies, schema design, extraction methods, storage solutions, and a range of practical applications such as intelligent tagging, recommendation, policy analysis, and executive relationship mining.
The DataFunTalk Year-End Knowledge Graph Forum on December 19, 2023, will be streamed live and feature four expert speakers from Baidu, Alibaba, Meituan, and Beike who will share cutting‑edge knowledge‑graph technologies, applications, and practical techniques for industry and research audiences.
Meituan’s search and NLP team announced that six knowledge‑graph papers—covering query‑aware tip generation, BERT‑based ranking, multi‑modal and sequential recommendation, conversational recommendation, and graph‑embedding for personalized product search—were accepted at CIKM 2020, resulting from university collaborations and already deployed to boost Meituan’s search, recommendation and product‑search services.
At ICDM 2020, the JD Digits Silicon Valley team achieved top results in the Knowledge Graph Contest by fine‑tuning BERT and introducing a novel sequence‑labeling framework that jointly extracts consumer behavior types and their underlying reasons, leveraging CRF decoding and model ensemble for superior performance.
The DataFunTalk year‑end conference will be held online on December 19‑20, featuring over 90 speakers across multiple forums covering recommendation algorithms, knowledge graphs, AI, big data, security, and product development, with detailed session schedules, speaker bios, and registration information.
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.
This article presents Baidu's large‑scale event graph technology, detailing its concepts, construction pipeline, detection, representation, extraction methods, causal relation mining, and real‑world applications such as hotspot discovery, event tracing, resource linking, and causal reasoning, while outlining future research directions.
This presentation explains how knowledge graphs serve as the foundation for new‑infrastructure initiatives, detailing the evolution of AI from perception to cognition, the role of big‑data centers, DIKW modeling, intelligent data governance, and the construction of a cognitive AI middle‑platform for industry applications.
The article presents a comprehensive overview of NetEase Cloud Music's recommendation system, detailing its rapid user growth, diverse music‑recommendation scenarios, differences from e‑commerce recommendation, and the evolution of recall and ranking models that leverage real‑time interest vectors, dynamic multi‑interest modeling, knowledge graphs, long‑short‑term interest mining, and multi‑path fusion to deliver personalized music experiences.
The paper introduces a symbolic‑logic‑focused evaluation set for knowledge‑base question answering, defining single, deterministic, and non‑deterministic element symbols with linguistic tags, constructing complex celebrity‑domain queries, and showing that current commercial KBQA systems achieve only modest accuracy, highlighting the need for more robust reasoning capabilities.
This article explains how knowledge graphs can power new‑infrastructure initiatives by enabling deep semantic data governance, supporting large‑scale AI platforms, and driving the transition from perception‑level AI to cognitive intelligence across industries.
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 surveys knowledge‑graph reasoning, explaining deductive and inductive reasoning fundamentals, description‑logic and logic‑programming approaches, and modern embedding techniques such as TransE, TransH, TransR and TransD, while highlighting their theoretical bases, practical implementations and recent research progress.
This article explores the evolution of e‑commerce search, detailing why search matters, the technical pipeline—including query preprocessing, entity and intent recognition, knowledge‑graph construction, recall, coarse and fine ranking—and demonstrates substantial performance gains through real‑world case studies.
JD’s recommendation team outlines three key innovations—knowledge‑graph‑based interest recall, enhanced CTR estimation with a DRM module, and a listwise ranking strategy—that together address user‑interest expansion challenges in e‑commerce, especially for cold‑start items, long‑tail products, and dynamic promotional scenarios.
This article reviews the challenges of short‑text query understanding in medical search and surveys a range of term‑weighting methods—including statistical models, supervised weighting, knowledge‑graph‑enhanced extraction, and neural network‑based approaches—highlighting their assumptions, implementations, and practical considerations for improving retrieval relevance.
The paper introduces QDGAT, a question‑directed graph attention network that enhances numerical reasoning in reading comprehension by explicitly modeling relationships among numbers, entities, and questions, and demonstrates its effectiveness through extensive experiments on the DROP dataset.
The article presents AliCoCo, Alibaba’s large‑scale e‑commerce knowledge graph that models user demand as concepts, describes its four‑layer architecture, the algorithms for concept extraction, taxonomy building, and item association, and demonstrates its impact on search and recommendation systems.
This technical presentation explains how Youku tackles the massive, real‑time update problem of video‑content graphs by adopting a graph‑database architecture, sub‑graph partitioning, schema‑driven logical views, and Flink‑based pipelines to achieve second‑level updates for billions of entities and attributes.
This article describes Suning's AI‑driven end‑to‑end solution for massive time‑series monitoring, anomaly detection, forecasting with DeepAR, MQ‑RNN, MQ‑CNN, ensemble methods, root‑cause localization using Hotspot and Monte‑Carlo Tree Search, and the evolution of its large‑scale log analytics platform.
The article outlines how advances in neural networks, semantic search, document understanding, image and voice search, and knowledge graphs are transforming enterprise search of unstructured data, enabling more accurate, context‑aware answers and new business use cases across organizations.
JD Digits' intelligent anti‑fraud platform leverages machine learning, big‑data processing, graph neural networks and small‑sample knowledge‑graph algorithms to provide millisecond‑level, real‑time protection across 600+ scenarios, while also offering AI‑powered solutions to banks and publishing research at top conferences.
This article explains how Alibaba B2B leverages knowledge‑graph technology—from its historical roots in knowledge engineering and expert systems to modern semantic‑web models, extraction pipelines, reasoning methods, storage solutions, and representation learning—to improve search, recommendation, and scene‑based procurement incentives in e‑commerce platforms.
This article details the technical architecture behind WeChat's 'Kan Kan' content understanding platform, covering text and multimedia analysis, tag extraction, entity recognition, knowledge graph construction, and how these components enhance recommendation recall, ranking, and user engagement across the ecosystem.
The 43rd ACM SIGIR 2020 International Conference on Research and Development in Information Retrieval, a premier CCF A‑class event, will be held virtually from July 25‑30, featuring theoretical and applied research on knowledge graph construction, explainable recommendation, and content generation, with a keynote by Meituan Waimai’s senior algorithm expert Maodi Hu.
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.
The article presents a detailed technical overview of Youku's video search system, covering multi‑modal inputs, multi‑level element indexing, face search, cross‑level and cross‑modal retrieval, and the design and applications of a multimodal graph engine with knowledge‑graph integration.
This article explores the breadth and challenges of operational data, outlines high‑level use cases such as knowledge graphs, automated fault assessment, unattended change management, and dynamic thresholds, and provides practical guidance for integrating these advanced scenarios into DevOps and AIOps workflows.
This article presents Beike's experience building a large‑scale graph database platform, covering the need for graph databases, technology selection between JanusGraph and Dgraph, detailed architecture, data ingestion pipelines, query interfaces, performance benchmarks, and future roadmap.
Facing billions of video entities and edges, Youku’s engineering team replaced traditional relational stores with a graph‑based knowledge platform, leveraging Alibaba’s Blink streaming engine and Lindorm to enable real‑time, incremental updates, unified UDF logic, and scalable feature computation for search and recommendation.
This article explains how Weibo's advertising platform leverages content understanding—covering system architecture, problems caused by insufficient comprehension, the construction of NLP and vision capabilities, content‑based ad strategies, and a celebrity‑brand knowledge graph—to improve ad relevance and ROI.
This article reviews the factors influencing search results, explains why query expansion is crucial for improving recall, surveys various sources of expansion terms, describes probabilistic and translation‑based models, and offers practical recommendations for building effective, data‑driven query expansion pipelines.
In this article, Wang Zhe addresses fifteen common questions about recommendation systems, covering topics such as building cross‑domain knowledge, the role of deep reinforcement learning, handling sparse or low‑sample data, offline‑online evaluation, knowledge graphs, graph neural networks, model interpretability, large‑scale ID embedding, and career advice for engineers.
The article explains how Beike Real Estate constructs an industry knowledge graph by integrating internal and external data, outlines the technical framework and data processing steps, and demonstrates its AI-driven applications such as intelligent Q&A, recommendation, and decision support for the real‑estate market.
This article outlines Baidu's large‑scale knowledge graph applications in AI, detailing the need for multimodal semantic understanding, challenges in text and video comprehension, and the technical solutions including entity annotation, conceptization, knowledge networks, and multimodal fusion for enhanced search, recommendation, and visual question answering.
Alibaba’s AliCoCo, a large‑scale e‑commerce cognitive concept net, models user needs as graph nodes, linking concepts, primitives, taxonomy and items, and leverages advanced NLP, BiLSTM‑CRF, projection learning and knowledge‑enhanced models to boost search relevance, recommendation diversity, and overall user experience.
This article explores how integrating unstructured business data into structured knowledge graphs and applying graph neural networks can overcome deep‑learning bottlenecks in NLP, dramatically improving HS‑code product classification accuracy from around 60% to over 75% through richer reasoning and multimodal knowledge.
This article presents a comprehensive overview of 360's voice interaction platform, detailing dialogue system fundamentals, platform architecture, and core technologies such as semantic understanding, dialog management, and question answering, all driven by deep learning and multimodal innovations.
Personalized recommendation systems, essential for modern internet platforms, can be enhanced by knowledge graphs which provide auxiliary information to improve accuracy, diversity, and explainability, with various methods such as embedding-based (DKN, MKR), path-based, and hybrid approaches like RippleNet and KGCN.
The article reviews recent deep‑learning breakthroughs in personalized content recommendation, covering news and e‑commerce systems, JD Digits' multi‑dimensional user behavior prediction models, knowledge‑graph meta‑learning, and the impact of multimodal AI on future recommendation technologies.
The talk explains how Beike Real Estate constructs a large‑scale relationship graph from billions of user, house, and agent interactions, quantifies edge strengths, builds homogeneous and heterogeneous sub‑graphs, derives graph capabilities such as node influence, embedding, similarity and relation prediction, and finally deploys these capabilities in multi‑degree queries, house‑similarity recommendations and B‑side agent assistance, achieving measurable CTR improvements.
This article presents the 2019 GOPS Global Operations Conference talk by Gong Cheng, detailing how intelligent monitoring leverages multi‑dimensional anomaly detection, machine‑learning‑based alert merging, knowledge‑graph construction, and root‑cause analysis to automate and improve large‑scale IT operations.
The OpenKG initiative, together with dozens of university and industry partners, has released a series of open‑source COVID‑19 knowledge graphs—including encyclopedia, research, clinical, hero, hotspot‑event, and upcoming prevention and resource graphs—detailing their data sources, scale, schema designs, and potential AI‑driven applications such as semantic search and intelligent question answering.
The article details how Beike's Agent Cooperation Network employs a multi‑layered risk control framework built on large‑scale knowledge graphs, graph mining, and machine‑learning techniques to detect fake listings, malicious competition, and other threats across both online and offline real‑estate scenarios.
This comprehensive report analyzes the evolution of knowledge graphs, reviews achievements of the first half, and examines the challenges and opportunities of the emerging second half, highlighting shifts from large‑scale simple applications to complex, expert‑driven scenarios, and outlining strategies for representation, acquisition, and application in the era of big data and AI.
The Hulu AI team’s ICLR‑2020 paper introduces a consciousness‑prior‑driven graph neural network that dynamically prunes message‑passing subgraphs, achieving state‑of‑the‑art results on large‑scale knowledge‑graph completion tasks while improving interpretability and computational efficiency.
The article reports on Beike Zhaofang's knowledge‑graph technology conference, detailing how relationship graphs are applied to risk control, the four‑layer graph architecture, the use of Spark GraphX, JanusGraph and DGraph, and broader industry‑graph applications in real‑estate AI solutions.
This article surveys recent advances in recommendation system technology, covering the evolution from a two‑stage recall‑ranking pipeline to a four‑stage architecture, and detailing emerging trends in model‑based recall, user‑behavior sequence modeling, knowledge‑graph integration, graph neural networks, advanced ranking models, multi‑objective optimization, multimodal fusion, and listwise re‑ranking.
This article presents an in‑depth overview of Alibaba's Xiaomì conversational AI system, describing how structured knowledge—including FAQs, phrase‑based knowledge, knowledge graphs, and machine‑read documents—is organized into a two‑level schema and applied to knowledge‑based QA (KBQA) and event‑based QA (EBQA) with detailed model pipelines, ranking, type inference, and recommendation techniques, while also discussing practical challenges and future directions.
This article reviews the principles, techniques, and challenges of automatically building knowledge graphs, covering logical modeling, latent‑space analysis, human‑computer interaction, ontology support, and practical pipelines, and illustrates their use in network behavior analysis, intelligent Q&A, and recommendation systems.
This article explains how query understanding (QP) transforms user search queries into structured semantic blocks and intent categories using rule‑based NLP, entity recognition, and post‑processing, and describes its taxonomy, implementation details, and practical impact on search engine results.
This presentation reviews recent progress in spoken and multimodal dialogue systems, covering X‑driven architectures, task‑oriented and open‑domain approaches, NLU/DM integration, FAQ, KB/KG‑driven methods, document‑driven dialogue, and outlines remaining challenges and future research directions.
This article presents a comprehensive case study of how 58.com built a personalized recommendation system for its large‑scale recruitment platform, covering business background, data challenges, user modeling, recall strategies, ranking pipelines, system architecture, experimental infrastructure, and future research directions.
This article describes the development of MT‑BERT, a BERT‑based language model pre‑trained on Meituan‑Dianping business data, its distributed mixed‑precision training pipeline, domain adaptation, knowledge‑graph integration, model compression techniques, and the wide range of downstream NLP applications achieved in the platform.
This article details how 58.com constructed a large‑scale personalized recommendation platform for its recruitment business, covering business background, user intent modeling, knowledge‑graph and NER techniques, user profiling, multi‑stage recall strategies, ranking model pipelines, serving infrastructure, AB testing, and future research directions.
MT‑BERT at Meituan‑Dianping combines mixed‑precision, domain‑adapted continual pre‑training, knowledge‑graph‑aware masking, and extensive compression techniques to produce fast, accurate BERT models that power fine‑grained sentiment analysis, intent classification, recommendation reasoning, and other NLP tasks across the platform.
This talk presents how Meituan leverages knowledge‑graph QA (KBQA) across restricted and complex smart‑interaction scenarios, compares semantic‑parsing and information‑retrieval approaches, introduces three‑layer concept nodes to handle entity explosion and non‑connected queries, and outlines architectural refinements for multi‑turn dialogue integration.
This article summarizes the keynote on intelligent operations presented at the 13th GOPS Global Operations Conference, covering multi‑dimensional anomaly detection, smart alarm aggregation, the construction of an operations knowledge graph, and AI‑driven root‑cause analysis techniques for large‑scale server environments.
The article details 360's AIOps deployment, covering external speaker insights, internal architecture, data collection pipelines, AI models for resource recycling, alarm reduction, and correlation, as well as visualization dashboards, labeling platforms, and self‑healing mechanisms, illustrating a comprehensive AI‑driven operations framework.
This talk analyzes the technical and practical challenges of current chatbots, introduces the next‑generation paradigm of virtual life, and details how AI technologies such as speech, vision, NLP, and knowledge graphs can be combined with multimodal interaction to create more intelligent and engaging conversational agents.
This article explores how AI is used in iQIYI's video advertising pipeline to analyze video content, generate and recommend ad placement points, create scene‑aware ad creatives, build a video knowledge graph, and support various ad formats, ultimately improving ad relevance and revenue.
This lecture explores how machine learning can generate individualized learning pathways for students by building knowledge dependency graphs, defining optimization goals, and leveraging historical data to rank candidate routes, while reflecting on data, model, business, and demand challenges in AI-driven education.
This article explains the motivation, challenges, architecture, and algorithms behind building a large‑scale cognitive concept graph for search, detailing data construction, concept mining, fusion, confidence scoring, hierarchical structuring, validation, service algorithms, platform access, and real‑world applications such as intent recognition and entity recommendation.
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 report explains how Alibaba uses knowledge graph construction, semantic parsing, and structured answer generation to overcome knowledge management and language understanding challenges in next‑generation intelligent customer service, delivering efficient reuse, precise comprehension, and fine‑grained management of knowledge.
This article introduces KOBE, a knowledge‑based personalized text generation system that leverages Transformer architecture, attribute fusion, and external knowledge graphs to produce fluent, domain‑aware recommendation reasons for e‑commerce products, with a case study on the Spring Festival cloud theme.
CrossE introduces an explicit crossover interaction mechanism for knowledge graph embedding, learning both general and interaction-specific representations of entities and relations, which improves link prediction accuracy and provides interpretable explanations, as demonstrated on benchmark datasets WN18, FB15k, and FB15k-237.
At SIGIR 2019 in Paris, Alipay presented two AI research papers—one applying reinforcement learning to predict user intent in customer‑service bots and another introducing the unsupervised QUEST method that builds noisy quasi‑knowledge graphs for answering complex multi‑document questions.
This article reviews the evolution, architecture, and key technical challenges of general‑domain conversational QA systems, describing Alibaba UC’s search background, dialogue bot types, data pipelines, and advanced methods such as transfer learning, few‑shot learning, and multi‑dimensional dialogue management.
This article introduces the "Kelu Fang" product, which leverages knowledge graph, NLU, and ranking technologies to enhance real‑estate search by adding commute‑based filtering and a local view of surrounding facilities, and discusses its architecture, implementation details, and future improvement directions.
This article presents a comprehensive overview of Alibaba's large entertainment division research on multimodal video analysis, covering intelligent video asset management, automated cover creation with personalized distribution, video knowledge graph construction, multimodal search techniques, and future directions in AI-driven media processing.
This article outlines the evolution of knowledge graphs, describes typical enterprise application scenarios, compares graph database options such as Neo4j, Cayley and Dgraph, and presents a six‑step methodology for building, storing, and applying knowledge graphs in large‑scale business environments.
This article explains the concept of a marketing data middle platform, its origins, the expectations of advertisers, how it differs from traditional data warehouses, the technical challenges of data governance, analysis and real‑time output, the role of knowledge graphs, system architecture, data sources, and the three main forms—Data Lake, CDP and DMP—offering a comprehensive overview for marketers and data professionals.
In an interview, JD data platform leader Li Wei discusses how dynamic balance between business demands and technical improvement, knowledge computing, and AI-driven product quality control can drive innovation, enhance user experience, and shape future R&D management strategies.
This article explains how Alibaba’s Search & Recommendation team constructs a large‑scale e‑commerce concept graph—defining e‑commerce concepts, mining them from queries and titles, building an ontology, linking concepts to entities, and applying the graph to improve personalized search and recommendation.
This article reviews Alibaba’s year‑long Knowledge Engine program, detailing its five‑module architecture, major technical breakthroughs such as automatic ontology building and deep‑learning alignment, and two flagship research works: adversarial learning for crowdsourced NER and an iterative rule‑and‑embedding reasoning framework.
