This article explains how the data challenges of modern intelligent supply chains can be addressed by using graph databases and knowledge graphs, detailing supply chain background, graph database fundamentals, graph algorithms, and real‑world case studies that illustrate risk assessment and logistics optimization.
This article introduces the fundamentals of knowledge graphs, explores their research dimensions—including knowledge engineering, NLP, databases, and machine learning—examines graph database storage models, discusses their relevance to AI and big data, and showcases the authors' own graph‑based projects and case studies.
This article presents concise introductions and download links for seven Meituan research papers accepted at KDD 2022, covering knowledge‑graph pre‑training, automatic feature and architecture selection for pre‑ranking, user intent discovery, persuasion factor modeling, counterfactual policy learning for top‑K recommendation, probabilistic forecasting of food preparation time, and a multi‑stage bonus allocation framework for meal delivery.
This article introduces the basic concepts of knowledge graphs, explores their research dimensions across knowledge engineering, natural language processing, databases and machine learning, discusses graph database storage models and their integration with artificial intelligence and big data, and presents related projects and real‑world case studies.
This article reviews recent work on combining knowledge graphs with neural networks, covering generative self‑supervised graph neural network pre‑training, differentiable logical reasoning over graphs, and a fuzzy‑logic based query‑embedding model that improves open‑domain question answering, especially for rare relations.
The 16th National Knowledge Graph and Semantic Computing Conference (CCKS 2022) organized by the Chinese Society of Chinese Information and hosted by Yanshan University runs from August 24‑27, 2022 in Qinhuangdao, featuring a theme of knowledge graphs empowering the digital economy, multiple academic and industry tracks, poster and demo calls, and registration details.
This talk presents recent advances in multi-step reasoning over large-scale, noisy knowledge graphs, introducing the Query2Box model that uses box embeddings for complex queries and the SMORE framework that enables efficient multi-hop inference on massive graphs through scalable query generation, embedding computation, and training pipelines.
This report details the concept, systematic construction process, and diverse applications of a large‑scale Traditional Chinese Medicine (TCM) knowledge graph, illustrating how semantic standards, ontology engineering, and graph databases enable intelligent retrieval, recommendation, and clinical decision support across multiple TCM sub‑domains.
This article details Meituan's "Meituan Brain" initiative, describing the roadmap and techniques for building large-scale life‑service knowledge graphs—including tag and dish graphs—through data mining, semantic extraction, synonym discovery, graph neural networks, and their integration into search, recommendation, and question‑answering systems.
This presentation explains where knowledge resides in data architectures, demonstrates knowledge‑graph‑driven skill discovery, metadata management, and semantic search, and concludes with a comparison of GraphQL and Cypher for graph queries, illustrated with real‑world Neo4j case studies.
This article introduces VentureSights, an AI‑driven intelligent industry analysis platform built on knowledge‑graph technology and the concept of industry atoms, detailing its core modules, workflow, industry‑atom representation, extraction algorithms, and overall system architecture for generating comprehensive industry reports and insights.
The article provides a comprehensive overview of education knowledge graphs, explaining their definition, significance, diverse application scenarios such as smart textbooks, deep reading, subject insight, and intelligent services, while also analyzing technical challenges like data heterogeneity, granularity, multimodality, quality control, and proposing future research directions.
This article presents several mature knowledge‑graph application cases, including Meituan’s large‑scale “Meituan Brain” for lifestyle services, the Sage Knowledge Base platform by Fourth Paradigm, and additional examples in recommendation, medical, QA, and power‑industry domains, highlighting methods, challenges, and model designs.
This article reviews several mature knowledge‑graph applications, describing Meituan’s large‑scale “Meituan Brain” for lifestyle services, the Fourth Paradigm’s Sage Knowledge Base platform with various representation‑learning models, and additional use cases in recommendation, QA, drug discovery, and power‑grid domains.
This article describes Ant Group's merchant knowledge graph, covering its background, overall architecture, data sources, schema design, processing pipeline, cross‑graph fusion techniques, cognitive applications such as vector‑based recall, and the upcoming open‑source and SDK initiatives aimed at sharing the knowledge and tools with the community.
This article presents a comprehensive case study on constructing an enterprise relationship knowledge graph for financial technology, detailing data preparation, ontology modeling, graph building, and three practical applications—path query, ultimate controller discovery, and group detection—demonstrating how AI-driven graph analytics can uncover hidden corporate connections.
58.com’s NLP senior engineer explains how a recruitment knowledge graph is built—through multi‑dimensional tag systems, tag mining, and relation extraction—and how it enhances bidirectional matching and recommendation efficiency, addressing challenges such as weak expression, cold start, and supply‑demand imbalance.
This article presents the JiuGe system developed by THUNLP for automatically generating Chinese classical poetry, detailing its research motivations, model architecture—including salient‑clue, working‑memory, topic‑memory, style‑transfer and reinforcement‑learning components—implementation, applications, and future directions.
This article introduces the intelligent question‑answering technology used in WeChat Search, covering background, knowledge‑graph‑based QA, document‑based QA, technical pipelines, key modules such as entity linking and relation recognition, and future research directions.
This talk explains how Meituan builds and leverages a massive lifestyle-domain knowledge graph to improve LBS recommendation, covering explicit and implicit graph applications, challenges such as explainability and data sparsity, and advanced models like dual‑memory networks and cross‑domain learning.
This article presents OPPO's large‑scale knowledge graph, detailing the algorithmic challenges and solutions for entity classification, alignment, information extraction, and query parsing, and explains how these techniques power the XiaoBu assistant's knowledge‑based QA, search, and recommendation services while outlining future research directions.
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 presents JD Technology's recent research on financial causal knowledge graphs, detailing the overall knowledge‑graph architecture, data layers, causal relation extraction, argument extraction, and graph‑alignment methods, and discusses their applications in finance, intelligent research reports, and industry‑leader recommendation.
This article reviews the emerging field of explainable recommendation, covering its motivation, historical evolution from template‑based to knowledge‑graph and generative‑language approaches, recent advances in graph‑structured and natural‑language explanations, key research works, industrial applications, and open challenges such as fact‑checking, low‑resource settings, and evaluation methods.
This article reviews recent advances in combining knowledge graphs with neural networks, covering generative pre‑training of graph neural networks, wiki‑graph based open‑domain question answering, differentiable logical reasoning, and a fuzzy‑logic query‑embedding model that improves performance on sparse‑relation queries.
This presentation explores how deep semantic role labeling and multimodal data fusion of biomedical literature can build fine‑grained knowledge graphs that enable drug repurposing, describing the AGAC corpus, modeling approaches, evaluation results, and future research directions.
This article explains Meituan's 'Meituan Brain' initiative, detailing the construction of life‑service knowledge graphs—including tag and dish graphs—through data mining, semantic extraction, synonym discovery, graph labeling, and applications such as open QA, search ranking, and recommendation using AI and GNN techniques.
This article outlines the development of a power‑sector knowledge graph by China Electric Power Research Institute, covering the AI institute overview, the background of power knowledge engineering, methods for representation and ontology construction, practical applications in dispatch, inspection and customer service, and future challenges.
The article explains the emerging concept of Data Fabric (data weaving), its design principles, how it integrates metadata, knowledge graphs, and AI/ML to automate data integration across hybrid and multi‑cloud environments, and outlines four essential pillars that leaders must master to deliver business value.
This talk introduces automated knowledge graph representation learning, covering background, key techniques such as triple‑based, path‑based and subgraph‑based models, AutoML‑driven model search (AutoSF, Interstellar, RED‑GNN), evaluation metrics, and future research directions in AI.
This article presents Meituan's large‑scale knowledge graph, its integration into location‑based recommendation, the challenges of explainability, domain diversity, data sparsity and spatiotemporal complexity, and describes a dual‑memory neural network and cross‑domain learning approach that improve recall, ranking and recommendation fairness.
This article introduces the AI Institute of China Electric Power Research Institute, outlines the background, core concepts, and development of power sector knowledge engineering, details knowledge representation, ontology construction, and graph building methods, and discusses practical applications and future challenges of power domain knowledge graphs.
The article introduces TextToKnowledge, an open‑source Baidu platform that provides a unified Chinese term taxonomy (TermTree) and two annotation tools (WordTag and NPTag) to enable zero‑sample text labeling, term‑linking, and downstream knowledge‑mining applications for various NLP tasks.
This talk introduces the background, key directions, and model designs for automated knowledge‑graph representation learning, covering triple‑based, path‑based, and subgraph‑based approaches, the role of AutoML in searching optimal bilinear scoring functions, and future research challenges such as scalability, inductive inference, and domain‑specific applications.
This article reviews recent advances in knowledge graph reasoning, introduces the RNNLogic framework that jointly learns a rule‑generating LSTM and a stochastic logic programming predictor, and demonstrates its competitive performance and interpretability on benchmark datasets while outlining future neural‑symbolic directions.
This talk presents Meituan Brain's massive product knowledge graph, explains how knowledge‑enhanced models improve product title parsing, category association and sample governance, and demonstrates the resulting gains in search, recommendation and other downstream services while keeping the system online‑controllable and explainable.
The 2022 CCKS conference invites submissions of open‑resource papers on knowledge graphs and semantic computing, offering details on submission deadlines, required paper length, evaluation criteria, example resources, chairs, important dates, and links to templates and the EasyChair submission portal.
This article details Meituan's end‑to‑end practice of knowledge‑graph visualization, covering basic concepts, scenario analysis, D3.js‑based architecture, layout optimization, visual‑noise reduction, rich interaction features, 3D extensions, real‑world deployments and future research directions.
This article presents a comprehensive overview of QQ Music's recall pipeline, detailing business characteristics, challenges such as noisy user behavior and cold‑start, and four major solutions—including knowledge‑graph‑enhanced recall, sequence‑based and multi‑interest modeling, audio‑based recall, and federated learning—along with practical insights and Q&A.
This article introduces Meituan's on‑site comprehensive knowledge graph, detailing its multi‑layer design, data‑driven construction pipeline, challenges of diverse user demands and industry complexity, and showcases practical applications in search, recommendation, intelligent display, as well as future expansion plans.
This article explains the importance of selecting the right graph database for knowledge‑graph systems, compares major graph‑DB options, outlines essential characteristics of effective benchmarks, and describes the development of a financial‑graph benchmark based on LDBC standards.
This article introduces the Dxm Eros ultra‑large graph platform, explains its architecture, storage, analysis, modeling and visualization capabilities, and demonstrates how graph‑based machine learning is applied to fintech risk control, anti‑fraud, anti‑money‑laundering and automated audit workflows.
This article presents a comprehensive study of graph‑based recall techniques for content recommendation, detailing how knowledge‑graph‑augmented user‑behavior graphs and novel attention‑driven models such as GADM, SGGA, and SGGGA improve performance for users with sparse interaction histories.
The DataFun Summit 2022 Knowledge Graph Forum, held on March 12, presents cutting‑edge research on knowledge graph representation learning, multi‑hop reasoning, temporal KG question answering, and their applications in finance and retail, featuring talks by leading experts from JD, Fourth Paradigm, Stanford, and Meituan.
The paper proposes graph‑based deep recall models that enrich sparse user behavior sequences in video recommendation by integrating content knowledge graphs and adaptive attention mechanisms, demonstrating that variants such as GADM, SGGA, and SGGGA significantly boost click‑through rates in online experiments.
This article reviews recent research on e‑commerce search, covering transformer‑based complementary rankings, Alibaba's cognitive concept net and its extension, joint deep retrieval with product quantization, personalized semantic retrieval, multi‑granularity deep semantic retrieval, and graph‑attention networks for long‑tail shop search.
This article introduces the Dxm Eros ultra‑large graph platform, detailing its architecture, storage, analysis, modeling, and visualization modules, and demonstrates how graph machine‑learning techniques are applied to financial risk control, fraud detection, anti‑money‑laundering, and automated credit review.
This article details Tencent Music's music knowledge graph, covering data classification, graph database evaluation, system architecture, online and offline data pipelines, advanced search use cases, and practical business scenarios, illustrating how graph technology enhances intelligent retrieval and recommendation.
This article presents a comprehensive overview of Taobao's user‑generated content sentiment analysis pipeline, covering task definition, challenges, model architecture with RoBERTa‑based extraction, sentiment‑knowledge pre‑training, graph augmentation, personalized ranking, business impact metrics, and future research directions.
The article explains how the cloud‑construction platform evaluated graph‑database options based on open‑source, scalability, latency, storage capacity and import capabilities, ultimately choosing NebulaGraph, and then details NebulaGraph’s distributed meta, storage and query services as well as the overall multi‑layer knowledge‑graph platform architecture and future application scenarios.
This technical report presents a comprehensive overview of artificial intelligence evolution, knowledge‑graph construction techniques—including traditional, cross‑lingual and reading‑comprehension based named entity recognition, weak‑supervised and joint relation extraction, attribute completion via multi‑source cues, and conditional knowledge‑graph modeling—highlighting recent research findings and experimental results.
The article outlines five major trends—neural-network-enhanced search, semantic search, document understanding, image and voice search, and knowledge graphs—that are transforming enterprise use of unstructured data by leveraging AI to deliver precise, context-aware answers and insights.
This presentation reviews the concepts, tasks, and challenges of information extraction from unstructured text, covering closed and open settings, relation extraction, joint extraction, and open extraction methods, and discusses recent advances such as segment‑attention, global‑rationale models, ETL, TPLinker, and maximal‑clique based approaches with experimental results.
This article reviews Baidu's knowledge‑enhanced semantic understanding models, detailing the evolution from early semantic techniques to ERNIE 1.0, 2.0 and the large‑scale ERNIE 3.0, its architecture, training strategies, performance benchmarks, and real‑world applications across industry.
This article presents a comprehensive overview of building and applying product knowledge graphs for e‑commerce, covering background, recent advances in graph neural network‑based entity alignment, online prediction pipelines, data construction, evaluation metrics, attribute extraction, and future research directions.
This talk reviews recent progress in neural‑symbolic learning and multimodal knowledge discovery, highlighting examples such as GPT‑3 reasoning failures, the need for symbolic knowledge, historical developments, various integration methods, challenges in multimodal knowledge graphs, and future research directions.
The CNCC 2021 forum brought together leading academics and industry experts to discuss advances in graph neural networks, graph computing for quantum chemistry, and practical applications of knowledge‑graph reasoning in sectors such as real‑estate and online video, showcasing both research breakthroughs and industrial deployment strategies.
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 talk presents DAMO Academy’s end‑to‑end NLG pipeline for e‑commerce, covering the XGeneration content‑reproduction system, controllable short‑text generation models (PKM, PSCN), KG‑to‑text conversion, metaphor generation, and new‑media extensions such as short‑video script creation and intelligent video editing, together with experimental results and practical applications.
This article presents a curated collection of high‑star GitHub "awesome" repositories covering knowledge graph fundamentals, relation extraction, KG‑QA, graph construction, graph neural networks, dynamic graph learning, and multimodal knowledge graphs, providing links, summaries, and key resources for researchers and practitioners.
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 knowledge‑based question answering system tackles diverse, constraint‑rich, multi‑hop queries across pre‑sale, in‑sale and post‑sale scenarios by integrating fine‑grained query understanding, relation recognition, sub‑graph retrieval and answer ranking, employing optimized BERT models, pre‑training tasks, and domain‑specific enhancements to boost response speed, conversion rates, and benchmark performance, while acknowledging remaining challenges in long‑tail and complex queries.
This article introduces knowledge graphs, defining them as semantic networks or multi‑relational graphs, explains entities and relations, compares RDF and graph‑database storage, outlines construction steps including entity extraction and ontology building, reviews embedding models like TransE/H/R/D, and explores applications in search, finance, recommendation, and language models.
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.
Alibaba’s Frontend Knowledge Graph, curated by dozens of experts over a year, maps 717 key topics across beginner, intermediate, and advanced levels, offering detailed explanations, resources, and an interactive visual tool for developers to explore and deepen their web development skills.
This article presents a comprehensive overview of constructing an industry‑chain knowledge graph for the financial sector, covering its theoretical background, architectural design, automated building pipeline, key NLP techniques, and practical applications such as visualization, IPO review, and investment analysis.
The article explains graph databases—where nodes and edges model entities and relationships—covers their query languages, rapid global popularity since 2014, Neo4j’s property‑graph implementation and example knowledge graph, compares alternatives, and urges Chinese researchers to pursue independent theoretical and engineering innovation.
This article presents Alibaba's AliMe team’s year‑long progress on knowledge graphs, covering the basics of knowledge graphs, domain‑specific and multi‑modal graph construction techniques, practical e‑commerce applications such as dialogue‑driven recommendation, virtual‑anchor script generation, and key takeaways for future research.
The CNCC2021 Technical Forum, held from October 28‑30 in Shenzhen, gathers leading experts to discuss knowledge graph construction, large‑scale federated training, cloud‑edge integrated platforms, and trustworthiness, offering insights into how knowledge graphs drive digital transformation across industries.
This presentation reviews the importance of entity‑relation extraction for knowledge‑graph construction, compares sentence‑level and complex contexts, and surveys joint extraction techniques—including sequence labeling, table filling, and seq2seq models—as well as document‑level graph‑based methods and future research directions.
This article presents Alibaba's AliMe team's year‑long progress on knowledge graph research, covering the fundamentals of knowledge graphs, domain and multimodal graph construction techniques, practical e‑commerce applications such as dialogue‑driven recommendation, virtual‑anchor script generation, and insights on future directions.
This article describes how Meituan builds a multi‑level, multi‑dimensional retail product knowledge graph to support new‑retail scenarios, detailing its architecture, data acquisition challenges, labeling pipelines, attribute extraction methods, efficiency improvements, human‑machine collaboration, and downstream search and recommendation applications.
The ByteDance Engine Developer Community Meetup featured senior engineers from ByteDance and OPPO who presented the latest advances in intelligent audio signal processing, natural language processing for recommendation, entity linking in knowledge graphs, and multimedia machine translation, highlighting practical applications and performance challenges.
This article presents the architecture, techniques, and practical solutions behind intelligent question answering in QQ Browser's search engine, covering knowledge‑graph based QA (KBQA), machine‑reading‑comprehension QA (DeepQA), and information‑retrieval QA (IRQA), and discusses system design, model optimization, and future directions.
This talk explores how multimodal AI, knowledge‑graph‑enhanced script generation, and advanced reading‑comprehension techniques enable virtual anchors to transform e‑commerce live streaming from simple Q&A bots into interactive, content‑rich live broadcasts, addressing challenges of material sourcing, personalization, and low‑latency response.
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 describes how HuiMei Technology leverages a medical knowledge graph, natural‑language processing, and AI‑driven scoring to automate venous thromboembolism (VTE) risk assessment in large hospitals, detailing the business background, technical architecture, implementation workflow, and ongoing research directions.
The article provides a comprehensive overview of knowledge graphs, covering their background, definitions, evolution, core concepts such as entities, attributes, and relationships, and illustrating typical applications across medical, finance, government, e‑commerce, and chatbot domains.
This article reviews algorithmic solutions for cold‑start recommendation, covering the efficient use of side information, knowledge graphs, cross‑domain transfer, multi‑behavior signals, limited interaction data, explore‑exploit tactics, and additional practical scenarios, while summarizing key methods such as DropoutNet, MetaEmbedding, MWUF, MeLU and MetaHIN.
This article describes how JD Health’s platform product team applies natural language processing, knowledge graphs, and AI-driven quality control to address industry pain points in online medical consultations, detailing the workflow, technical modules, current results, and future directions for comprehensive tele‑health assurance.
This article details Meituan's development of a multi‑level, multi‑dimensional retail product knowledge graph, covering its background in new retail, hierarchical design, attribute modeling, challenges, efficiency improvements, human‑machine collaboration, and its impact on search, recommendation and both C‑ and B‑side services.
iQIYI’s Qisou Knowledge Graph, built since 2015 through a five‑stage pipeline of schema modeling, multi‑source data acquisition, entity alignment fusion, JanusGraph‑HBase storage, and inference‑driven querying, now powers precise video search, intelligent Q&A, tag mining, and relationship‑based recommendation across its platform.
This article reviews the fundamentals of entity‑relation extraction, surveys joint extraction techniques such as sequence labeling, table‑filling and seq2seq models, discusses document‑level graph‑based methods, highlights experimental findings, and outlines future research directions in knowledge‑graph construction.
The Local Life Comprehensive Demand Knowledge Graph (GENE) reorients Meituan’s supply‑demand matching by building a multi‑layer, user‑centric graph that captures intent and consideration, employing BERT, Word2Vec, ELECTRA, and reinforcement‑learning models to generate concrete and scene‑based demand nodes, now powering parent‑child, leisure, medical‑beauty, and education services.
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.
