This article examines the distinctive features of tourism-domain knowledge graphs, outlines methods for constructing them from internal and external data sources, and explores their practical applications such as question‑answering bots, personalized recommendation, and advanced search within the online travel sector.
This article details the evolution, architecture, and practical applications of knowledge graphs at Beike Real Estate, covering their historical background, five‑view advantages, data pipelines, ontology construction, intelligent search, recommendation, and chatbot integration, while also discussing challenges and future directions.
Meituan leverages deep‑learning models such as Word2Vec, DSSM, and LSTM‑based encoders within its ClickNet framework to compute text similarity and rank results, integrating rich business features like user location and merchant rating, thereby surpassing traditional TF‑IDF, BM25, and XGBoost approaches and boosting click‑through rates and revenue.
Google Research unveiled “Talk to Books,” a semantic‑experience that lets users type statements or questions and receive full‑sentence matches from books, showcasing how AI trained on billions of conversational pairs can understand meaning beyond simple keyword matching.
Alibaba’s search team outlines how deep learning transforms e‑commerce search and recommendation, detailing system infrastructure, AI‑driven features like intelligent interaction, semantic search, personalized matching, performance optimizations, multi‑agent learning, and future plans for unified user and query representations.
This article presents a knowledge‑graph‑driven question answering system, detailing its three‑layer architecture, semantic search and disambiguation techniques, verb‑semantic templates, deep‑learning models, experimental results, and current challenges in data quality and model integration.
