基于知识图谱的搜索与推荐.pdf

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Intelligent Search Engine and Recommender Systems based on Knowledge Graph 阳德青复旦大学知识工场实验室 yangdeqing@fudan.edu.cn 2017-07-13

Background • Knowledge Graph exhibits its excellent performance through the intelligent applications built on it • As typical AI systems, Search engine and recommender system are very popular and promising in the era of large data • Many previous literatures and systems have proved KG’s merits on such AI’s applications

KG-based Search Engine

The History of Search Engine • 1st: category-based • Yahoo, hao123 • 2nd: IR-based • Keyword-based, vector space, Boolean model result in • 3rd: link-based • PageRank (Google) • The keyword of high click frequency are ranked higher • The pages containing the keywords of more weights are ranked higher • The pages having more important in-links are ranked higher if users want to search something new or the ones of long tail? However, how to handle it

The History of Search Engine • When Knowledge Graph emerges keyword/string thing/entity relevant entities

The History of Search Engine • Search 4.0: user-based search engine • Really understand the search intent of users, especially for those rare entities • Directly return pure answers (entities) instead of relevant web pages • Provide plentiful/relevant results with high confidence/interpretability intelligent search engine Knowledge graph can help the engine achieve these goals It is actually a recommendation.

Entity Suggestion with Conceptual Explanation [1] • More than search iPhone 6 Plus or Can any search engine return such results? • Problem statement • Given a query q described by a set of entities, the engine should return some relevant entities along with some fine-grained concepts which can well explain the results

Entity Suggestion with Conceptual Explanation • What are the relevant entities and fine-grained concepts? Growing Market Emerging economy BRIC What entities should be suggested? Country Company Chinese company Chinese internet giant

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