信息检索：基于知识图谱和深度学习的文本表示和搜索(Explicit and distributed semantics for text representation and retrieval)

https://pan.baidu.com/s/1hsaMmTe
https://pan.baidu.com/s/1nvyY5kP
Language Technologies Institute - Carnegie Mellon University - Chenyan Xiong

本篇博文是根据论文作者分享讲座整理，主要介绍了作者用知识图谱和分布式表示扩展语义信息来做信息检索的工作。(查询扩展的延伸)

引子

在信息检索中，文本大多数是由词袋模型来表示的。包括Query和Document

词袋模型：词向量空间里的离散的维度。当代搜索引擎的一个根基。

模型：BM25，LM，Learn2Rank

特征：TF， IDF，etc

问题：Vocabulary Mismatch

缺点：No Semantics, No Understanding, relies on a lot of feature engineering, 只是利用了统计特征

Focusing Ad hoc Search Task

Two ways to overcome the limitation of bag-of-words

• Knowledge graph: Introduing explicit semantics from kownledge graph to search
• Deep learning: Learn distributed semantics end-to-end from large data

知识图谱(Kownledge Graph)

实体（Entity）：Concepts, named entities, general entities, Perhaps all noun phrases

常用: Freebase、yaGO、DBpedia、WIKIPEDIA

实体的优势

• 更精简的表示 A more abstract text representation, focus on the more meaningful part of the text, noun phrases and concepts
• 引入背景知识 Bring in semantics from the knowledge graph

---

Xiong C, Power R, Callan J. Explicit semantic ranking for academic search via knowledge graph embedding[C