AliCG：面向阿里巴巴语义搜索的细粒度可演化概念图构建
AliCG: Fine-grained and Evolvable Conceptual Graph Construction for Semantic Search at Alibaba
摘要：
概念图是知识图的一种特殊类型，在语义搜索中起着重要的作用。现有的概念图构造方法通常从形式文本中提取高频、粗粒度和时不变的概念。然而，在实际应用中，有必要提取不太频繁的，细粒度的，随时间变化的概念知识，并建立一个不断发展的方式分类。在本文中，我们介绍了一种在阿里巴巴实现和部署概念图的方法。具体来说，我们提出了一个框架，称为AliCG，它能够a）提取细粒度的概念，通过一种新的自举与对齐共识的方法，b）挖掘长尾概念，一种新的低资源短语挖掘方法，c）通过概念分布估计方法的基础上，隐式和显式的用户行为动态更新的图形。我们已经在阿里巴巴UC浏览器上部署了这个框架。广泛的离线评估以及在线A/B测试证明了我们的方法的有效性。
英文摘要：
Conceptual graphs, which is a particular type of Knowledge Graphs, play an essential role in semantic search. Prior conceptual graph construction approaches typically extract high-frequent, coarse-grained, and time-invariant concepts from formal texts. In real applications, however, it is necessary to extract less-frequent, fine-grained, and time-varying conceptual knowledge and build taxonomy in an evolving manner. In this paper, we introduce an approach to implementing and deploying the conceptual graph at Alibaba. Specifically, We propose a framework called AliCG which is capable of a) extracting fine-grained concepts by a novel bootstrapping with alignment consensus approach, b) mining long-tail concepts with a novel low-resource phrase mining approach, c) updating the graph dynamically via a concept distribution estimation method based on implicit and explicit user behaviors. We have deployed the framework at Alibaba UC Browser. Extensive offline evaluation as well as online A/B testing demonstrate the efficacy of our approach.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
