AliCG: Fine-grained and Evolvable Conceptual Graph Construction for Semantic Search at Alibaba

TL;DR

AliCG is a novel framework for constructing fine-grained, evolving conceptual graphs that adapt to real-world, time-varying data, enhancing semantic search capabilities at Alibaba.

Contribution

It introduces new methods for extracting detailed, long-tail concepts and dynamically updating the graph based on user behavior, addressing limitations of prior approaches.

Findings

Effective extraction of fine-grained concepts demonstrated.

Successful deployment and online A/B testing at Alibaba.

Improved semantic search performance observed.

Abstract

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.