AliCG: Fine-grained and Evolvable Conceptual Graph Construction for Semantic Search at Alibaba
This work addresses the need for more dynamic and detailed conceptual knowledge in semantic search applications, particularly for e-commerce platforms like Alibaba, though it appears incremental as it builds on prior conceptual graph methods.
The paper tackles the problem of constructing conceptual graphs for semantic search by extracting fine-grained, long-tail, and time-varying concepts, and demonstrates its efficacy through offline evaluation and online A/B testing at Alibaba UC Browser.
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.