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Conceptual graphs, which is a particular type of Knowledge Graphs, play anessential role in semantic search. Prior conceptual graph constructionapproaches typically extract high-frequent, coarse-grained, and time-invariantconcepts from formal texts. In real applications, however, it is necessary toextract less-frequent, fine-grained, and time-varying conceptual knowledge andbuild taxonomy in an evolving manner. In this paper, we introduce an approachto implementing and deploying the conceptual graph at Alibaba. Specifically, Wepropose a framework called AliCG which is capable of a) extracting fine-grainedconcepts by a novel bootstrapping with alignment consensus approach, b) mininglong-tail concepts with a novel low-resource phrase mining approach, c)updating the graph dynamically via a concept distribution estimation methodbased on implicit and explicit user behaviors. We have deployed the frameworkat Alibaba UC Browser. Extensive offline evaluation as well as online A/Btesting demonstrate the efficacy of our approach.

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