  Understanding what online users may pay attention to is key to content
recommendation and search services. These services will benefit from a highly
structured and web-scale ontology of entities, concepts, events, topics and
categories. While existing knowledge bases and taxonomies embody a large volume
of entities and categories, we argue that they fail to discover properly
grained concepts, events and topics in the language style of online population.
Neither is a logically structured ontology maintained among these notions. In
this paper, we present GIANT, a mechanism to construct a user-centered,
web-scale, structured ontology, containing a large number of natural language
phrases conforming to user attentions at various granularities, mined from a
vast volume of web documents and search click graphs. Various types of edges
are also constructed to maintain a hierarchy in the ontology. We present our
graph-neural-network-based techniques used in GIANT, and evaluate the proposed
methods as compared to a variety of baselines. GIANT has produced the Attention
Ontology, which has been deployed in various Tencent applications involving
over a billion users. Online A/B testing performed on Tencent QQ Browser shows
that Attention Ontology can significantly improve click-through rates in news
recommendation.
