GIANT: Scalable Creation of a Web-scale Ontology
- URL: http://arxiv.org/abs/2004.02118v1
- Date: Sun, 5 Apr 2020 07:51:23 GMT
- Title: GIANT: Scalable Creation of a Web-scale Ontology
- Authors: Bang Liu, Weidong Guo, Di Niu, Jinwen Luo, Chaoyue Wang, Zhen Wen, Yu
Xu
- Abstract summary: We argue that existing knowledge bases and categories fail to discover properly grained concepts, events and topics in the language style of online population.
We present 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.
We present our graph-neural-network-based techniques used in GIANT, and evaluate the proposed methods as compared to a variety of baselines.
- Score: 29.628181324907295
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
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