Abstract

Online social networks such as Facebook and LinkedIn have been an integrated part of people’s everyday life. To improve the user experience and power the products around the social network, Knowledge Graphs (KG) are used as a standard way to extract and organize the knowledge in the social network. This tutorial focuses on how to build Knowledge Graphs for social networks by developing deep NLP models, and holistic optimization of Knowledge Graphs and the social network. Building KG for social networks poses two challenges: 1) input data for each member in the social network is noisy, implicit and in multilingual, so a deep understanding of the input data is needed; 2) KG and the social network influence each other via multiple organic feedback loops, so a holistic view on both networks is needed.

In this tutorial, we will share the lessons we learned from tackling the above challenges in the past seven years on building the Knowledge Graph for the LinkedIn social network. To address the first challenge of noisy and implicit input data, we present how to train high precision language understanding models by adding small clean data to the noisy data. By doing so, we enhance the-state-of-the-art NLP models such as BERT for building KG. To address multilingual aspect of the input data, we explain how to expand a single-language KG to multilingual KGs by applying transfer learning. For the second challenge of modeling interactions between social network and KG, we launch new products to get explicit feedback on KG from users, and refine KG by learning deep embeddings from the social network. Lastly, we present how we use our KG to empower more than 20+ products at LinkedIn with high business impacts.

Schedule

Date: 9:00 -- 11:30, April 20

Location: Room 4

Materials

Please download the slides here at SlideShare

https://www.slideshare.net/ssuser979685/constructing-knowledge-graph-for-social-networks-in-a-deep-and-holistic-way

The tutorial recording can be found at

https://www.youtube.com/watch?v=AWotm2b3bLs&feature=youtu.be

Presenters

Qi He​ is a Director of Engineering at LinkedIn, leading a team of 100+ machine learning scientists, software engineers and linguistic specialists to standardize LinkedIn data and build the LinkedIn Knowledge Graph. He has 15+ years of experience managing and executing large complex AI projects in knowledge graph, recommendation, information retrieval, natural language processing and many others. He is a Member Board of Directors for ACM CIKM, and served as General Chair of CIKM 2013 and PC Chair of CIKM 2019. He serves as Associate Editor of IEEE Transactions on Knowledge and Data Engineering (TKDE) and Neurocomputing Journal, and regularly serves on the (senior) program committee of SIGKDD, SIGIR, WWW, CIKM and WSDM for 10+ years. He received the 2008 SIGKDD Best Application Paper Award, the 2020 WSDM 10-year Test of Time Award, and has 50+ papers in top-tier international conferences/journals with 5000+ citations.

Jaewon Yang​ is a Senior Staff Software Engineer at LinkedIn, where he leads projects on building machine learning models for standardizing member profiles and job postings to build the LinkedIn Knowledge Graph. His research interests include Information extraction, Knowledge graph mining and Conversational AI. Prior to joining LinkedIn in 2014, he obtained a Ph.D degree from Stanford Infolab and Master in Statistics at Stanford University. He received the SIGKDD dissertation award, the ICDM KAIS journal best paper award, and the ICDM best paper award.

Baoxu Shi is a Senior Machine Learning and Relevance Engineer at LinkedIn, who mainly works on Knowledge Graph Representation Learning and Knowledge Graph Construction. Prior to LinkedIn, he obtained his Ph.D. degree from the University of Notre Dame with a focus on Knowledge Graph Completion and Knowledge Graph Mining. He regularly serves as the program committee members for conferences including AAAI, ACL, EMNLP, ICWSM, NAACL, SDM.