1. MINING DEEP KNOWLEDGE FROM SCIENTIFIC NETWORKS
郑晓晴

2. Jie Tang’s Home Page

3. 1. AMiner: Toward Understanding Big Scholar Data
Journal Publications
1. AMiner: Toward Understanding Big Scholar Data 
AMiner aims to provide a systematic modeling approach to gain a deep understanding of the large and heterogeneous networks formed by authors, papers they have published,and venues in which they were published.
developed an approach named COSNET to connect AMiner with several professional social networks,such as LinkedIn and VideoLectures, which significantly enriches the scholar metadata.
AMiner offers a set of researcher-centered functions, including social influence analysis , influence visualization, collaboration recommendation, relationship mining，similarity analysis, and community evolution.
Tang J. AMiner: Toward Understanding Big Scholar Data[C]//Proceedings of the Ninth ACM International Conference on Web Search and Data Mining. ACM, 2016:

4. 2. AMiner-mini_A People Search Engine for University
Journal Publications
2. AMiner-mini_A People Search Engine for University
It integrates academic data from multiple sources and performs disambiguation for people names, which is a fundamental issue for searching people.
Major contributions：
Name Disambiguation
Academic Search
Distributed Structure
The system mainly consists of the following components
Data Preparation
Core Techniques
System Applications
Liu J, Liu D, Yan X, et al. AMiner-mini: A People Search Engine for University[C]//Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management. ACM, 2014:

5. System Overview

6. Journal Publications
3. ArnetMiner: An Expertise Oriented Search System for Web Community
Web community targets at providing user-centered services to facilitate
1) how to automatically extract the researcher profile from the existing unstructured Web,
2) how to integrate the information (i.e.,researchers’ profiles and publications) from different sources,
3) how to provide useful search services based on the constructed web community, and
4) how to minethe web community so as to provide more powerful services to the users
The system mainly consists of the following components
Tang J, Zhang J, Zhang D, et al. Arnetminer: An expertise oriented search system for web community[C]//Proceedings of the 2007 International Conference on Semantic Web Challenge-Volume 295. CEUR-WS. org, 2007: 1-8.

7. System Overview 1. Extraction of the Researcher Community
2. Integration of Heterogeneous Data
DBLP bibliography(covers approximately 800,000 papers from major Computer Science publication venues)
Based on a unified probabilistic model using Hidden Markov Random Fields (HMRF)
3. Storage and Access:
4. Search
Person search.
Publication search.
Conference search.
5. Mining
Expert finding,
People association finding,
hot-topic finding & sub-topic finding
survey paper finding.

8. Journal Publications
4. NewsMiner: Multifaceted news analysis for event search
1. represent news as a link-centric heterogeneous network and formalize news analysis and mining task as link discovery problem
2. propose a co-mention and context based knowledge linking method and a topic-level social content alignment method
3. introduce a unified probabilistic model for topic extraction and inner relationship discovery within events
Hou L, Li J, Wang Z, et al. NewsMiner: Multifaceted news analysis for event search[J]. Knowledge-Based Systems, 2015, 76:

9. Journal Publications
Social content alignment is trying to find the links between social content and news articles.
extract topics from news and comments
represent news segments and comments by the obtained topic distribution and original wordlevel features
finally calculate the relatedness between comments and news segments.
For each comment, sort the related
news segments and take the most-related one as its aligned result.

10. Journal Publications
5. PatentMiner: Topic-driven Patent Analysis and Mining
(1) topic-driven modeling;
(2) heterogeneous network co- ranking;
(3) intelligent competitive analysis;
(4) patent summarization.
Tang J, Wang B, Yang Y, et al. PatentMiner: topic-driven patent analysis and mining[C]//Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2012:

11. Thanks