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MINING DEEP KNOWLEDGE FROM
SCIENTIFIC NETWORKS
郑晓晴
2016.10.24
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Jie Tang’s Home Page
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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: 467-467.
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Journal Publications
 2. AMiner-mini_A People Search Engine for University
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It integrates academic data from multiple sources and performs
disambiguation for people names, which is a fundamental issue
for searching people.
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Major contributions：
Name Disambiguation
Academic Search
Distributed Structure
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The system mainly consists of the following components
Data Preparation
Core Techniques
System Applications
Distributed Structure
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: 2069-2071.
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System Overview
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Journal Publications
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3. ArnetMiner: An Expertise Oriented Search System for Web Community
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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. CEURWS. org, 2007: 1-8.
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System Overview
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1. Extraction of the Researcher Community
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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)
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3. Storage and Access:
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4. Search
Person search.
Publication search.
Conference search.
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5. Mining
Expert finding,
People association finding,
hot-topic finding & sub-topic finding
survey paper finding.
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Journal Publications
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4. NewsMiner: Multifaceted news analysis for event search
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1. represent news as a link-centric
heterogeneous network and
formalize news analysis and mining
task as link discovery problem
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2. propose a co-mention and context
based knowledge linking method and
a topic-level social content alignment
method
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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: 17-29.
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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.
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Journal Publications
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5. PatentMiner: Topic-driven Patent Analysis and Mining
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(1) topic-driven modeling;
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(2) heterogeneous network coranking;
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(3) intelligent competitive analysis;
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(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: 1366-1374.
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Thanks
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