[Haiqing Forum] The latest research results presented by two scholars
time: 2017-05-20

Haiqing Forum will be held in B3-213 of School of Computer Science and Engineering of South China University of Technology on 27th May, 2017. 

1. Date and time: 14: 00-15: 30, 27-May- 2017

2. Venue: B3-213, School of Computer Science and Engineering, South China University of Technology, Higher Education Mega Center

3. Agenda

(1) 14: 00-14: 10     Welcoming speech by leaders of School

(2) 15: 10-15: 50     Speaker: Dr. Hsiao-chiu Song, Hong Kong University of Science and Technology

Title: Heterogeneous Information Networks for Knowledge Graphs: Recent Developments

(3) 15: 50-16: 30 Speaker: Dr. Jing Li, Paris Business School

Title: The Impact of the XBRL Mandate on Reporting Complexity

Seminar 1:Heterogeneous Information Networks for Knowledge Graphs: Recent Developments


In the past decades, especially in recent years, there are a lot of general-purpose knowledge bases (or knowledge graphs) developed, e.g., Wikipedia, Freebase, KnowItAll, DBpedia, YAGO, NELL, and Knowledge Vault. Instead of treating knowledge base as a source of generating flat features, it is also possible to consider the structural information provided by the knowledge base. Traditionally, the graph based algorithms only consider the knowledge base as homogeneous graph, and use homogeneous graph based features, e.g., least common ancestor, shortest paths, etc., to disambiguate the words and further refine the features in the text documents. However, when working on the world knowledge graphs, the sparsity of entity relations and computational complexity of finding shortest paths over all possible entities makes shortest path less useful. In this sense, simpler approaches such as count based features are preferred. In this talk, I will review the recent development of using heterogeneous information networks to represent the knowledge graph, and using the meta-path to characterize the count-based features through certain relations between entities. Moreover, given the limitation of representative power of meta-paths, we proposed how to incorporate meta-graph into similarity and recommendation.


Dr. Yangqiu Song is an assistant professor at HKUST. Before that, he was an assistant professor at WVU (2015-2016), a post-doctoral researcher at the Cognitive Computation Group at UIUC (2013-2015), a post-doctoral fellow at HKUST and visiting researcher at Huawei Noah's Ark Lab, Hong Kong (2012-2013), an associate researcher at Microsoft Research Asia (2010-2012) and a staff researcher at IBM Research China (2009-2010) respectively. He received his B.E. and Ph.D. degrees from Tsinghua University, China, in July 2003 and January 2009, respectively. His current research focuses on using machine learning and data mining to extract and infer insightful knowledge from big data. The knowledge helps users better enjoy their daily living and social activities, or helps data scientists do better data analytics. He is particularly interested in working on large scale learning algorithms, on natural language understanding, text mining and visual analytics, and on knowledge engineering for domain applications.

Seminar 2 :The Impact of the XBRL Mandate on Reporting Complexity


When appropriately implemented, data standardization has the potential to improve certain aspects of data quality. However, it may also have unintended and undesirable side effects. The eXtensible Business Reporting Language (XBRL) is an open standard that aims to facilitate the preparation, exchange, and comparison of financial reports. Leveraging the opportunity created by the exogenous XBRL adoption mandated by the U.S. Securities and Exchange Commission, we use a difference-in-differences (DID) research design to identify the causal effect of XBRL adoption on readability of HTML-formatted financial reports, an important source of financial information for investors and analysts. We find that mandatory XBRL adoption has made adopting firms’ HTML-formatted financial reports more complex and harder to read. Further analysis shows that this undesirable effect is more pronounced for firms with more quantitative disclosure.