（10:30-12:00）Exploiting and Reasoning With Open Knowledge Graph

Jeff Pan

（15:30-17:00）語義計算與知識問答技術在實際場景中的應用

劉權

Time:8:30 – 10:00

Abstract: Learning to reason and understand the world’s knowledge is a fundamental problem in Artificial Intelligence (AI). The core research question that I will address in this tutorial is the following: how can we design scalable statistical learning and inference methods to operate over rich knowledge representations? In this tutorial, I will describe some recent studies on learning to reason in large scale knowledge graphs (KGs). More specifically, I will introduce both path-based and embedding-based approaches. Then, I will introduce DeepPath, a novel deep reinforcement learning framework for learning multi-hop relational paths: it uses a policy-based agent with continuous states based on knowledge graph embeddings, which reasons in a KG vector space by sampling the most promising relation to extend its continuous states based on knowledge graph embeddings, which reasons in a KG vector space by sampling the most promising relation to extend its path. To conclude, I will also some of our initial attempts of bridging path-finding and path-reasoning in a principled variational inference framework.

Bio: William Wang is the Director of the Natural Language Processing Group (http://nlp.cs.ucsb.edu/) and an Assistant Professor in Computer Science at University of California, Santa Barbara. He received his PhD from School of Computer Science, Carnegie Mellon University. He has broad interests in machine learning approaches to data science, including statistical relational learning, information extraction, computational social science, speech, and vision. He has published more than 50 papers at leading conferences and journals, and received best paper awards (or nominations) at ASRU 2013, CIKM 2013, and EMNLP 2015, a best reviewer award at NAACL 2015, an IBM Faculty Award, an Adobe Research Award, and the Richard King Mellon Presidential Fellowship in 2011. He is an alumnus of Columbia University, and a former research scientist intern of Yahoo! Labs, Microsoft Research Redmond, and University of Southern California. In addition to research, William enjoys writing scientific articles that impact the broader online community: his microblog @王威廉 has more than 100,000 followers and millions of monthly views. His work and opinions appear at major tech media outlets such as Wired, VICE, Fast Company, and Mental Floss.

Time:10:30-12:00

Abstract: While domain specific knowledge graphs are useful within specific domains, open knowledge graphs such as DBPedia, YAGO and Wikidata, have recently played instrumental roles in a number of applications. They can used as common sense knowledge for machine learning applications. They can also serve as reusable knowledge to complement domain specific knowledge graphs. In this tutorial, we will (1) introduce some well known open knowledge graphs, including DBPedia, YAGO and Wikidata, and their applications, and (2) survey on existing reasoning techniques for large scale open knowledge graphs. This tutorial is designed for a general semantic technology practitioner, whether from research or industry. Participants will only be expected to have basic knowledge of semantic technologies.

Bio:Prof Dr Jeff Z. Pan leads the Knowledge Technology group in the Department of Computing Science at University of Aberdeen. His research focuses primarily on knowledge representation, artificial intelligence and data science, in particular knowledge graph construction and maintenance, large-scale ontology reasoning, stream reasoning, question answering, and combining ontology reasoning with machine learning, as well as their applications. He is a key contributor of the W3C OWL (Web Ontology Language) standard. He leads the development of the award-wining TrOWL reasoner, the only ontology reasoner that Oracle Spatial and Graph (from v12) uses via the OWL-DBC database connection. He is an internationally leading expert on Knowledge Graph, being the Chief Editor of the first two books on Knowledge Graph, a new technology that is widely used by world leading IT companies. As the Chief Scientist and Coordinator of the EU Marie-Curie K-Drive project, he coordinated 22 Marie Curie Fellows on Knowledge Graph and Ontology research. He is an Associate Editor of the Journal of Web Semantics (JWS) and of the International Journal on Semantic Web and Information Systems (IJSWIS). He actively teams up with industrial collaborators on innovative research

Time:13:30-17:00

Abstract: Reasoning and inference are central to both human and artificial intelligence (AI). Modeling inference in natural language is notoriously challenging but is a basic problem towards true natural language understanding, as pointed out by MacCartney and Manning (2008), “a necessary (if not sufficient) condition for true natural language understanding is a mastery of open-domain natural language inference.” In this tutorial, I will introduce the state-of-the-art deep learning models for natural language inference (NLI). The tutorial will start with even more fundamental problems: semantic representation and composition, to lay the basis for the tutorial and our discussion. The tutorial will then focus not only on how deep learning models achieve the state-of-the-art performance but also on the limitations.

Bio: Xiaodan Zhu is an Assistant Professor of the Department of Electrical and Computer Engineering (ECE), Queen’s University, Canada. His research interests include Deep Learning, Natural Language Processing, Machine Learning, and Artificial Intelligence. Dr. Zhu received his Ph.D. from the Department of Computer Science at the University of Toronto in 2010 and his Master’s degree from the Department of Computer Science and Technology at Tsinghua University in 2000. Dr. Zhu is an Associate Editor of the Computational Intelligence journal. He also served on many academic committees, e.g., as the Publication Chair for COLING-2018, Area Chair of ACL-2018 and COLING-2018, and Steering Committee Member of SemEval-2018. Dr. Zhu is a panel member of Canada NSERC Discovery Grants (Computer Science; year 2017, 2020, 2021). He also served as an external reviewer for many government grants in Canada (e.g., NSERC), Singapore, and Hong Kong (e.g., GRF). Dr. Zhu also helps assess start-up companies’ proposals for seed-stage programs. In the past, he worked with top government research lab (e.g., NRC) and industrial research labs such as Google (New York), IBM T.J. Watson Research Center, and Intel China Research Center.

Time:8:30-12:00

Abstract: Every non-trivial text describes interactions and relations between people, institutions, activities, events and so on. What we know about the world consists in large part of such relations, and that knowledge contributes to the understanding of what texts refer to. Newly found relations can in turn become part of this knowledge that is stored for future use. To grasp a text’s semantic content, an automatic system must be able to recognize relations in texts and to reason about them. This may be done by applying and updating previously acquired knowledge. We focus here in particular on semantic relations that describe the interactions between nouns and compact noun phrases, and we present such relations from both a theoretical and a practical perspective. The theoretical exploration sketches the historical path that has brought us to the contemporary view and interpretation of semantic relations. We discuss a wide range of relation inventories proposed by linguists and by language processing people. Such inventories vary by domain, granularity and suitability for downstream applications. On the practical side, we investigate the recognition and acquisition of relations from texts. In a look at supervised learning methods, we present some of the available datasets, the variety of features that can describe relation instances, and some learning algorithms found appropriate for the task, including recent feature-less deep learning approaches. Next, we present weakly supervised and unsupervised learning methods of acquiring relations from large corpora with little or no previously annotated data. We show how enduring the bootstrapping algorithm based on seed examples or patterns has proved to be, and how it has been adapted to tackle Web-scale text collections. We also show a few machine learning techniques that can perform fast and reliable relation extraction by taking advantage of data redundancy and variability.

Bio: Dr. Preslav Nakov is a Senior Scientist at the Qatar Computing Research Institute, HBKU. His research interests include computational linguistics and natural language processing (for English, Arabic and other languages), question answering, fact-checking, machine translation, sentiment analysis, lexical semantics, Web as a corpus, and biomedical text processing. Preslav Nakov received a PhD degree in Computer Science from the University of California at Berkeley (supported by a Fulbright grant and a UC Berkeley fellowship), and an MSc degree from the Sofia University. He was a Research Fellow at the National University of Singapore (2008-2011), an honorary lecturer in the Sofia University (2008), research staff at the Bulgarian Academy of Sciences (2008), and a visiting researcher at the University of Southern California, Information Sciences Institute (2005). Preslav Nakov co-authored a Morgan & Claypool book on Semantic Relations between Nominals, two books on computer algorithms, and many research papers in top-tier conferences and journals. He received the Young Researcher Award at RANLP’2011. He was also the first to receive the Bulgarian President’s John Atanasoff award, named after the inventor of the first automatic electronic digital computer. Preslav Nakov is Secretary of ACL SIGLEX, the Special Interest Group (SIG) on the Lexicon of the Association for Computational Linguistics (ACL). He is also Secretary of SIGSLAV, the ACL SIG on Slavic Natural Language Processing. He is an Action Editor of the Transactions of the Association for Computational Linguistics (TACL) journal, a Member of the Editorial Board of the Journal of Natural Language Engineering, an Associate Editor of the AI Communications journal, and Editorial Board member of the Language Science Press Book Series on Phraseology and Multiword Expressions. He served on the program committees of the major conferences and workshops in Computational Linguistics, including as a co-organizer and as an area/publication/tutorial/shared task chair, Senior PC member, student faculty advisor, etc.; he co-chaired SemEval 2014-2016 and was an area co-chair of ACL, EMNLP, NAACL-HLT, and *SEM, a Senior PC member of IJCAI, and a shared task co-chair of IJCNLP.

Time:13:30 – 15:00

Abstract: 本報告系統地介紹阿里巴巴知識圖譜技術的發展。同時以商品知識圖譜為例，介紹在商業領域垂直知識圖譜構建和服務的實踐。 包括1. 大規模知識建模、知識獲取的技術和產品化思路。2. 垂直知識圖譜在商業領域的應用案例和挑戰。

Bio: 張偉是阿里巴巴知識圖譜負責人，張偉博士畢業于新加坡國立大學，本科畢業于哈爾濱工業大學。現為阿里巴巴業務平臺高級算法專家。曾任職新加坡資訊通信研究院自然語言處理應用實驗室主任。研究領域：知識圖譜、自然語言處理，機器學習等。

Time:15:30-17:00

Abstract: 隨著機器智能需求的不斷增加，如何實現對自然語言的深度認知理解成為包括工業界和學術界的重點研究對象。本報告面向實際應用場景自然語言理解的兩大主要任務，語義計算和知識問答展開深入介紹。在復雜多樣的應用場景需求中，如何實現精準的語義計算，如何實現高效自動的知識構建，以及在此基礎上的問答能力，是非常具有挑戰的課題。在給出語義計算及知識問答技術的背景及進展的基礎上，本報告將重點介紹相應技術在各垂直應用領域中的實際應用效果及可能存在的問題，以期為語義計算及問答領域的技術發展提供參考。

Bio:劉權是科大訊飛AI研究院語音交互研究主管，高級研究員，訊飛超腦常識推理研究負責人，國際常識知識推理會議Commonsense 2017學術委員會委員，博士畢業于中國科學技術大學電子工程與信息科學系、語音及語言信息處理國家工程實驗室。在語義理解、常識推理、人機交互等領域開展了多項核心技術研究,作為第一作者在三大自然語言理解會議（ACL、EMNLP、NAACL）及IJCAI等國際頂級會議上發表多篇學術論文，曾獲2013年全國語音通訊學術會議最佳學生論文獎，并作為主要負責人參與多項國家級與省部級科研攻關項目的技術研發工作。曾任加拿大約克大學計算機系訪問學者。2016年，所設計的神經網絡常識推理技術及系統，在美國紐約舉辦的國際新一輪認知智能評測Winograd Schema Challenge任務上取得冠軍成績。作為科大訊飛研究院語音交互研究主管，提出多項關鍵語義理解及問答技術，持續提升了科大訊飛AIUI平臺語義能力。