時(shí)間：2019年10月11日（周五）中午12點(diǎn)30分
地點(diǎn)：北洋園校區(qū)50樓A333教工活動(dòng)中心(咖啡廳)
報(bào)告題目：知識(shí)圖譜——人工智能的基石
主講人：王鑫
【個(gè)人簡(jiǎn)介】
王鑫,，天津大學(xué)智能與計(jì)算學(xué)部副教授,、人工智能學(xué)院副院長(zhǎng)。2009年和2004年于南開(kāi)大學(xué)計(jì)算機(jī)科學(xué)與技術(shù)系分別獲工學(xué)博士和學(xué)士學(xué)位,，澳大利亞西澳大學(xué),、格里菲斯大學(xué)訪問(wèn)學(xué)者。中國(guó)計(jì)算機(jī)學(xué)會(huì)高級(jí)會(huì)員,、信息系統(tǒng)專業(yè)委員會(huì)秘書長(zhǎng),、計(jì)算機(jī)術(shù)語(yǔ)審定工作委員會(huì)執(zhí)行委員、數(shù)據(jù)庫(kù)專業(yè)委員會(huì)委員,、大數(shù)據(jù)專家委員會(huì)通訊委員,；中國(guó)中文信息學(xué)會(huì)語(yǔ)言與知識(shí)計(jì)算專業(yè)委員會(huì)委員；中國(guó)人工智能學(xué)會(huì)教育工作委員會(huì)委員,；ACM 會(huì)員,、IEEE會(huì)員。主要研究方向?yàn)橹R(shí)圖譜數(shù)據(jù)管理與學(xué)習(xí),、大規(guī)模圖數(shù)據(jù)庫(kù),、大數(shù)據(jù)分布式處理。主持國(guó)家自然科學(xué)基金項(xiàng)目,、天津市自然科學(xué)基金項(xiàng)目,、“百度主題研究”項(xiàng)目、“CCF-華為數(shù)據(jù)庫(kù)創(chuàng)新研究計(jì)劃”等項(xiàng)目,。在IEEE TPDS, Complexity, WWW, ICDE, CIKM, ISWC, ER等國(guó)內(nèi)外學(xué)術(shù)期刊和會(huì)議上發(fā)表論文70多篇,。國(guó)際會(huì)議APWeb-WAIM2020程序委員會(huì)主席，JIST2019程序委員會(huì)主席,、DASFAA2018宣傳主席以及WWW2019, KDD2019, ISWC2019, DASFAA2017~2019, WISE2018~2019等國(guó)際會(huì)議程序委員會(huì)委員,。獲得國(guó)際會(huì)議APWeb-WAIM2018最佳論文提名獎(jiǎng)和最佳演示論文獎(jiǎng)。SCI期刊Big Data Research編委,、中文核心期刊《計(jì)算機(jī)工程與應(yīng)用》、《計(jì)算機(jī)系統(tǒng)應(yīng)用》編委,，IEEE TKDE,、KBS、WWWJ等國(guó)際期刊審稿人,。
【報(bào)告內(nèi)容簡(jiǎn)介】
知識(shí)圖譜是人工智能的重要基石,，其包括知識(shí)獲取,、知識(shí)組織、知識(shí)存儲(chǔ),、知識(shí)查詢與檢索,、知識(shí)推理與應(yīng)用等方面，是人工智能符號(hào)主義學(xué)派的新發(fā)展,，是解決人工智能可解釋性難題的關(guān)鍵工具,。本報(bào)告在給出人工智能歷史背景之后，追溯知識(shí)圖譜的發(fā)展脈絡(luò),，包括知識(shí)表示方法和知識(shí)工程的發(fā)展,，主要介紹目前以語(yǔ)義萬(wàn)維網(wǎng)和關(guān)聯(lián)數(shù)據(jù)為代表的主流知識(shí)圖譜理論、技術(shù),、標(biāo)準(zhǔn)與應(yīng)用,，展望知識(shí)圖譜如何促進(jìn)新一代人工智能的發(fā)展。
【相關(guān)學(xué)科】人工智能,、計(jì)算機(jī),、軟件工程、圖書情報(bào)
【主辦單位】校工會(huì),、圖書館,、科研院、校青年教師聯(lián)誼會(huì)
Lecture: Knowledge Graphs—the Cornerstone of AI
When: 12:30 p.m., Friday, October, 11th, 2019
Where: A333, 50th Building, School of Chemical Engineering and Technology, Beiyang campus
Lecturer:
Xin Wang is an Associate Professor at College of Intelligence and Computing and the vice-dean of School of Artificial Intelligence, Tianjin University. He obtained his Ph.D. and Bachelor degrees in Computer Science from Nankai University in 2009 and 2004, respectively, and worked as a visiting scholar at the University of Western Australia and Griffith University. He is a senior member of China Computer Federation (CCF), and the secretary-general of CCF Technical Committee on Information Systems, a member of CCF Technical Committee on Databases. His research interests include knowledge graph data management and learning, large-scale graph databases, and big data processing. He has been the main investigator of two research projects funded by the National Natural Science Foundation of China (NSFC). He has published more than 70 research papers in various international conferences and journals, including ICDE, WWW, CIKM, ISWC, ER, IEEE TPDS, and Complexity. He served as a PC co-chair of APWeb-WAIM’20, PC co-chair of JIST’19, a publicity co-chair of DASFAA’18, and PC members of WWW’19, KDD’19, ISWC’19, DASFAA’17-’19, WISE’18-’19, etc. He won the best paper runner-up award and the best demo paper award in APWeb-WAIM2018. He is an editor of the SCI journal Big Data Research, reviewer of international journals including IEEE TKDE, KBS, WWWJ, etc.
About the Lecture:
Knowledge Graph is the cornerstone of Artificial Intelligence, which includes knowledge acquisition, knowledge organization, knowledge storage, knowledge query and retrieval, knowledge reasoning and application, etc, which is the new development of Symbolic Artificial Intelligence, which is a key tool to solve the interpretability problem of AI. This talk will first give the historical background of Artificial Intelligence, then track back the development of knowledge graphs, which includes the approaches of knowledge representations and development of knowledge engineering, then mainly introduce the current mainstream theories, methods, standards, and applications of knowledge graphs, which is represented by the Semantic Web and Linked Data, and finally look forward to how knowledge graphs promote the development of the next generation of AI.
Relevant Discipline: Artificial Intelligence, Computer Science, Software Engineering, Library and Information
Organizers: Trade Unions, Library, Office of Science and Technology, Young Teachers Association
All students and staff of Tianjin University are welcome.