{"id":683,"date":"2024-08-06T10:30:07","date_gmt":"2024-08-06T02:30:07","guid":{"rendered":"https:\/\/sigkg.cn\/ccks-ijckg2024\/?page_id=683"},"modified":"2024-08-06T14:55:44","modified_gmt":"2024-08-06T06:55:44","slug":"%e7%89%b9%e9%82%80%e6%8a%a5%e5%91%8a","status":"publish","type":"page","link":"http:\/\/sigkg.cn\/ccks-ijckg2024\/keynotes\/","title":{"rendered":"\u7279\u9080\u62a5\u544a"},"content":{"rendered":"\n

\u62a5\u544a<\/strong>\u9898\u76ee: What is next for Knowledge Graphs:  Relevating the semantic Web vision<\/h2>\n\n\n\n

James Hendler\uff08\u7f8e\u56fd\u4f26\u65af\u52d2\u7406\u5de5\u5b66\u9662\uff09<\/h3>\n\n\n\n
\"\"<\/figure>\n\n\n\n

\u4e13\u5bb6\u4ecb\u7ecd\uff1a<\/strong>
James Hendler is the Director of the Future of Computing Institute and the Tetherless World Professor of Computer, Web and Cognitive Sciences at RPI.  Hendler is a data scientist with specific interests in open government and scientific data, AI and machine learning, semantic data integration and the data policy in government. One of the originators of the Semantic Web, he has authored over 450 books, technical papers, and articles in the areas of Open Data, the Semantic Web, AI, and data policy and governance. Hendler is a Fellow of the AAAI, AAIA, AAAS, ACM, BCS, IEEE and the US National Academy of Public Administration.<\/p>\n\n\n\n

\u62a5\u544a\u6458\u8981\uff1a<\/strong>
Knowledge Graphs have become a cornerstone in organizing and interpreting vast amounts of data, offering unprecedented insights and fostering innovation across various industries. As we stand on the cusp of the next evolution, this talk will explore the future directions and potential breakthroughs in Knowledge Graphs. We will delve into emerging trends such as the integration of machine learning with semantic technologies, enhancing interoperability, and scaling knowledge graphs for real-time applications. Additionally, we will discuss the role of advanced reasoning capabilities and the importance of contextual understanding in enriching the semantic vision. In this talk, we explore some of the original goals of the semantic web and knowledge graph technologies, some of their current uses, and some issues to consider for the future.<\/p>\n\n\n\n

\u62a5\u544a<\/strong>\u9898\u76ee: \u5730\u5b66\u77e5\u8bc6\u56fe\u8c31<\/h2>\n\n\n\n

\u738b\u65b0\u5175\uff08\u4e0a\u6d77\u4ea4\u901a\u5927\u5b66\uff09<\/h3>\n\n\n\n
\"\"<\/figure>\n\n\n\n

\u4e13\u5bb6<\/strong>\u4ecb\u7ecd\uff1a<\/strong>
\u738b\u65b0\u5175\uff0c\u4e0a\u6d77\u4ea4\u901a\u5927\u5b66\u7279\u8058\u6559\u6388\uff0c\u56fd\u5bb6\u81ea\u7136\u79d1\u5b66\u57fa\u91d1\u59d4\u6770\u51fa\u9752\u5e74\u57fa\u91d1\u83b7\u5f97\u8005\uff0c\u56fd\u5bb6\u4e07\u4eba\u8ba1\u5212\u79d1\u6280\u521b\u65b0\u9886\u519b\u4eba\u624d\u3002\u73b0\u4efbACM\u4e2d\u56fd\u7406\u4e8b\u4f1a\u4e3b\u5e2d\uff082021-\uff09\uff0c\u4e2d\u56fd\u5730\u8d28\u5b66\u4f1a\u201c\u6570\u636e\u9a71\u52a8\u4e0e\u5730\u5b66\u53d1\u5c55\u4e13\u4e1a\u59d4\u5458\u4f1a\u201d\u526f\u4e3b\u4efb\u59d4\u5458\uff082023-\uff09\u3002\u66fe\u4efb\u4e0a\u6d77\u4ea4\u901a\u5927\u5b66\u7535\u5b50\u4fe1\u606f\u4e0e\u7535\u6c14\u5de5\u7a0b\u5b66\u9662\u526f\u9662\u957f\uff0c\u4e0a\u6d77\u4ea4\u901a\u5927\u5b66John Hopcroft\u8ba1\u7b97\u673a\u79d1\u5b66\u4e2d\u5fc3\u6267\u884c\u4e3b\u4efb\u3002\u62c5\u4efb\u591a\u4e2aCCF-A\u7c7b\u56fd\u9645\u671f\u520a\u8d44\u6df1\u7f16\u59d4\uff0c\u5305\u62ecIEEE Transactions on Information Theory, IEEE\/ACM Transactions on Networking\u3002\u53d1\u8868\u9886\u57dfA\u7c7b\u671f\u520a\u4e0e\u4f1a\u8bae\u8bba\u6587100\u4f59\u7bc7\uff0c\u8c37\u6b4c\u5b66\u672f\u5f15\u75281\u4e07\u4f59\u6b21\uff0c\u8fde\u7eed\u516d\u5e74\u5165\u9009\u7231\u601d\u552f\u5c14\uff08Elsevier\uff09\u4e2d\u56fd\u9ad8\u88ab\u5f15\u5b66\u8005\u30022012\u5e74\u83b7\u4e2d\u56fd\u8ba1\u7b97\u673a\u5b66\u4f1a\u9752\u5e74\u79d1\u5b66\u5bb6\u5956\uff0c2018\u5e74\u83b7\u9ad8\u7b49\u6559\u80b2\u56fd\u5bb6\u7ea7\u6559\u5b66\u6210\u679c\u5956\u4e8c\u7b49\u5956\uff08\u63922\uff09\u3002\u7d2f\u8ba1\u57f9\u517b57\u4f4d\u5b66\u751f\uff08\u5176\u4e2d\u4f18\u535a3\u4f4d\uff09\u5728\u9ad8\u6821\u4efb\u6559\u804c\uff0c\u5305\u62ecC9\u9ad8\u682117\u4f4d\u3001985\u9ad8\u682126\u4f4d\u30018\u4f4d\u62c5\u4efb\u9662\u957f\/\u7cfb\u4e3b\u4efb\u804c\u52a1\u3002<\/p>\n\n\n\n

\u62a5\u544a\u6458\u8981\uff1a<\/strong>
\u5730\u7403\u79d1\u5b66\u4e0e\u4fe1\u606f\u79d1\u5b66\u7684\u6df1\u5ea6\u4ea4\u53c9\u878d\u5408\uff0c\u4e3a\u4eba\u5de5\u667a\u80fd\u52a9\u529b\u7684\u5730\u5b66\u7814\u7a76\u3001\u6df1\u65f6\u7a7a\u6570\u636e\u9a71\u52a8\u7684\u77e5\u8bc6\u56fe\u8c31\u6784\u5efa\u5e26\u6765\u4e86\u673a\u9047\u548c\u6311\u6218\u3002\u4e00\u65b9\u9762\uff0c\u4fe1\u606f\u79d1\u5b66\u7684\u4eba\u5de5\u667a\u80fd\u3001\u5927\u6570\u636e\u3001\u7f51\u7edc\u4fe1\u606f\u7b49\u9886\u57df\u7684\u98de\u901f\u53d1\u5c55\uff0c\u4e3a\u5730\u5b66\u7814\u7a76\u7684\u79d1\u5b66\u53d1\u73b0\u63d0\u4f9b\u4e86\u65b0\u624b\u6bb5\uff1b\u53e6\u4e00\u65b9\u9762\uff0c\u5730\u5b66\u5927\u6570\u636e\u7684\u6df1\u65f6\u7a7a\uff08\u6df1\u65f6\u3001\u6df1\u7a7a\u3001\u6df1\u5730\u3001\u6df1\u6d77\uff09\u7279\u6027\uff0c\u62d3\u5c55\u4e86\u77e5\u8bc6\u56fe\u8c31\u6f14\u5316\u5fc5\u9700\u7684\u6570\u636e\u591a\u6837\u6027\u3002\u5730\u5b66\u77e5\u8bc6\u56fe\u8c31\u5df2\u6210\u4e3a\u5730\u7403\u79d1\u5b66\u4e0e\u4fe1\u606f\u79d1\u5b66\u7684\u56fd\u9645\u524d\u6cbf\u4ea4\u53c9\u7814\u7a76\u70ed\u70b9\u3002\u56f4\u7ed5\u6df1\u65f6\u7a7a\u6570\u636e\u4e0e\u77e5\u8bc6\u56fe\u8c31\u4e4b\u95f4\u7684\u8868\u5f81\u540c\u6784\u6620\u5c04\u3001\u4e92\u4fc3\u4f5c\u7528\u673a\u7406\u7b49\u79d1\u5b66\u95ee\u9898\uff0c\u5f00\u5c55\u5730\u5b66\u6570\u636e\u7684\u7ed3\u6784\u5316\u8868\u793a\u91cd\u5efa\u3001\u77e5\u8bc6\u56fe\u8c31\u7684\u6df1\u65f6\u7a7a\u8fdb\u5316\u751f\u957f\u3001\u56fe\u8c31\u9a71\u52a8\u7684\u77e5\u8bc6\u8ba1\u7b97\u53ca\u53d1\u73b0\u7b49\u57fa\u7840\u7814\u7a76\uff0c\u80fd\u6709\u6548\u52a9\u529b\u5927\u6570\u636e\u65f6\u4ee3\u7684\u5730\u5b66\u79d1\u5b66\u53d1\u73b0\u3002<\/p>\n\n\n\n

\u62a5\u544a<\/strong>\u9898\u76ee: \u661f\u706b\u5927\u6a21\u578b\u6700\u65b0\u8fdb\u5c55\u53ca\u5178\u578b\u884c\u4e1a\u5e94\u7528<\/h2>\n\n\n\n

\u738b\u58eb\u8fdb\uff08\u8baf\u98de\u7814\u7a76\u9662\uff09<\/h3>\n\n\n\n
\"\"<\/figure>\n\n\n\n

\u4e13\u5bb6<\/strong>\u4ecb\u7ecd\uff1a<\/strong>
\u738b\u58eb\u8fdb\u535a\u58eb\uff0c\u6b63\u9ad8\u7ea7\u5de5\u7a0b\u5e08\uff0c\u73b0\u4efb\u8baf\u98de\u7814\u7a76\u9662\u6267\u884c\u9662\u957f\u3001\u8ba4\u77e5\u667a\u80fd\u5168\u56fd\u91cd\u70b9\u5b9e\u9a8c\u5ba4\u526f\u4e3b\u4efb\u3002\u4ed6\u4e3b\u8981\u4ece\u4e8b\u4fe1\u53f7\u5904\u7406\u3001\u5927\u6a21\u578b\u3001\u884c\u4e1a\u667a\u80fd\u7b49\u7814\u7a76\uff0c\u83b7\u56fd\u5bb6\u79d1\u6280\u8fdb\u6b65\u4e00\u7b49\u5956\u3001\u7701\u79d1\u5b66\u6280\u672f\u5956\u4e00\u7b49\u5956\u3001\u5434\u6587\u4fca\u4eba\u5de5\u667a\u80fd\u79d1\u6280\u8fdb\u6b65\u5956\u4e00\u7b49\u5956\u3001\u4e2d\u56fd\u9752\u5e74\u4e94\u56db\u5956\u7ae0\u96c6\u4f53\u3001\u4e2d\u56fd\u79d1\u534f\u6c42\u662f\u6770\u51fa\u9752\u5e74\u6210\u679c\u8f6c\u5316\u5956\uff0c\u5e26\u9886\u56e2\u961f\u83b7\u5f97\u00a0SQuAD\u3001GLUE\u3001VCR\u7b49\u5341\u51e0\u9879\u56fd\u9645\u6bd4\u8d5b\u51a0\u519b\u3002\u4ed6\u4e3b\u5bfc\u53d1\u5e03\u4e86\u884c\u4e1a\u9886\u5148\u7684\u661f\u706b\u8ba4\u77e5\u5927\u6a21\u578b\uff0c\u5e76\u6301\u7eed\u8d4b\u80fd\u591a\u4e2a\u884c\u4e1a\u5e94\u7528\u3002

\u62a5\u544a\u6458\u8981\uff1a<\/strong>
\u672c\u62a5\u544a\u9996\u5148\u63d0\u51fa\u5f53\u524d\u4ee5\u8ba4\u77e5\u5927\u6a21\u578b\u4e3a\u4ee3\u8868\u7684\u901a\u7528\u4eba\u5de5\u667a\u80fd\u6280\u672f\u5f15\u53d1\u5168\u7403\u5e7f\u6cdb\u5173\u6ce8\uff0c\u7136\u540e\u8fd8\u5206\u6790\u4e86\u4ece\u8ba4\u77e5\u5927\u6a21\u578b\u5230\u591a\u6a21\u6001\u5927\u6a21\u578b\u7684\u6280\u672f\u7279\u6027\u3001\u53d1\u5c55\u8d8b\u52bf\u53ca\u5e94\u7528\u4ef7\u503c\u3002\u5176\u6b21\uff0c\u62a5\u544a\u6c47\u62a5\u4e86\u79d1\u5927\u8baf\u98de\u7814\u53d1\u661f\u706b\u5927\u6a21\u578b\u7684\u6210\u679c\u548c\u7814\u53d1\u7ecf\u5386\uff0c\u6700\u540e\u91cd\u70b9\u4ecb\u7ecd\u4e86\u5927\u6a21\u578b\u670d\u52a1\u5178\u578b\u884c\u4e1a\u7684\u63a2\u7d22\u7ecf\u9a8c\u3002<\/p>\n\n\n\n

\u62a5\u544a<\/strong>\u9898\u76ee: Semantic Aware Machine Learning and Explanations for Knowledge Graphs<\/h2>\n\n\n\n

Claudia d’Amato\uff08\u610f\u5927\u5229\u5df4\u91cc\u5927\u5b66\uff09<\/h3>\n\n\n\n
\"\"<\/figure>\n\n\n\n

\u4e13\u5bb6<\/strong>\u4ecb\u7ecd\uff1a<\/strong>
Claudia d\u2019Amato is associate professor at the University of Bari \u2013 Computer Science Department and she got the Italian Habilitation for the functions of Full Professor for the Scientific Sector \u201c09\/H1 \u2013 Information Processing Systems\u201d (currently GDS 09\/IINF-05 \u2013 SISTEMI DI ELABORAZIONE DELLE INFORMAZIONI) on April 14th, 2021 and for the Scientific Sector \u201c01\/B1 \u2013 Informatics\u201d (currently GDS 01\/INFO-01 \u2013 INFORMATICA) on April 29th, 2021. She obtained her PhD in 2007 from the University of Bari, Italy, defending the thesis titled \u201cSimilarity Based Learning Methods for the Semantic Web. She pioneered the research on Machine Learning methods for ontology mining and Knowledge Graphs that still represents her main research interest jointly with the development of neural-symbolic and explainable solutions to be applied to Knowledge Graphs. Claudia d\u2019Amato has been also invited researcher at several universities and international research institutes such as: the University of Koblenz-Landau in 2006, 2007, 2008, 2013 working with Prof. Stefen Staab, the University of Oxford in 2012 working with Prof. Thomas Lukasiewicz, INRIA \u2013Sophia-Antipolis in 2015 working with Dr. Fabien Gandon and Prof. Andrea Tettamanzi, the University of Poznan in 2011 and 2013 working with Dr. Agnieszka Lawrynowicz, FBK in 2012 working with Dr. Luciano Serafini. She is member of the editorial board of the Transactions on Graph Data and Knowledge (TGDK) journal, the Neurosymbolic Artificial Intelligence Journal, the Semantic Web Journal and the Journal of Web Semantics. She served as General Chair for ISWC 2022, Program Chair for ISWC 2017, ESWC 2014, Vice-Chair for ISWC 2009, Journal Track chair for TheWebConf 2018 (previously WWW), Tutorial Chair for ECAI 2020, Machine Learning Track Chair for ESWC\u201912- \u201913-\u201916-\u201917 and PhD Symposium chair at ESWC\u201915-\u201921 and at ISWC\u201923. She served\/is serving as a program committee member of a number of international conferences in the area of Artificial Intelligence, Machine Learning and Semantic Web such as AAAI, IJCAI, ECAI, ECML, ISWC, TheWebConf, ESWC.

\u62a5\u544a\u6458\u8981\uff1a<\/strong>
Knowledge Graphs (KGs) are receiving increasing attention both from academia and industry, as they represent a source of structured knowledge of unprecedented dimension to be exploited in a multitude of application domains as well as research fields. Nevertheless, despite their large usage, it is well known that KGs suffer of incompleteness and noise since they often come as a result of a complex building process. As such non-negligible research efforts are currently devoted to improve the coverage and quality of existing KGs. Particularly, for the purpose numeric based Machine Learning (ML) solutions are generally adopted, given their proved ability to scale on very large KGs. Numeric-based approaches mostly focus on the graph structure and they generally consist of series of numbers without any obvious human interpretation, thus possibly affecting \u00a0the interpretability, \u00a0the explainability and sometimes the trustworthiness of the results. Nevertheless, KGs may also rely on expressive representation languages, e.g. RDFS and OWL, that are also endowed with deductive reasoning capabilities. However, both expressiveness and reasoning are most of the time disregarded by the majority of the numeric methods that have been developed, thus somehow loosing knowledge that is already available. In this talk, the role and the value added that the semantics may have for ML solutions as well as for providing explanations to tasks such as link prediction will be argued. Hence, the research directions on empowering ML and explanation solutions by injecting background knowledge will be presented jointly with the analysis of the most urgent issues that need to be solved.<\/p>\n","protected":false},"excerpt":{"rendered":"

\u62a5\u544a\u9898\u76ee: What is next for Knowledge Graphs:  Relevati […]<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"full-width-page-template.php","meta":[],"_links":{"self":[{"href":"https:\/\/sigkg.cn\/ccks-ijckg2024\/wp-json\/wp\/v2\/pages\/683"}],"collection":[{"href":"https:\/\/sigkg.cn\/ccks-ijckg2024\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/sigkg.cn\/ccks-ijckg2024\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/sigkg.cn\/ccks-ijckg2024\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/sigkg.cn\/ccks-ijckg2024\/wp-json\/wp\/v2\/comments?post=683"}],"version-history":[{"count":2,"href":"https:\/\/sigkg.cn\/ccks-ijckg2024\/wp-json\/wp\/v2\/pages\/683\/revisions"}],"predecessor-version":[{"id":729,"href":"https:\/\/sigkg.cn\/ccks-ijckg2024\/wp-json\/wp\/v2\/pages\/683\/revisions\/729"}],"wp:attachment":[{"href":"https:\/\/sigkg.cn\/ccks-ijckg2024\/wp-json\/wp\/v2\/media?parent=683"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}