Knowledge-based Artificial Intelligence

A recent interview with a noted researcher, IEEE Fellow Michael I. Jordan, Pehong Chen Distinguished Professor at the University of California, Berkeley, provided a downplayed view of recent AI hype. Jordan was particularly critical of AI metaphors to real brain function and took the air out of the balloon about algorithm advances, pointing out that most current methods have roots that are decades long [1]. In fact, the roots of knowledge-based artificial intelligence (KBAI), the subject of this article, also extend back decades.

[1]  Gerhard Weikum,et al.  WWW 2007 / Track: Semantic Web Session: Ontologies ABSTRACT YAGO: A Core of Semantic Knowledge , 2022 .

[2]  Victor R. Lesser,et al.  A Multi-Level Organization For Problem Solving Using Many, Diverse, Cooperating Sources Of Knowledge , 1975, IJCAI.

[3]  Praveen Paritosh,et al.  Freebase: a collaboratively created graph database for structuring human knowledge , 2008, SIGMOD Conference.

[4]  Li Fei-Fei,et al.  ImageNet: A large-scale hierarchical image database , 2009, CVPR.

[5]  Catherine Havasi,et al.  Representing General Relational Knowledge in ConceptNet 5 , 2012, LREC.

[6]  Simone Paolo Ponzetto,et al.  Deriving a Large-Scale Taxonomy from Wikipedia , 2007, AAAI.

[7]  Gerhard Weikum,et al.  Knowledge Bases in the Age of Big Data Analytics , 2014, Proc. VLDB Endow..

[8]  Jens Lehmann,et al.  DBpedia: A Nucleus for a Web of Open Data , 2007, ISWC/ASWC.

[9]  Doug Downey,et al.  Unsupervised named-entity extraction from the Web: An experimental study , 2005, Artif. Intell..

[10]  Gerhard Weikum,et al.  YAGO2: A Spatially and Temporally Enhanced Knowledge Base from Wikipedia: Extended Abstract , 2013, IJCAI.

[11]  Simone Paolo Ponzetto,et al.  Collaboratively built semi-structured content and Artificial Intelligence: The story so far , 2013, Artif. Intell..

[12]  Haixun Wang,et al.  Probase: a probabilistic taxonomy for text understanding , 2012, SIGMOD Conference.

[13]  S. M. García,et al.  2014: , 2020, A Party for Lazarus.

[14]  Franz Josef Och Statistical Machine Translation: Foundations and Recent Advances , 2005, MTSUMMIT.

[15]  Simone Paolo Ponzetto,et al.  BabelNet: The automatic construction, evaluation and application of a wide-coverage multilingual semantic network , 2012, Artif. Intell..

[16]  Jimmy J. Lin,et al.  Large-scale machine learning at twitter , 2012, SIGMOD Conference.

[17]  Ali Farhadi,et al.  Learning Everything about Anything: Webly-Supervised Visual Concept Learning , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[18]  Rahul Gupta,et al.  Biperpedia: An Ontology for Search Applications , 2014, Proc. VLDB Endow..

[19]  Peter Norvig,et al.  The Unreasonable Effectiveness of Data , 2009, IEEE Intelligent Systems.

[20]  Ramanathan V. Guha,et al.  Building Large Knowledge-Based Systems: Representation and Inference in the Cyc Project , 1990 .

[21]  Wei Zhang,et al.  Knowledge vault: a web-scale approach to probabilistic knowledge fusion , 2014, KDD.

[22]  Fei-Fei Li,et al.  ImageNet: A large-scale hierarchical image database , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[23]  Markus Krötzsch,et al.  Wikidata , 2014, Commun. ACM.

[24]  Christopher Ré,et al.  DeepDive: Web-scale Knowledge-base Construction using Statistical Learning and Inference , 2012, VLDS.

[25]  Michael Strube,et al.  Transforming Wikipedia into a large scale multilingual concept network , 2013, Artif. Intell..

[26]  Jean Roy,et al.  Concepts, Models, and Tools for Information Fusion , 2007 .

[27]  Wei-Ying Ma,et al.  Statistical Entity Extraction From the Web , 2012, Proceedings of the IEEE.

[28]  Estevam R. Hruschka,et al.  Toward an Architecture for Never-Ending Language Learning , 2010, AAAI.