Along with the development of information technologies such as mobile Internet,information acquisition technology,cloud computing and big data technology,the traditional knowledge engineering and knowledge-based softw...Along with the development of information technologies such as mobile Internet,information acquisition technology,cloud computing and big data technology,the traditional knowledge engineering and knowledge-based software engineering have undergone fundamental changes where the network plays an increasingly important role.Within this context,it is required to develop new methodologies as well as technical tools for network-based knowledge representation,knowledge services and knowledge engineering.Obviously,the term“network”has different meanings in different scenarios.Meanwhile,some breakthroughs in several bottleneck problems of complex networks promote the developments of the new methodologies and technical tools for network-based knowledge representation,knowledge services and knowledge engineering.This paper first reviews some recent advances on complex networks,and then,in conjunction with knowledge graph,proposes a framework of networked knowledge which models knowledge and its relationships with the perspective of complex networks.For the unique advantages of deep learning in acquiring and processing knowledge,this paper reviews its development and emphasizes the role that it played in the development of knowledge engineering.Finally,some challenges and further trends are discussed.展开更多
1 Introduction and main contributions There are two expressive sub-languages in OWL 2, where OWL 2 Full is of the highest expressivity compared with OWL 2 DL, so that its reasoning has turned undecidable. The most dis...1 Introduction and main contributions There are two expressive sub-languages in OWL 2, where OWL 2 Full is of the highest expressivity compared with OWL 2 DL, so that its reasoning has turned undecidable. The most distinctive feature of OWL 2 Full is meta-modeling, i.e., names can have multiple uses, which, unfortunately, causes reasoning undecidability in OWL 2 Full. Meta-modeling can be frequently spotted in real-word domain knowledge bases （KBs）, for example, the FMA KB for canonical human anatomy, OpenCyc and SUMO for commonsense. In these KBs, most of the names for classes or roles are also used as individuals, leading them to fall into the category of OWL 2 Full. In contrast with OWL 2 DL, reasoning in OWL 2 Full has largely been unexplored, and there are no Reasoners tailored for OWL 2 Full. The gap between meta-modeling requirement in reality and the lack of studies on reasoning and querying in OWL 2 Full raises a challenge.展开更多
基金supported in part by the National Natural Science Foundation of China(61621003,62073079,62088101,12025107,11871463,11688101)。
文摘Along with the development of information technologies such as mobile Internet,information acquisition technology,cloud computing and big data technology,the traditional knowledge engineering and knowledge-based software engineering have undergone fundamental changes where the network plays an increasingly important role.Within this context,it is required to develop new methodologies as well as technical tools for network-based knowledge representation,knowledge services and knowledge engineering.Obviously,the term“network”has different meanings in different scenarios.Meanwhile,some breakthroughs in several bottleneck problems of complex networks promote the developments of the new methodologies and technical tools for network-based knowledge representation,knowledge services and knowledge engineering.This paper first reviews some recent advances on complex networks,and then,in conjunction with knowledge graph,proposes a framework of networked knowledge which models knowledge and its relationships with the perspective of complex networks.For the unique advantages of deep learning in acquiring and processing knowledge,this paper reviews its development and emphasizes the role that it played in the development of knowledge engineering.Finally,some challenges and further trends are discussed.
基金This work was supported by the National Key Research and Development Program of China （2016YFB1000902） and the National Natural Science Foundation of China （Grant Nos. 61232015, 61621003）.
文摘1 Introduction and main contributions There are two expressive sub-languages in OWL 2, where OWL 2 Full is of the highest expressivity compared with OWL 2 DL, so that its reasoning has turned undecidable. The most distinctive feature of OWL 2 Full is meta-modeling, i.e., names can have multiple uses, which, unfortunately, causes reasoning undecidability in OWL 2 Full. Meta-modeling can be frequently spotted in real-word domain knowledge bases （KBs）, for example, the FMA KB for canonical human anatomy, OpenCyc and SUMO for commonsense. In these KBs, most of the names for classes or roles are also used as individuals, leading them to fall into the category of OWL 2 Full. In contrast with OWL 2 DL, reasoning in OWL 2 Full has largely been unexplored, and there are no Reasoners tailored for OWL 2 Full. The gap between meta-modeling requirement in reality and the lack of studies on reasoning and querying in OWL 2 Full raises a challenge.
