TY - GEN
T1 - Answering who/when, what, how, why through constructing data graph, information graph, knowledge graph and wisdom graph
AU - Shao, Lixu
AU - Duan, Yucong
AU - Sun, Xiaobing
AU - Gao, Honghao
AU - Zhu, Donghai
AU - Miao, Weikai
PY - 2017
Y1 - 2017
N2 - Knowledge graphs have been widely adopted, in large part owing to their schema-less nature. It enables knowledge graphs to grow seamlessly and allows for new relationships and entities as needed. Natural language questions are the most intuitive way of formulating an information need. People can formulate questions to express their information needs. Natural language questions as a query language present an ideal compromise between keyword and structured querying. Questions can be used to express complex information needs that cannot be expressed as keywords without a significant loss in structure and semantics. Knowledge graph has abundant natural semantics and can contain various and more complete information. Its expression mechanism is closer to natural language. We propose to clarify the expression of knowledge graph as a whole.We use knowledge graph to solve the Five Ws problems respectively which are guided by interrogative words such as who/when, what, how and why. We also propose to specify knowledge graph in a progressive manner as four basic forms including data graph, information graph, knowledge graph and wisdom graph.
AB - Knowledge graphs have been widely adopted, in large part owing to their schema-less nature. It enables knowledge graphs to grow seamlessly and allows for new relationships and entities as needed. Natural language questions are the most intuitive way of formulating an information need. People can formulate questions to express their information needs. Natural language questions as a query language present an ideal compromise between keyword and structured querying. Questions can be used to express complex information needs that cannot be expressed as keywords without a significant loss in structure and semantics. Knowledge graph has abundant natural semantics and can contain various and more complete information. Its expression mechanism is closer to natural language. We propose to clarify the expression of knowledge graph as a whole.We use knowledge graph to solve the Five Ws problems respectively which are guided by interrogative words such as who/when, what, how and why. We also propose to specify knowledge graph in a progressive manner as four basic forms including data graph, information graph, knowledge graph and wisdom graph.
UR - https://www.scopus.com/pages/publications/85029492259
U2 - 10.18293/SEKE2017-079
DO - 10.18293/SEKE2017-079
M3 - 会议稿件
AN - SCOPUS:85029492259
T3 - Proceedings of the International Conference on Software Engineering and Knowledge Engineering, SEKE
SP - 1
EP - 6
BT - Proceedings - SEKE 2017
PB - Knowledge Systems Institute Graduate School
T2 - 29th International Conference on Software Engineering and Knowledge Engineering, SEKE 2017
Y2 - 5 July 2017 through 7 July 2017
ER -