TY - GEN
T1 - Knowledge Representation and Rule Mining in Entity-Centric Knowledge Bases
AU - Suchanek, Fabian M.
AU - Lajus, Jonathan
AU - Boschin, Armand
AU - Weikum, Gerhard
N1 - Publisher Copyright:
© 2019, Springer Nature Switzerland AG.
PY - 2019/1/1
Y1 - 2019/1/1
N2 - Entity-centric knowledge bases are large collections of facts about entities of public interest, such as countries, politicians, or movies. They find applications in search engines, chatbots, and semantic data mining systems. In this paper, we first discuss the knowledge representation that has emerged as a pragmatic consensus in the research community of entity-centric knowledge bases. Then, we describe how these knowledge bases can be mined for logical rules. Finally, we discuss how entities can be represented alternatively as vectors in a vector space, by help of neural networks.
AB - Entity-centric knowledge bases are large collections of facts about entities of public interest, such as countries, politicians, or movies. They find applications in search engines, chatbots, and semantic data mining systems. In this paper, we first discuss the knowledge representation that has emerged as a pragmatic consensus in the research community of entity-centric knowledge bases. Then, we describe how these knowledge bases can be mined for logical rules. Finally, we discuss how entities can be represented alternatively as vectors in a vector space, by help of neural networks.
U2 - 10.1007/978-3-030-31423-1_4
DO - 10.1007/978-3-030-31423-1_4
M3 - Conference contribution
AN - SCOPUS:85081365252
SN - 9783030314224
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 110
EP - 152
BT - Reasoning Web. Explainable Artificial Intelligence - 15th International Summer School 2019, Tutorial Lectures
A2 - Krötzsch, Markus
A2 - Stepanova, Daria
PB - Springer
T2 - 15th Reasoning Web Summer School, RW 2019
Y2 - 20 September 2019 through 24 September 2019
ER -