Tables in scientific papers contain a wealth of valuable knowledge for the scientific enterprise. To help the many of us who frequently consult this type of knowledge, we present Tab2Know, a new end-toend system to build a Knowledge Base (KB) from tables in scientific papers. Tab2Know addresses the challenge of automatically interpreting the tables in papers and of disambiguating the entities that they contain. To solve these problems, we propose a pipeline that employs both statistical-based classifiers and logic-based reasoning. First, our pipeline applies weakly supervised classifiers to recognize the type of tables and columns, with the help of a data labeling system and an ontology specifically designed for our purpose. Then, logic-based reasoning is used to link equivalent entities (via sameAs links) in different tables. An empirical evaluation of our approach using a corpus of papers in the Computer Science domain has returned satisfactory performance. This suggests that ours is a promising step to create a large-scale KB of scientific knowledge.
Cables are the main load-bearing components of cable-stayed Bridges. The durability and reliability of cables are directly related to the safe operation and service life of the whole bridge. Aiming at the problem of cable detection and maintenance, a new type of cable detection robot is designed in this paper. To further enhance the stability of the robot, the structure adopts symmetrical arrangement to make the center of mass of the robot system coincide with the cable axis. According to the working condition of the robot, a flexible body spring that meets the parameter requirements is designed, which not only meets the required preload, but also meets the starting torque of the motor. The robot is simple in structure, standardized in parts, easy to assemble and disassemble, and has good obstacle clearance ability and detection efficiency.
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