RDF Graph Data Research Articles

Author-Driven Computable Data and Ontology Production for Taxonomists

Author-Driven Computable Data and Ontology Production for Taxonomists

Innovative Application of Computer Technology in Engineering Management

With the rapid development of China’s economy, computers have been well developed at a certain level. In most of our project management process, if we can effectively use computer technology, to a certain extent, we can improve the quality and efficiency of the project. Based on this, this paper studies the innovative application of computer technology in engineering management. In this paper, an engineering team is selected as the research object. Based on the theory of computer technology and RDF graph data flow division algorithm, the engineering resource utilization rate, project management efficiency and engineering difficulty of the engineering team before and after the application of computer technology for project management are compared. The research results show that before the application of computer technology for project management, the resource utilization rate of engineering team is 72.5%, the project management efficiency is 61.6%, and the engineering construction difficulty is 76.3%. After the application of computer technology to project management, the data have changed, the resource utilization rate has increased to 88.3%, the pipeline efficiency has increased to 79.3%, and the engineering difficulty has been reduced to It can be seen from the data changes that the same engineering team has changed significantly before and after the application of computer technology in project management, which not only saves engineering resources, but also effectively improves the engineering efficiency.

A principled approach to bridging the gap between graph data and their schemas

Although RDF graph data often come with an associated schema, recent studies have proven that real RDF data rarely conform to their perceived schemas. Since a number of data management decisions, including storage layouts, indexing, and efficient query processing, use schemas to guide the decision making, it is imperative to have an accurate description of the structuredness of the data at hand (how well the data conform to the schema). In this paper, we have approached the study of the structuredness of an RDF graph in a principled way: we propose a framework for specifying structuredness functions, which gauge the degree to which an RDF graph conforms to a schema. In particular, we first define a formal language for specifying structuredness functions with expressions we call rules. This language allows a user to state a rule to which an RDF graph may fully or partially conform. Then we consider the issue of discovering a refinement of a sort (type) by partitioning the dataset into subsets whose structuredness is over a specified threshold. In particular, we prove that the natural decision problem associated to this refinement problem is NP-complete, and we provide a natural translation of this problem into Integer Linear Programming (ILP). Finally, we test this ILP solution with three real world datasets and three different and intuitive rules, which gauge the structuredness in different ways. We show that the rules give meaningful refinements of the datasets, showing that our language can be a powerful tool for understanding the structure of RDF data, and we show that the ILP solution is practical for a large fraction of existing data.