ABSTRACT Spreadsheets contain a lot of valuable data and have many practical applications. The key technology of these practical applications is how to make machines understand the semantic structure of spreadsheets, e.g., identifying cell function types and discovering relationships between cell pairs. Most existing methods for understanding the semantic structure of spreadsheets do not make use of the semantic information of cells. A few studies do, but they ignore the layout structure information of spreadsheets, which affects the performance of cell function classification and the discovery of different relationship types of cell pairs. In this paper, we propose a Heuristic algorithm for Understanding the Semantic Structure of spreadsheets (HUSS). Specifically, for improving the cell function classification, we propose an error correction mechanism (ECM) based on an existing cell function classification model [ 11 ] and the layout features of spreadsheets. For improving the table structure analysis, we propose five types of heuristic rules to extract four different types of cell pairs, based on the cell style and spatial location information. Our experimental results on five real-world datasets demonstrate that HUSS can effectively understand the semantic structure of spreadsheets and outperforms corresponding baselines.

As from time to time it is impractical to ask agents to provide linear orders over all alternatives, for these partial rankings it is necessary to conduct preference completion. Specifically, the personalized preference of each agent over all the alternatives can be estimated with partial rankings from neighboring agents over subsets of alternatives. However, since the agents' rankings are nondeterministic, where they may provide rankings with noise, it is necessary and important to conduct the certainty-based preference completion. Hence, in this paper firstly, for alternative pairs with the obtained ranking set, a bijection has been built from the ranking space to the preference space, and the certainty and conflict of alternative pairs have been evaluated with a well-built statistical measurement Probability-Certainty Density Function on subjective probability, respectively. Then, a certainty-based voting algorithm based on certainty and conflict has been taken to conduct the certainty-based preference completion. Moreover, the properties of the proposed certainty and conflict have been studied empirically, and the proposed approach on certainty-based preference completion for partial rankings has been experimentally validated compared to state-of-arts approaches with several datasets.