DeExcelarator

About

Spreadsheet applications are one of the most used tools for content generation and presentation in the industry and the Web. In spite of this success, there does not exist a comprehensive approach to automatically extract and reuse the richness of data maintained in this format. The biggest obstacle is the lack of awareness about the structure of the data in spreadsheets. Differently from other file types, such as XML and JSON, spreadsheets do not have attached metadata describing the (structural) function of the individual units of data. This makes it difficult for machines to interpret the information maintained in these files, although the same task is rather easy for humans.

In this page, we summarize the current status of our work on table identification and layout recognition in spreadsheets. For the first stage, we have focused on discovering the layout of the data, using machine learning for classification and a set of heuristics. We work on the cell level, considering a wide range of features not covered before by related work. We evaluated the performance of our classifiers on a large dataset covering three different corpora from various domains. Finally, our work includes a novel technique for detecting and repairing incorrectly classified cells in a post-processing step. The experimental results show that our approach delivers very high accuracy bringing us a crucial step closer towards automatic information extraction from spreadsheet files.

Layout Inference

The layout inference process is defined as a series of steps, illustrated in the figure below. Initially, the application reads the spreadsheet file and extracts the features of each non-blank cell. In the next step, cells are classified with high accuracy based on their features. Finally, a post-processing step improves the quality of the results even further by applying a set of rules that are able to identify (most probably) misclassified cells and relabel them. This figure also includes the Table Reconstruction task, which forms a separate topic and is therefore left as future work.

 Classification Labels

We define five building blocks for spreadsheet tables: Headers, Attributes, Metadata, Data and Derived (see figure below). A “Header” (H) cell represents the label of a column and can be flat or hierarchical (stacked). Hierarchical structures can be also found in the left-most or right-most columns of a table, which we call “Attributes” (A), a term first introduced in (Chen 2013). Attributes can be seen as instances from the same or different (relational) dimensions placed in one or multiple columns in a way that conveys the existence of a hierarchy. We label cells as “Metadata” (M) when they provide additional information about the table as a whole or its specific sections. Examples of Metadata are the table name, creation date, and the unit for the values of a column. The remaining cells form the actual payload of the table and are labeled as “Data”. Additionally, we use the label “Derived” (B) to distinguish those cells that are aggregations of other Data cells’ values. Derived cells can have a different structure from the core Data cells, therefore we need to treat them separately.

Gold Standard

We annotated a collection of 465 Excel sheets (216 files) from three spreadsheet corpora: FUSE, ENRON, and EUSES. To accomplish this task, we developed a specialized tool for interactive annotations in Microsoft Excel spreadsheets. In the figures bellow we present statistics about these annotations.

Annotated Sheets

Since the number of annotated Data cells is overwhelmingly larger than the other classes (labels), 808,179 cells (97.6% of the total annotated cells), we decided to downsample. This not only reduces the possibility of a biased feature selection process, but also speeds up the training and validation processes. For this purpose, from the Data region of each annotated table we consider only the first, last and three random rows in between. By applying these technique the Data class was reduced to 32,905 cells. Together with the annotated cells from the other classes, the final golden standard, used for training and validation, consist of 52,970 instances.

Cell Features

The initial set consisted of 81 cell features. Here we have also included string features such as file and sheet name. We were able to retrieve them using Apache POI, which is the most complete JAVA library for working with Excel spreadsheets. Nevertheless, some of the features required a custom implementation on top of this library. Furthermore, using Weka, a well known toolkit for data mining tasks, we binarized, cleaned, and evaluated these features. The final set contains 43 features, listed in the table below.

Selected Features

We have grouped the selected features into 5 categories based on their characteristics. The content features describe the cell value, but not its format. The features in the second group (column) characterize the styling aspects of the cell except of the font, which is the subject of third group. Formulas referencing other cells are the subject of the forth group. Finally, we have defined features that describe the location (neighborhood) of the cell. The adjacent cells are referred to as “neighbors”. Note, hidden and blank cells are not counted as neighbors.

Independently of the category, Features can be numeric or boolean. We mark the numeric ones with a hashtag (#) as suffix. We use question mark (?) for boolean features.