# Development of a Framework to Understand Tables in Engineering Specification Documents
## 1 Introduction
If tables in engineering specifications are handled separately from other texts, and if a computer can understand logical relationships among data in a table, engineers get benefits on searching product specification from engineering documents. Table understanding means recovering relationships among data values, labels (attributes), and domains (dimensions) in the table [1]. As defined in [2], the understanding table involves five sub-problems; (1) table detection (to detect positions of a table inside a source), (2) table recognition (to recover individual cells), (3) table functional analysis (to find labels and data in cells), (4) table structural analysis (to learn relationships between cells), and (5) table interpretation (to learn facts from a table). The proposed framework focused on the last four sub-problems except for the first sub-problem. Algorithms for table detection are heavily affected by the types of electrical formats of engineering documents [3], but the first problem becomes rather a simple issue if lines of a table fully exist and only well formatted text exists. Most engineering specification tables have solid lines and well formatted text for engineers to easily understand tables and to distinguish tables from other drawing images or descriptions of products. Once a table is detected with table's boundary lines, table recognition to find individual cells also becomes simple to find sub-tables from a complex table. Many previous works tackled the functional analysis and structural analysis of tables. Rule-based approach [4; 5; 6], classification approach [7], deep learning approach [8], and natural language processing approach [9] were applied to the functional analysis and structural analysis of tables. Most of the tables in engineering specification documents do not have a highly complex structure, but their structures vary depending on the context of product specifications. An engineering table understanding framework was proposed in this paper, and it consisted of three steps: (1) Identification of minimal tables, (2) Classification of cells into labels and data, (3) Extending a domain knowledge map. The first step is pre-processing and functional analysis of tables. The XY-tree algorithm was adopted to find sub-tables in a complex engineering table. The second step is structural analysis of table, which used a neural network algorithm to classify cells into labels and data. The last step is interpretation of tables using rule-based ontology extending algorithm. Ontology and rule-based approaches were adopted for the table interpretation like previous works [2; 9; 10], but the proposed approach incorporated natural language processing of the terms in tables with rule-based approach to extend a domain ontology. This paper is organized as follows. Previous works are explained in Section 2. Section 3 presents an overview of the proposed framework to understand tables in engineering specification documents. Section 4, Section 5, and Section 6, respectively, describe each step in the proposed framework. Each step is illustrated with table understanding examples. Finally, the conclusions and future works are presented in Section 7.