# A Novel Approach to Data Extraction on Hyperlinked Webpages
###### Contents  
* 1 Introduction
* 2.1 Introduction Over the years, the World Wide Web (WWW) has gained significant popularity and is presently reckoned to be a treasure trove of information. The plethora of this information takes the form of images, text, audios, videos, etc. The major portion of this information is freely available which benefits the users ingress to the required information coherently. A user poses a search query on a web-browser and acquires a list of possibly related uniform resource locators (URLs) against keyword(s). A tabular representation of data/information on the web is considered more effective and precise than non-tabular. This representation is a two-dimensional representation of facts. Generally, there are two types of web tables, (i) simple and (ii) complex. The simple structure tables contain a header row followed by one or more data rows, whereas complexly structured tables can contain a title, group header, data, and header rows. The number of available tables on the web ranges from hundreds of thousands to millions [1]. This data in the form of tables is brief most of the time, yet is very rich in information. Tables are designed for knowledge management, information retrieval, web mining, summarization, and content delivery to mobile devices [2; 3]. The pivot of existing state-of-the-art schema extraction techniques is the transformation of web tables into a relational schema to efficiently retrieve the relevant information against the search query. The contemporary state-of-the-art schema extraction techniques for tabular data played a vital role in enhancing the schema extraction process. These techniques support the conversion of web tables and spreadsheets into a relational database, because of their usefulness for querying data by using simple SQL statements. Furthermore, the schema extraction techniques proposed so far only handle the tables with simple structures, leaving millions of tables with complex structures untouched. None of the techniques proposed has contemplated the conversion of linked web tables into combined, relational schema to enable an advanced queries application. The hyper linkage between two tables on two web pages makes them known as a linked web table. The schema of linked web tables can guarantee maximum relevant information retrieval against the query based on the assumption that the base table linked to another table holds further information regarding the base table. None of the published articles focused on the linkage of two web tables to enable an enhanced query application and development of schema for linked webpages. This paper is an attempt to overcome that deficiency by identifying, processing, and converting linked web tables into relational schema by using primary/foreign key relationships. Our research was aimed at extending the Cafarella Cafarella (1999); [PERSON] and [PERSON] (2000); [PERSON] (2001); [PERSON] and Cafarella (2002) approach named WebTable, by considering the complex table structures. Our technique handles the schema extraction of complex table structures that are available in the millions on the web. We have developed our in-house web crawler to download web pages. We have targeted diversified websites to formulate the dataset. The websites included Wikipedia tables, faculty profiles, finance and commerce transactional tables, actor profiles, and different statistical data. Our algorithm further detects the table tags from the HTML code. Conditional random fields (CRF) have been adopted to identify the header row and return a sequence of row labels. The row labels were fed to our designed automaton model to handle the complex structures by identifying the header row with significant accuracy. Moreover, the linkage between two web tables was identified and primary/foreign keys were established. The individual schema of linked web tables was converted into a relational schema to scrutinize the potential of the combined schema in terms of advanced query application (i.e., SQL join operation).