Abstract
Information extraction (IE) systems can make the text online become more available to be accessed. Some IE applications need a separate set of rules tuned to the domain and the writing styles. In particular, rules created without carefully learning are too restricted to be reused in other web sties. Even in the same web site rules cannot be reused when the writing style is changed slightly. The Personal Navigating Agent (PNA) and Tabular Information Extraction (TIE) systems overcome the writing style restricts. Both systems are adaptive to extract information for one domain from different web sites with just one set of rules or knowledge. PNA is designed to follow the guide of domain knowledge to search information thru navigating related web pages as a person. It extracts information in one web page. When it recognized a hyperlink, it can navigate to the page pointed by this hyperlink and then extract information again after recognizing that the related information can be found in that page. PNA uses the most common pattern matching technique that is used by most IE systems to extract information from a single text input. It also uses advance technique such HTML structure analysis to associate extracted data. There also exist some problems when we use common techniques to extract information from tables. Some IE systems cannot extract slots shared by several case frames or text cannot be divided into segments of case frames. Two-dimensional tables are in such cases and there are other kinds of tables. TIE is designed to extract information just from tables. As same as PNA, TIE is also a domain-dependent and web-site-independent system. Moreover, the semantic templates and navigating maps in PNA are merged in TIE as knowledge maps with highly structured and the characteristic of reuse. A generic framework is also designed in TIE to identify and extract information for database queries. The knowledge map is the main power of TIE to extract information from tabular documents for each domain. A knowledge map is a concept model that describes the structure of objects in a domain. For tables, it describes the attributes a case frame may have. For attributes, it also describes the rules to recognize their labels and entry values. It allows an attribute to define its sub-attributes. It also allows attributes defined hierarchically. Based on the knowledge map, we can apply the information tagging for all cells in a table. The second power of TIE is a set of rules to recognize table layouts after the process of information tagging. These rules also define a semantic preserved transition to output a new table in form of relational database tables. These rules are domain independent and can be used to all tables. These rules are defined with layout description syntax that can describe tables in a concept model. Based these rules, we can find the best-fit layout and remove ambiguity. After the layout is recognized, we can transfer the input table into a relational database table. To extract case frames from such layout is as simple as to extract records from a relational database table.