Abstract: A mechanism to classify source documents into one of two categories, either likely to contain desired information or unlikely to contain desired information. Generally some form of rules based classification in conjunction with deeper analysis using advanced techniques on difficult cases is utilized. The rules based classification is generally good for eliminating cases from further consideration and for identifying documents of interest based on generally discernable relationships between data or based on the presence or absence of data. The deeper analysis is used to uncover more complex relationships between data that may identify documents of interest. Portions of the process may use the entire document while other portions of the process may use only a portion of the document.

Abstract: A method and system for ranking objects of different object types based on their popularity is provided. A ranking system calculates the popularity of objects based on relationships between the objects. A relationship indicates how one object is related to another object. Thus, objects of one object type may have one or more relationships with objects of another object type. One goal of the ranking system is to rank the objects of the different object types based on their popularity. The objects and their relationships can be represented using a graph with nodes representing objects and links representing relationships between objects. The ranking system assigns a popularity propagation factor to each relationship to represent its contribution to the popularity of objects of that type.

Abstract: A method and system for identifying object information of an information page is provided. An information extraction system identifies the object blocks of an information page. The extraction system classifies the object blocks into object types. Each object type has associated attributes that define a schema for the information of the object type. The extraction system identifies object elements within an object block that may represent an attribute value for the object. After the object elements are identified, the extraction system attempts to identify which object elements correspond to which attributes of the object type in a process referred to as “labeling.” The extraction system uses an algorithm to determine the confidence that a certain object element corresponds to a certain attribute. The extraction system then selects the set of labels with the highest confidence as being the labels for the object elements.