Jens Graupmann

COMPASS: a concept-based web search engine for HTML, XML, and deep web data

This chapter introduces a concept-based Web search engine for HTML, XML, and deep Web data—Context-Oriented Multi-Format Portal-Aware Search System (COMPASS). It also presents the features and architectures of COMPASS. The internal query language of COMPASS resembles a highly simplified version of mainstream languages such as SQL, XPath, or XQuery. Search conditions refer to concepts and values that correspond to element names and contents in an XML setting, and attribute names and values in a SQL setting. COMPASS uses a centralized data index for efficient search evaluation. All data and also the relationships among documents are represented in a relational database. All data formats are transformed into XML by using heuristics as well as external annotation tools such as GATE.

From focused crawling to expert information: an application framework for web exploration and portal generation

Publisher Summary Focused crawling is a relatively new, promising approach to improving the recall of expert search on the Web. It typically starts from a user- or community specific tree of topics along with a few training documents for each tree node, and then crawls the Web with focus on these topics of interest. This process can efficiently build a theme-specific, hierarchical directory whose nodes are populated with relevant high-quality documents for expert Web search. The BINGO! focused crawler implements an approach that aims to overcome the limitations of the initial training data. BINGO! identifies, among the crawled and positively classified documents of a topic, characteristic archetypes and uses them for periodically retraining the classifier. This way the crawler is dynamically adapted based on the most significant documents seen so far.

Concept-Based search on semi-structured data exploiting mined semantic relations

In this paper we show the current state of the ongoing research concerning our prototype for a search engine on semi-structured data incorporating rules mined on extracted structured data We illuminate some ideas from the research field of data mining and how to apply them to the retrieval process Additionally, we show technical aspects and features of our search engine.