Information Discovery on Graph-Structured Databases
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Graph-structured databases are widely prevalent, and the problem of effective search and retrieval from such graphs has been receiving much attention recently. For example, the Web can be naturally viewed as a graph. Likewise, a relational database can be viewed as a graph where tuples are modeled as vertices connected via foreign-key relationships, and a XML database can be represented as a graph with XML elements as nodes and containment or ID-IDREF edges as hyperlinks. Keyword search querying has emerged as one of the most effective paradigms for information discovery, especially over HTML ...
Graph-structured databases are widely prevalent, and the problem of effective search and retrieval from such graphs has been receiving much attention recently. For example, the Web can be naturally viewed as a graph. Likewise, a relational database can be viewed as a graph where tuples are modeled as vertices connected via foreign-key relationships, and a XML database can be represented as a graph with XML elements as nodes and containment or ID-IDREF edges as hyperlinks. Keyword search querying has emerged as one of the most effective paradigms for information discovery, especially over HTML documents in the World Wide Web. One of the key advantages of keyword search querying is its simplicity - users do not have to learn a complex query language, and can issue queries without any prior knowledge about the structure of the underlying data. Since the keyword search query interface is very flexible, queries may not always be precise and can potentially return a large number of query results, especially in large document collections. Consequently, an important requirement for keyword search is to rank the query result. The goal of this book is to present various search techniques.
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