Retrieving and organizing web pages by “information unit”

Effective XML Keyword Search with Relevance Oriented Ranking

“…query q by exactly following Formula 9(b). ρ s (q, a) is computed by a sum of the product of the similarity of each of its child c and the confidence value of c as a search via node (line [11][12][13][14]. Finally, ρ s (q, a) is normalized by a factor W q a (line 15), which is the weight of internal node a w.r.t.…”

“…Li et al [11] show the reduction from minimal reduced tree problem to the NP-complete Group Steiner Tree problem on graphs. BANKS [12] uses bidirectional expansion heuristic algorithms to search as small portion of graph as possible.…”

An Effective Object-Level XML Keyword Search

“…However, it suffers the same problem as those in tree model, as both of them exploit only the structure of XML data. Even worse, the problem of finding the results by increasing the sizes of reduced subtrees for keyword proximity is NP-hard [14], thus keyword search in digraph data model is heuristics-based and intrinsically expensive.…”

“…each G is the smallest subgraph containing all keywords. However, the cost of finding all such G ranked by size is intrinsically expensive due to its NP-hard nature [14]. Bidirectional expansion is proposed to find ranked reduced subtrees [12], but it requires the entire visited graph in memory, and suffers an inefficiency.…”

Ranked Search on Data Graphs

“…Likewise, a relational database can be viewed as a graph where tuples are modeled as vertices connected via foreign-key relationships [BNH+02], and a XML database can be represented as a graph with XML elements as nodes and containment or ID-IDREF edges as hyperlinks [GSBS03,CMKS03]. Keyword search querying has emerged as one of the most effective paradigms for information discovery, especially over HTML documents in the World Wide Web [PBMW98,Kle99,LCVA01]. One of the key advantages of keyword search querying is its simplicityusers do not have to learn a complex query language, and can issue queries without any prior knowledge about the structure of the underlying data.…”