Name
Abstract | ||
|---|---|---|
Keyword search has become one of hot topics in the field of information retrieval. It can provide users a simple and friendly interface. But the efficiency of some existing keyword search algorithms is low and there are some draws in sorting results. Most algorithms are suited for either unstructured data or structured data. This paper proposes a new kind of top-k keyword search algorithm. No matter the data is unstructured, semi-structured or structured, the algorithm is always effective. It introduces the concept of neighbor sets of nodes and uses set join algorithm to narrow the search space. We also propose the definition of classified Steiner tree, which can reduce the draw phenomenon in results. In addition, the algorithms can output the results of the classified Steiner tree at the same time of computing them. So it can reduce the waiting time of the users and improve the efficiency of keywords search. © 2012 Springer-Verlag. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1007/978-3-642-33050-6_27 | WAIM Workshops |
Keywords | Field | DocType |
classified steiner tree,keyword search,neighbor sets of nodes,top-k | Data mining,Search algorithm,Computer science,Steiner tree problem,Keyword search,Algorithm,Beam search,Unstructured data,Theoretical computer science,Sorting,Data model | Conference |
Volume | Issue | ISSN |
7419 LNCS | null | 16113349 |
Citations | PageRank | References |
0 | 0.34 | 14 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yan Yang | 1 | 19 | 11.40 |
| Mingzhu Tang | 2 | 0 | 0.34 |
| Yingli Zhong | 3 | 3 | 1.75 |
| Zhaogong Zhang | 4 | 22 | 2.15 |
| Longjiang Guo | 5 | 177 | 26.73 |