Name
Abstract | ||
|---|---|---|
We enhance an existing search engine's snippet (i.e. excerpt from a web page determined at query-time in order to efficiently express how the web page may be relevant to the query) with linked data (LD) in order to highlight non trivial relationships between the information need of the user and LD resources related to the result page. To do this, we introduce a multi-step unsupervised co-clustering algorithm so as to use the textual data associated with the resources for discovering additional relationships. Next, we use a 3-way tensor to mix these new relationships with the ones available from the LD graph. Then, we apply a first PARAFAC tensor decomposition [5] in order to (i) select the most promising nodes for a 1-hop extension, and (ii) build the enhanced snippet. A video demonstration is available online (http://liris.cnrs.fr/drim/projects/ensen/). |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1007/978-3-319-11955-7_34 | Lecture Notes in Computer Science |
Keywords | Field | DocType |
Linked data,Information retrieval,Snippets,Co-Clustering,Tensor decomposition | Data mining,Graph,Information needs,Search engine,Information retrieval,Web page,Computer science,Linked data,Biclustering,Snippet,Tensor decomposition | Conference |
Volume | ISSN | Citations |
8798 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 10 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mazen Alsarem | 1 | 2 | 1.77 |
| Pierre-edouard Portier | 2 | 33 | 7.54 |
| Sylvie Calabretto | 3 | 106 | 52.73 |
| Harald Kosch | 4 | 775 | 116.64 |