Name
Abstract | ||
|---|---|---|
Information retrieval on the social web moves from a pure term-frequency-based approach to an enhanced method that includes conceptual multimodal features on a semantic level. In this paper, we present an approach for semantic-based keyword search and focus especially on its optimization to scale it to real-world sized collections in the social media domain. Furthermore, we present a faceted indexing framework and architecture that relates content to semantic concepts to be indexed and searched semantically. We study the use of textual concepts in a social media domain and observe a significant improvement from using a concept-based solution for keyword searching. We address the problem of time-complexity that is critical issue for concept-based methods by focusing on optimization to enable larger and more real-world style applications. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/978-3-319-27932-9_13 | Trans. Computational Collective Intelligence |
Keywords | DocType | Volume |
Semantic indexing,Concept,Social web,Word2Vec | Journal | 26 |
ISSN | ISBN | Citations |
0302-9743 | 978-3-319-27931-2 | 0 |
PageRank | References | Authors |
0.34 | 17 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Navid Rekabsaz | 1 | 32 | 8.40 |
| Ralf Bierig | 2 | 201 | 14.65 |
| Mihai Lupu | 3 | 0 | 0.34 |
| Allan Hanbury | 4 | 1756 | 144.05 |