Name
Abstract | ||
|---|---|---|
This paper presents a knowledge representation document model, which is based on the classic information retrieval vector model. The aim of this paper is to reduce the level of complexity of the classical vector space model (VSM) and to simplify it by presenting an implementation on the plane. Furthermore, a comparison of tf-idf weighting scheme with the introduced Vector Plain Model (VPM) is presented. This method constitutes a significant tool for searching terms through auto-correlation and an innovating link between the scientific fields of information retrieval and quantitative linguistics. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1145/2910674.2910693 | PETRA |
Field | DocType | Citations |
Data mining,Divergence-from-randomness model,Knowledge representation and reasoning,Weighting,tf–idf,Information retrieval,Computer science,Document model,Search engine indexing,Vector space model,Quantitative linguistics | Conference | 0 |
PageRank | References | Authors |
0.34 | 2 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sylvia Poulimenou | 1 | 1 | 1.09 |
| Sozon Papavlasopoulos | 2 | 21 | 4.79 |
| Sarantos Kapidakis | 3 | 201 | 39.46 |
| Marios Poulos | 4 | 109 | 15.71 |