Name
Abstract | ||
|---|---|---|
•Building fragments of Bayesian networks (BNs) from unstructured data is an important step towards automatic knowledge extraction.•Identifying connections between concepts is useful to automatically build knowledge for supporting decision making.•Extraction, classification and aggregation of probabilistic information is crucial in modelling BNs.•We introduce a systematic way to extract fragments of BNs from text, based on grammar, lexical properties and on topological features. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1016/j.asoc.2017.07.009 | Applied Soft Computing |
Keywords | Field | DocType |
Text mining,Network theory,Bayesian networks | Data mining,Computer science,Unstructured data,Bayesian network,Dynamic data,Knowledge engineering,Artificial intelligence,Knowledge extraction,Statistical model,Big data,Decision-making,Machine learning | Journal |
Volume | ISSN | Citations |
60 | 1568-4946 | 4 |
PageRank | References | Authors |
0.50 | 12 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Marcello Trovati | 1 | 68 | 17.35 |
| Jer Hayes | 2 | 281 | 15.32 |
| Francesco Palmieri | 3 | 1713 | 182.92 |
| Bessis, N. | 4 | 806 | 88.01 |