Name
Title | ||
|---|---|---|
A Deep Learning-Based Recommendation System To Enable End User Access To Financial Linked Knowledge |
Abstract | ||
|---|---|---|
Motivated by the assumption that Semantic Web technologies, especially those underlying the Linked Data paradigm, are not sufficiently exploited in the field of financial information management towards the automatic discovery and synthesis of knowledge, an architecture for a knowledge base for the financial domain in the Linked Open Data (LOD) cloud is presented in this paper. Furthermore, from the assumption that recommendation systems can be used to make consumption of the huge amounts of financial data in the LOD cloud more efficient and effective, we propose a deep learning-based hybrid recommendation system to enable end user access to the knowledge base. We implemented a prototype of a knowledge base for financial news as a proof of concept. Results from an Information Systems-oriented validation confirm our assumptions. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/978-3-319-92639-1_1 | HYBRID ARTIFICIAL INTELLIGENT SYSTEMS (HAIS 2018) |
Keywords | Field | DocType |
Linked Open Data, Knowledge base, Ontology, Deep learning, Collaborative filtering, Content-based recommendation | Recommender system,Collaborative filtering,End user,Computer science,Semantic Web,Linked data,Proof of concept,Knowledge base,Finance,Cloud computing | Conference |
Volume | ISSN | Citations |
10870 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 17 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Luis Omar Colombo-Mendoza | 1 | 27 | 4.28 |
| José Antonio García-Díaz | 2 | 0 | 1.35 |
| Juan Miguel Gómez Berbís | 3 | 184 | 13.15 |
| Rafael Valencia-garcía | 4 | 630 | 54.42 |