EDBT 2026 Demo / reviewers in the wild / expert
Yuxiang Zengt
dblp:352/6432
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2023
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Databases, data management, data science and information retrieval · 1 · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Databases, data mining, and information retrieval
1 paper |
Knowledge graphs · 100% | |
| Interdisciplinary, comprehensive, and emerging computing
1 paper |
Computational finance and economics · 100% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Knowledge graphs › knowledge graph construction
knowledge extraction |
0.7 | 1 | 2023 | T-FinKB: A Platform of Temporal Financial Knowledge Base Construction · ICDE 2023 |
Knowledge graphs
knowledge graph construction |
0.7 | 1 | 2023 | T-FinKB: A Platform of Temporal Financial Knowledge Base Construction · ICDE 2023 |
Computational finance and economics › financial market prediction › stock prediction
stock movement prediction |
0.2 | 1 | 2023 | T-FinKB: A Platform of Temporal Financial Knowledge Base Construction · ICDE 2023 |
Methods — techniques the papers use, named apart from their topics
record linkage · 1.3knowledge extraction · 1.3conflict resolution · 1.3
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2023 | T-FinKB: A Platform of Temporal Financial Knowledge Base ConstructionabstractIn recent years, domain-specific knowledge bases (KBs) have attracted more attention in academics and industry because of their expertise and in-depth representation in a specific domain. However, when constructing a domain-specific KB, one needs to address not only the challenges in constructing a general KB, but also the difficulties raised by the nature of domain-specific raw data. Considering the usability of financial Knowledge Bases (KBs) in many downstream applications, such as financial risk analysis and fraud detection, we aim to build a Temporal Financial KB. However, the complex, time-varying financial knowledge and the volatile financial market evolution make construction quite challenging. Thus, we propose a Platform for Temporal Financial KB Construction, T-FinKB Platform1. This platform consists of three fundamental modules, i.e., evolved knowledge extraction module, temporal record linkage and conflicts resolution module, and dynamic knowledge update module designed for financial knowledge. Generated temporal financial KBs (T-FinKBs) from T-FinKB Platform can be updated automatically. T-FinKB has been successfully applied to a downstream application, stock trend prediction with backtesting evaluation. In addition, T-FinKB Platform provides several APIs for visualization, queries and analytics. Liping Wang 0015, Hao Xin, Zhifeng Jia, Chunming Ma, Yuxiang Zengt |
ICDE | 8 |