Demo Data from EDBT 2026 / reviewers in the wild / expert
Huafei Yang
dblp:57/1521
· DBLP profile ↗
2ranked-venue papers
0as first author
0since 2021 · last 2009
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 2
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.
| Artificial intelligence
1 paper |
Knowledge representation and reasoning · 100% |
Topics — the 1 heaviest of 1, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Knowledge, reasoning and agents › Knowledge representation and reasoning
knowledge-based systems |
0.1 | 1 | 2009 | A Novel Knowledge-Based System for Interpreting Complex Engineering Drawings: Theory, Representation, and Implementation · IEEE Trans. Pattern Anal. Mach. Intell. 2009 |
Methods — techniques the papers use, named apart from their topics
depth-first search · 0.1EBNF parsing · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2009 | A Novel Knowledge-Based System for Interpreting Complex Engineering Drawings: Theory, Representation, and ImplementationabstractWe present a novel knowledge-based system to automatically convert real-life engineering drawings to content-oriented high-level descriptions. The proposed method essentially turns the complex interpretation process into two parts: knowledge representation and knowledge-based interpretation. We propose a new hierarchical descriptor-based knowledge representation method to organize the various types of engineering objects and their complex high-level relations. The descriptors are defined using an Extended Backus Naur Form (EBNF), facilitating modification and maintenance. When interpreting a set of related engineering drawings, the knowledge-based interpretation system first constructs an EBNF-tree from the knowledge representation file, then searches for potential engineering objects guided by a depth-first order of the nodes in the EBNF-tree. Experimental results and comparisons with other interpretation systems demonstrate that our knowledge-based system is accurate and robust for high-level interpretation of complex real-life engineering projects. Tong Lu 0002, Chiew-Lan Tai, Huafei Yang, Shijie Cai |
IEEE Trans. Pattern Anal. Mach. Intell. | 3 |
| 2007 | Automatic analysis and integration of architectural drawings
Huafei Yang, Ruoyu Yang, Shijie Cai |
Int. J. Document Anal. Recognit. | 2 |