Demo Data from EDBT 2026 / reviewers in the wild / expert
Samuel Y. E. Lim
dblp:79/2848
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 1998
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1Systems, architecture and hardware · 1
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 2 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Knowledge, reasoning and agents › Knowledge representation and reasoning › uncertainty reasoning
fuzzy reasoning |
0.0 | 1 | 1998 | Integration of Knowledge-Based Systems and Neural Networks: Neuro-Expert Petri Net Models and Applications · ICRA 1998 |
Knowledge, reasoning and agents › Knowledge representation and reasoning
knowledge-based systems |
0.0 | 1 | 1998 | Integration of Knowledge-Based Systems and Neural Networks: Neuro-Expert Petri Net Models and Applications · ICRA 1998 |
Methods — techniques the papers use, named apart from their topics
fuzzy logic · 0.0expert petri net · 0.0artificial neural network · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 1998 | Integration of Knowledge-Based Systems and Neural Networks: Neuro-Expert Petri Net Models and ApplicationsabstractThis paper identifies and describes how knowledge based systems (KBSs), fuzzy logic and artificial neural networks (ANNs) can be integrated, and provides a novel (fuzzy) expert Petri net (EPN (FEPN)) based approach for the integration of KBSs and ANNs. A generic expert Petri net model of single neuron is presented, and a two-layer generic Petri net model for (fuzzy) neural networks and neural (fuzzy) expert Petri nets (NEPN, NFEPN)) that use this neuron model as a building block is described. The NEPN and NFEPN models can be used for representing a (fuzzy) knowledge base and (fuzzy) reasoning. Also, they can be utilized to develop ANN-like multilayered Petri net architectures of distributed hybrid intelligence having learning ability. Some application examples are illustrated for validation of the proposed models. Xuan F. Zha, Samuel Y. E. Lim, Sai Cheong Fok |
ICRA | 2 |