Demo Data from EDBT 2026 / reviewers in the wild / expert
Katsuhiro Mayama
dblp:117/2152
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2012
—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% | |
| Human-computer interaction and pervasive computing
1 paper |
Human-robot interaction · 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 › commonsense reasoning
commonsense knowledge |
0.1 | 1 | 2012 | Evaluation of commonsense knowledge for intuitive robotic service · ICRA 2012 |
Knowledge, reasoning and agents › Knowledge representation and reasoning › knowledge management
knowledge evaluation |
0.1 | 1 | 2012 | Evaluation of commonsense knowledge for intuitive robotic service · ICRA 2012 |
Methods — techniques the papers use, named apart from their topics
weighting mechanism · 0.3questionnaire · 0.3
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2012 | Evaluation of commonsense knowledge for intuitive robotic serviceabstractHuman commonsense is required to improve quality of robotic application. However, to acquire the necessary knowledge, robot needs to evaluate the appropriateness of the data it has collected. This paper presents an evaluation method, by combining the weighting mechanism in commonsense databases with a set of weighting factors. The method was verified on our Basic-level Knowledge Network. We conducted questionnaire to collect a commonsense data set and estimate weighting factors. Result showed that, the proposed method was able to build Robot Technology (RT) Ontology for a smart “Bring something” robotic service. More importantly, it allowed robot to learn new knowledge when necessary. An intuitive human-robot interface application was developed as an example base on our approach. Trung Ngo Lam, Haeyeon Lee, Katsuhiro Mayama, Makoto Mizukawa |
ICRA | 3 |