Demo Data from SIGIR 2026 / reviewers in the wild / expert
Mysore Ramaswamy
dblp:80/680
· DBLP profile ↗
1ranked-venue papers
1as first author
0since 2021 · last 1997
—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 first-author
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% | |
| Theoretical computer science
1 paper |
Combinatorics and discrete mathematics · 100% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Knowledge, reasoning and agents › Knowledge representation and reasoning › expert systems
rule-based expert systems |
0.0 | 1 | 1997 | Using Directed Hypergraphs to Verity Rule-Based Expert Systems · IEEE Trans. Knowl. Data Eng. 1997 |
Combinatorics and discrete mathematics › hypergraph
directed hypergraphs |
0.0 | 1 | 1997 | Using Directed Hypergraphs to Verity Rule-Based Expert Systems · IEEE Trans. Knowl. Data Eng. 1997 |
Methods — techniques the papers use, named apart from their topics
graphical representation · 0.0adjacency matrix operations · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 1997 | Using Directed Hypergraphs to Verity Rule-Based Expert SystemsabstractRule-based representation techniques have become popular for storage and manipulation of domain knowledge in expert systems. It is important that systems using such a representation are verified for accuracy before implementation. In recent years, graphical techniques have been found to provide a good framework for the detection of errors that may appear in a rule base. The authors present a graphical representation scheme that: 1) captures complex dependencies across clauses in a rule base in a compact yet intuitively clear manner and 2) is easily automated to detect structural errors in a rigorous fashion. Their technique uses a directed hypergraph to accurately detect the different types of structural errors that appear in a rule base. The technique allows rules to be represented in a manner that clearly identifies complex dependencies across compound clauses. Subsequently, the verification procedure can detect errors in an accurate fashion by using simple operations on the adjacency matrix of the directed hypergraph. The technique is shown to have a computational complexity that is comparable to that of other graphical techniques. The graphical representation coupled with the associated matrix operations illustrate how directed hypergraphs are a very appropriate representation technique for the verification task. Mysore Ramaswamy, Sumit Sarkar, Ye-Sho Chen |
IEEE Trans. Knowl. Data Eng. | 1 |