Demo Data from SIGIR 2026 / reviewers in the wild / expert
Mahesh Gupta
dblp:92/9574
· DBLP profile ↗
2ranked-venue papers
0as first author
0since 2021 · last 2015
—ORCID · unresolved
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1Databases, data management, data science and information retrieval · 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.
| Databases, data mining, and information retrieval
1 paper |
Knowledge graphs · 44% Data models and query languages · 44% Graph data management · 13% |
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 querying |
0.2 | 1 | 2014 | GQBE: Querying knowledge graphs by example entity tuples · ICDE 2014 |
Data models and query languages › query interface
query by example |
0.2 | 1 | 2014 | GQBE: Querying knowledge graphs by example entity tuples · ICDE 2014 |
Graph data management
graph query processing |
0.1 | 1 | 2014 | GQBE: Querying knowledge graphs by example entity tuples · ICDE 2014 |
Methods — techniques the papers use, named apart from their topics
weighted maximum query graph · 0.2top-k approximate matching · 0.2
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2015 | Throughput accounting and performance of a manufacturing company under stochastic demand and scrap rates
Olli-Pekka Hilmola, Mahesh Gupta |
Expert Syst. Appl. | 2 |
| 2014 | GQBE: Querying knowledge graphs by example entity tuplesabstractWe present GQBE, a system that presents a simple and intuitive mechanism to query large knowledge graphs. Answers to tasks such as “list university professors who have designed some programming languages and also won an award in Computer Science” are best found in knowledge graphs that record entities and their relationships. Real-world knowledge graphs are difficult to use due to their sheer size and complexity and the challenging task of writing complex structured graph queries. Toward better usability of query systems over knowledge graphs, GQBE allows users to query knowledge graphs by example entity tuples without writing complex queries. In this demo we present: 1) a detailed description of the various features and user-friendly GUI of GQBE, 2) a brief description of the system architecture, and 3) a demonstration scenario that we intend to show the audience. Nandish Jayaram, Mahesh Gupta, Arijit Khan 0001, Chengkai Li 0001, Xifeng Yan, Ramez Elmasri |
ICDE | 2 |