Demo Data from SIGIR 2026 / reviewers in the wild / expert
Vasilis Andritsoudis
dblp:418/0056
· DBLP profile ↗
1ranked-venue papers
1as first author
1since 2021 · last 2025
0009-0008-5389-9458ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Computer networks · 1 · 1 first-author · 1 since 2021
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 · 50% Data models and query languages · 50% | |
| Computer networks
1 paper |
Internet architecture and protocols · 100% |
Topics — the 2 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Knowledge graphs
knowledge graph querying |
0.9 | 1 | 2025 | Poster: ChatIYP: Enabling Natural Language Access to the Internet Yellow Pages Database · IMC 2025 |
Data models and query languages
natural language interface |
0.9 | 1 | 2025 | Poster: ChatIYP: Enabling Natural Language Access to the Internet Yellow Pages Database · IMC 2025 |
Methods — techniques the papers use, named apart from their topics
retrieval-augmented generation · 1.7large language model · 1.7
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | Poster: ChatIYP: Enabling Natural Language Access to the Internet Yellow Pages DatabaseabstractThe Internet Yellow Pages (IYP) aggregates information from multiple sources about Internet routing into a unified, graph-based knowledge base. However, querying it requires knowledge of the Cypher language and the exact IYP schema, thus limiting usability for non-experts. In this paper, we propose ChatIYP, a domain-specific Retrieval-Augmented Generation (RAG) system that enables users to query IYP through natural language questions. Our evaluation demonstrates solid performance on simple queries, as well as directions for improvement, and provides insights for selecting evaluation metrics that are better fit for IYP querying AI agents. Vasilis Andritsoudis, Pavlos Sermpezis, Ilias Dimitriadis, Athena Vakali |
IMC | 1 |