EDBT 2026 Demo / reviewers in the wild / expert
Jan Frederik Forst
dblp:84/3468
· DBLP profile ↗
1ranked-venue papers
1as first author
0since 2021 · last 2007
—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.
| Databases, data mining, and information retrieval
1 paper |
Information retrieval · 100% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Information retrieval › retrieval models
logic-based retrieval models |
0.1 | 1 | 2007 | A summarisation logic for structured documents · SIGIR 2007 |
Information retrieval › document retrieval
structured document retrieval |
0.1 | 1 | 2007 | A summarisation logic for structured documents · SIGIR 2007 |
Methods — techniques the papers use, named apart from their topics
probabilistic logic · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2007 | A summarisation logic for structured documentsabstractThe logical approach to Information Retrieval tries to model the relevance of a document given a query as the logical implication between documents and queries. In early work, van Rijsbergen states that the retrieval status value of a document given a query is proportional to the degree of implication between a document and a query. Based on this, several probabilistic logics for information retrieval have been conceived, which add an additional layer of abstraction to the information retrieval task: probabilistic models for the retrieval of documents are expressed in those logics, rather than implemented directly. Jan Frederik Forst |
SIGIR | 1 |