Demo Data from VLDB 2026 / reviewers in the wild / expert
Houssem Assadi
dblp:65/180
· 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
Artificial intelligence and machine learning · 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 · 77% Information extraction and text analysis · 23% |
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 › ontology
ontology construction |
0.0 | 1 | 1997 | Knowledge Acquisition from Texts: Using an Automatic Clustering Method Based on Noun-Modifier Relationship · ACL 1997 |
Natural language and speech › Information extraction and text analysis › phrase extraction
terminology extraction |
0.0 | 1 | 1997 | Knowledge Acquisition from Texts: Using an Automatic Clustering Method Based on Noun-Modifier Relationship · ACL 1997 |
Methods — techniques the papers use, named apart from their topics
morpho-syntactic analysis · 0.0clustering · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 1997 | Knowledge Acquisition from Texts: Using an Automatic Clustering Method Based on Noun-Modifier RelationshipabstractWe describe the early stage of our methodology of knowledge acquisition from technical texts. First, a partial morpho-syntactic analysis is performed to extract "candidate terms". Then, the knowledge engineer, assisted by an automatic clustering tool, builds the "conceptual fields" of the domain. We focus on this conceptual analysis stage, describe the data prepared from the results of the morpho-syntactic analysis and show the results of the clustering module and their interpretation. We found that syntactic links represent good descriptors for candidate terms clustering since the clusters are often easily interpreted as "conceptual fields". Houssem Assadi |
ACL | 1 |