Demo Data from EDBT 2026 / reviewers in the wild / expert
Y. S. Jiang
dblp:64/2889
· DBLP profile ↗
3ranked-venue papers
0as first author
0since 2021 · last 1999
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Databases, data management, data science and information retrieval · 3Artificial intelligence and machine learning · 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 |
Data integration and cleaning · 50% Web and social media mining · 50% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Data integration and cleaning › data extraction
web data extraction |
0.0 | 1 | 1999 | Record-Boundary Discovery in Web Documents · SIGMOD Conference 1999 |
Web and social media mining › web mining
web page structure analysis |
0.0 | 1 | 1999 | Record-Boundary Discovery in Web Documents · SIGMOD Conference 1999 |
Methods — techniques the papers use, named apart from their topics
heuristic tree analysis · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 1999 | Record-Boundary Discovery in Web DocumentsabstractExtraction of information from unstructured or semistructured Web documents often requires a recognition and delimitation of records. (By “record” we mean a group of information relevant to some entity.) Without first chunking documents that contain multiple records according to record boundaries, extraction of record information will not likely succeed. In this paper we describe a heuristic approach to discovering record boundaries in Web documents. In our approach, we capture the structure of a document as a tree of nested HTML tags, locate the subtree containing the records of interest, identify candidate separator tags within the subtree using five independent heuristics, and select a consensus separator tag based on a combined heuristic. Our approach is fast (runs linearly for practical cases within the context of the larger data-extraction problem) and accurate (100% in the experiments we conducted). David W. Embley, Y. S. Jiang, Yiu-Kai Ng |
SIGMOD Conference | 2 |
| 1999 | Conceptual-Model-Based Data Extraction from Multiple-Record Web Pages
David W. Embley, Douglas M. Campbell, Y. S. Jiang, Stephen W. Liddle, Yiu-Kai Ng, Dallan Quass, Randy D. Smith |
Data Knowl. Eng. | 3 |
| 1998 | A Conceptual-Modeling Approach to Extracting Data from the Web
David W. Embley, Douglas M. Campbell, Y. S. Jiang, Stephen W. Liddle, Yiu-Kai Ng, Dallan Quass, Randy D. Smith |
ER | 3 |