Demo Data from EDBT 2026 / reviewers in the wild / expert
Masaya Eki
dblp:66/6072
· DBLP profile ↗
1ranked-venue papers
1as first author
0since 2021 · last 2008
—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-authorApplied, interdisciplinary, general and emerging computing · 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 |
Data models and query languages · 67% Data integration and cleaning · 33% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Data integration and cleaning
schema inference |
0.1 | 1 | 2008 | Extracting XML schema from multiple implicit xml documents based on inductive reasoning · WWW 2008 |
Data models and query languages
XML data management |
0.1 | 1 | 2008 | Extracting XML schema from multiple implicit xml documents based on inductive reasoning · WWW 2008 |
Data models and query languages
XML schema inference |
0.1 | 1 | 2008 | Extracting XML schema from multiple implicit xml documents based on inductive reasoning · WWW 2008 |
Methods — techniques the papers use, named apart from their topics
ontology · 0.1inductive logic programming · 0.1constraint logic programming · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2008 | Extracting XML schema from multiple implicit xml documents based on inductive reasoningabstractWe propose a method of classifying XML documents and extracting XML schema from XML by inductive inference based on constraint logic programming. The goal of this work is to type a large collection of XML approximately but efficiently. This can also process XML code written in a different schema or even code which is schema-less. Our approach is intended to achieve identification based on the syntax and semantics of the XML documents by information extraction using ontology, and to support retrieval and data management. Our approach has three steps. The first step is XML to predicates, the second step is to compare predicates and classifies structures which represent similar meanings in different structures, and the last step is predicates to rules by using ontology and to maintain XML Schema. We evaluate similarity of data type and data range by using an ontology dictionary, and XML Schema is made from results of second and last step. Masaya Eki, Tadachika Ozono, Toramatsu Shintani |
WWW | 1 |