Finding diagrams, chemical structures, maps or other journal article components is difficult because the articles typically are indexed by topic. Many of these components are all but invisible to standard keyword search. This research describes a method to extract component maps along with metadata from journal articles, and a way to index the maps to make them retrievable by category browse as...

In this paper, we propose a novel feature enrichment method for short text classification based on the link analysis on topic-keyword graph. After topic modeling, we re-rank the keywords distribution extracted by biterm topic model (BTM) to make the topics more salient. Then a topic-keyword graph is constructed and link analysis is conducted. For complement, the K-L divergence is integrated wit...

Relational databases are used to store a large quantity of data scattered around the world. However, users face difficulties in accessing such data for lack of a more natural way of specifying queries. Techniques that use natural language words to search different information sources on the Web are now very common, but they cannot be employed to search relational databases. This work proposes a...

This paper proposes a method for creating a high quality collection of researchers’ homepages. The proposed method consists of three phases: rough filtering of the possible web pages, accurate evaluation of the web pages and precise selection of the correct homepages. For the rough filtering, the authors first define content-based keyword-lists, then generate filtering rules and relax the rules...

Keyword search in relational databases has recently emerged as a new research topic. As a search result is often assembled from multiple relational tables, existing IR-style ranking strategies can not be applied directly. In this paper, we propose a novel IR ranking strategy considering query semantics for effective keyword search. The experimental results on a large-scale real database demonst...