2017Unpublished venueRequires access

LOD conversion system for generating large knowledge base from web contents

Kazuki Takahashi, Toshitaka Maki, Toshihiko Wakahara, Toru Kobayashi, Akihisa Kodate, Noboru Sonehara

Open publisher page 0 citations

Abstract

In recent years, the Linked Open Data (LOD) have been attracting attention in the world as the technology that can construct the semantic Web. The LOD are created based on the Resource Description Framework (RDF), and can link each of resources considering semantic relations. However, there are problems that the LOD do not have enough links between resources, and the LOD need a lot of time for creation. Therefore, this paper presents the new LOD conversion system that can convert the Web contents to the LOD. This system extracts keywords from sentences in the Web contents using DBpedia LOD, and generates the knowledge base. By experiments, the proposed system was confirmed that it can convert the target Web pages to the RDF data fully at 2.03 seconds per a page. In addition, the system realized to generate the knowledge base by estimating the concepts of the resources using the DBpedia LOD. The number of links in the LOD is increasing 2.0 times and more than non-estimated RDF data.

About this research paper

What this paper is about

In recent years, the Linked Open Data (LOD) have been attracting attention in the world as the technology that can construct the semantic Web. The LOD are created based on the Resource Description Framework (RDF), and can link each of resources considering semantic relations. However, there are problems that the LOD do not have enough links between resources, and the LOD need a lot of time for creation. Therefore, this paper presents the new LOD conversion system that can convert the Web contents to the LOD. This system extracts keywords from sentences in the Web contents using DBpedia LOD, and generates the knowledge base. By experiments, the proposed system was confirmed that it can convert the target Web pages to the RDF data fully at 2.03 seconds per a page. In addition, the system realized to generate the knowledge base by estimating the concepts of the resources using the DBpedia LOD. The number of links in the LOD is increasing 2.0 times and more than non-estimated RDF data.

Why it matters

A significance statement is not available in the OpenAlex record.

Key contribution

A contribution statement is not available in the OpenAlex record.

Method / approach

Method details are not available in the OpenAlex metadata.

Main findings

Findings are not separately available in the OpenAlex metadata.

Limitations

Limitations are not available in the OpenAlex metadata.

Applications

Application details are not available in the OpenAlex metadata.

Available abstract

In recent years, the Linked Open Data (LOD) have been attracting attention in the world as the technology that can construct the semantic Web. The LOD are created based on the Resource Description Framework (RDF), and can link each of resources considering semantic relations. However, there are problems that the LOD do not have enough links between resources, and the LOD need a lot of time for creation. Therefore, this paper presents the new LOD conversion system that can convert the Web contents to the LOD. This system extracts keywords from sentences in the Web contents using DBpedia LOD, and generates the knowledge base. By experiments, the proposed system was confirmed that it can convert the target Web pages to the RDF data fully at 2.03 seconds per a page. In addition, the system realized to generate the knowledge base by estimating the concepts of the resources using the DBpedia LOD. The number of links in the LOD is increasing 2.0 times and more than non-estimated RDF data.

Key concepts: Linked data, RDF, Computer science, Semantic Web, Construct (python library), Knowledge base, SPARQL, World Wide Web

Related papers

Back to paper searchBrowse research topicsOriginal source
LOD conversion system for generating large knowledge base from web contents — Research Paper | ScholarLens