| Revista: | Computación y sistemas |
| Base de datos: | |
| Número de sistema: | 000560427 |
| ISSN: | 1405-5546 |
| Autores: | Xia, Yingju1 Zheng, Zhongguang1 Meng, Yao1 Sun, Jun1 |
| Instituciones: | 1Fujitsu Research & Development Center Co. LTD, Beijing. China |
| Año: | 2019 |
| Periodo: | Jul-Sep |
| Volumen: | 23 |
| Número: | 3 |
| Paginación: | 785-793 |
| País: | México |
| Idioma: | Inglés |
| Tipo de documento: | Artículo |
| Resumen en inglés | Knowledge graphs represent information in the form of entities and relation-ships between them. A knowledge graph consists of multi-relational data, having entities as nodes and relations as edges. The relation indicates a relationship between these two entities. Relation extraction is the key step to construct a knowledge graph. Conventional relation extraction methods usually need large scale labeled samples for each website. It's difficult to deal with the large number of relations and the various representations of each relation. This paper proposed a novel semi-automatic method that builds knowledge graph by extracting relation patterns and finding new relations. The proposed method models the relation pattern as a tag sequence and learns the pattern similarity metric using the existing relation instances. The pattern similarity is adopted to extract new patterns for existing relations. The new relations are detected by using the pattern similarity and clustering technique. The experimental results on large scale web pages show the effectiveness and efficiency of the proposed method. |
| Disciplinas: | Ciencias de la computación |
| Palabras clave: | Procesamiento de datos |
| Keyword: | Semantic web, Relation extraction, Knowledge graph, Data processing |
| Texto completo: | Texto completo (Ver HTML) Texto completo (Ver PDF) |
