Automating the expansion of a knowledge graph
| Autor: | Ok-Ran Jeong, SoYeop Yoo |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
0209 industrial biotechnology
Information retrieval Computer science business.industry media_common.quotation_subject General Engineering Novelty Common sense 02 engineering and technology Relationship extraction Computer Science Applications 020901 industrial engineering & automation Knowledge base Knowledge graph Artificial Intelligence 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Social media business Scope (computer science) Neologism media_common |
| Zdroj: | Expert Systems with Applications. 141:112965 |
| ISSN: | 0957-4174 |
| DOI: | 10.1016/j.eswa.2019.112965 |
| Popis: | In order to make computers understand human languages and to reason, human knowledge needs to be represented and stored in a form that can be processed by computers. Knowledge graphs have been developed for use as a form of the knowledge base for words and general relationships among words. However, they have two limitations. One is that the knowledge graph is limited in size and scope for most of the human languages. Another is that they are not able to deal with neologisms that form a part of the human common sense. Addressing these problems, we have developed and validated PolarisX which can automatically expand a knowledge graph, by crawling and analyzing the news sites and social media in real-time. We utilize and fine-tune the pre-trained multilingual BERT model for the construction of knowledge graphs without language dependencies. We extract new relationships using the BERT-based relation extraction model and integrate them into the knowledge graph. We verify the novelty and accuracy of PolarisX. It deals with neologisms and does not have language dependencies. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full Text from ScienceDirect