Name
Abstract | ||
|---|---|---|
In the era of big data explosion, the Internet serving as an infrastructure for organizing and acquiring information and knowledge, has usability shortcomings in specific application scenarios. For professional application business, we need more efficient information organization and interactive interface to facilitate the formalization of expert experience and access to associated information by ordinary users. This paper designs a domain knowledge graph driven multi-modal question answering system, using knowledge graph as foundation framework for domain data, combined with intelligent question answering and image recognition technology, achieve efficient information query scheme. In the process of establishing the domain knowledge graph, we adopt the method of combining intelligent natural language processing with crowdsourcing artificial entity-relational extraction, which can effectively solve the problem of knowledge engineering in professional fields. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/BIGCOM.2019.00015 | 2019 5th International Conference on Big Data Computing and Communications (BIGCOM) |
Keywords | Field | DocType |
big data, domain knowledge graph, knowledge engineering, question-answering system, multimodal combination | Graph,Question answering,Domain knowledge,Computer science,Theoretical computer science,Artificial intelligence,Modal,Machine learning | Conference |
ISBN | Citations | PageRank |
978-1-7281-4025-4 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhengwei Zhao | 1 | 0 | 0.34 |
| Xiaodong Wang | 2 | 4 | 2.44 |
| Xiaowei Xu | 3 | 0 | 0.68 |
| Qing Wang | 4 | 345 | 76.64 |