학과 세미나 및 콜로퀴엄

구분 학과 세미나/콜로퀴엄
분류 응용 및 계산수학 세미나
제목 Representation Learning on Knowledge Graphs
Abstract Knowledge graphs represent human knowledge as a directed graph, representing each fact as a triplet consisting of a head entity, a relation, and a tail entity. Knowledge graph embedding is a representation learning technique that aims to convert the entities and relations into a set of low-dimensional embedding vectors while preserving the inherent structure of the given knowledge graph. Once the entities and relations in a knowledge graph are represented as a set of feature vectors, those vectors can be easily integrated into diverse downstream tasks. This talk introduces a new concept of knowledge graph called a bi-level knowledge graph, where the higher-level relationships between triplets can be represented. Learning representations on a bi-level knowledge graph, machines are allowed to solve problems requiring more advanced reasoning than simple link prediction. Also, as a practical example of knowledge graph embedding, how one can utilize the knowledge representations to operate a real robot is briefly explained. This talk discusses how knowledge graph embedding models effectively deliver human knowledge to machines, which is critical in many AI applications.
일시 2023-04-14 (Fri) / 11:00 ~ 12:00 ** 날짜에 유의하세요. **
장소 산업경영학동(E2) Room 2216
강연언어 영어
강연자성명 황지영
강연자소속 KAIST
강연자홈페이지
기타정보 (Online participation) Zoom Link: https://kaist.zoom.us/j/87516570701 ACMseminar mailing list registration: https://mathsci.kaist.ac.kr/mailman/listinfo/acmseminar
초청인 신연종
URL https://sites.google.com/view/kaist-acm
담당자 박준서
연락처