| LDR | | 02925nam u200445 4500 |
| 001 | | 000000420439 |
| 005 | | 20190215164458 |
| 008 | | 181129s2018 |||||||||||||||||c||eng d |
| 020 | |
▼a 9780438283725 |
| 035 | |
▼a (MiAaPQ)AAI10969856 |
| 040 | |
▼a MiAaPQ
▼c MiAaPQ
▼d 247004 |
| 082 | 0 |
▼a 004 |
| 100 | 1 |
▼a Moon, Changsung. |
| 245 | 10 |
▼a Predictive Modeling of Complex Graphs as Context and Semantics Preserving Vector Spaces. |
| 260 | |
▼a [S.l.]:
▼b North Carolina State University.,
▼c 2018. |
| 260 | 1 |
▼a Ann Arbor:
▼b ProQuest Dissertations & Theses,
▼c 2018. |
| 300 | |
▼a 93 p. |
| 500 | |
▼a Source: Dissertation Abstracts International, Volume: 79-12(E), Section: B. |
| 500 | |
▼a Adviser: Nagiza F. Samatova. |
| 502 | 1 |
▼a Thesis (Ph.D.)--North Carolina State University, 2018. |
| 520 | |
▼a Predictive modeling of complex graphs is a process that uses complex graph data and probability theory to forecast outcomes. Relational data, such as social networks and knowledge bases, can be represented as complex graphs with directed, multi- |
| 520 | |
▼a This work initially addresses two research challenges with regards to predictive analysis in complex graphs: 1) automatic completion of user-intended actions and 2) automatic knowledge graph (KG) completion, i.e., inference of missing entities a |
| 520 | |
▼a Typically, frequency analysis based models andMarkov models have been applied to address the task of predicting user actions. Although the existing models have been shown to have a reasonable predictive strength, they have issues capturing seman |
| 520 | |
▼a In this dissertation, we seek to improve both prediction of user actions and KG completion by inventing vector space embedding methods. We first propose an online method, Frequency Vector (FVEC) prediction, that predicts next actions by combinin |
| 520 | |
▼a For the prediction of missing entities and relation types in KGs, we present a contextual embedding method CONTE that learns the vector embeddings of entities and relation types while taking contextual relation types into account. Contextual re |
| 520 | |
▼a Finally, we present an embedding method inferring missing entity types, which refer to collections of entities that share common definitions (e.g., /music/artist). In addition to inference of missing entities and relation types, inferring missi |
| 590 | |
▼a School code: 0155. |
| 650 | 4 |
▼a Computer science. |
| 650 | 4 |
▼a Artificial intelligence. |
| 690 | |
▼a 0984 |
| 690 | |
▼a 0800 |
| 710 | 20 |
▼a North Carolina State University.
▼b Computer Science. |
| 773 | 0 |
▼t Dissertation Abstracts International
▼g 79-12B(E). |
| 773 | |
▼t Dissertation Abstract International |
| 790 | |
▼a 0155 |
| 791 | |
▼a Ph.D. |
| 792 | |
▼a 2018 |
| 793 | |
▼a English |
| 856 | 40 |
▼u http://www.riss.kr/pdu/ddodLink.do?id=T15001284
▼n KERIS
▼z 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
| 980 | |
▼a 201812
▼f 2019 |
| 990 | |
▼a ***1012033 |