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Constructing open domain knowledge base and knowledge-empowered applications

Constructing open domain knowledge base and knowledge-empowered applications

자료유형
학위논문
개인저자
류우종 柳宇鍾
서명 / 저자사항
Constructing open domain knowledge base and knowledge-empowered applications / Woo-jong Ryu
발행사항
Seoul :   Graduate School, Korea University,   2019  
형태사항
vi, 114장 : 삽화, 도표 ; 26 cm
기타형태 저록
Constructing Open Domain Knowledge Base and Knowledge-empowered Applications   (DCOLL211009)000000084422  
학위논문주기
학위논문(박사)-- 고려대학교 대학원: 컴퓨터·전파통신공학과, 2019. 8
학과코드
0510   6YD36   369  
일반주기
지도교수: 이상근  
서지주기
참고문헌: 장 103-114
이용가능한 다른형태자료
PDF 파일로도 이용가능;   Requires PDF file reader(application/pdf)  
비통제주제어
knowledge base , contextual advertising , keyphrase extraction,,
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100 1 ▼a 류우종 ▼g 柳宇鍾
245 1 0 ▼a Constructing open domain knowledge base and knowledge-empowered applications / ▼d Woo-jong Ryu
260 ▼a Seoul : ▼b Graduate School, Korea University, ▼c 2019
300 ▼a vi, 114장 : ▼b 삽화, 도표 ; ▼c 26 cm
500 ▼a 지도교수: 이상근
502 1 ▼a 학위논문(박사)-- ▼b 고려대학교 대학원: ▼c 컴퓨터·전파통신공학과, ▼d 2019. 8
504 ▼a 참고문헌: 장 103-114
530 ▼a PDF 파일로도 이용가능; ▼c Requires PDF file reader(application/pdf)
653 ▼a knowledge base ▼a contextual advertising ▼a keyphrase extraction
776 0 ▼t Constructing Open Domain Knowledge Base and Knowledge-empowered Applications ▼w (DCOLL211009)000000084422
900 1 0 ▼a Ryu, Woo-jong, ▼e
900 1 0 ▼a 이상근 ▼g 李尙根, ▼e 지도교수
945 ▼a KLPA

전자정보

No. 원문명 서비스
1
Constructing open domain knowledge base and knowledge-empowered applications (39회 열람)
PDF 초록 목차
No. 소장처 청구기호 등록번호 도서상태 반납예정일 예약 서비스
No. 1 소장처 과학도서관/학위논문서고/ 청구기호 0510 6YD36 369 등록번호 123062335 도서상태 대출가능 반납예정일 예약 서비스 B M
No. 2 소장처 과학도서관/학위논문서고/ 청구기호 0510 6YD36 369 등록번호 123062336 도서상태 대출가능 반납예정일 예약 서비스 B M
No. 3 소장처 세종학술정보원/5층 학위논문실/ 청구기호 0510 6YD36 369 등록번호 153083332 도서상태 대출가능 반납예정일 예약 서비스 M
No. 소장처 청구기호 등록번호 도서상태 반납예정일 예약 서비스
No. 1 소장처 과학도서관/학위논문서고/ 청구기호 0510 6YD36 369 등록번호 123062335 도서상태 대출가능 반납예정일 예약 서비스 B M
No. 2 소장처 과학도서관/학위논문서고/ 청구기호 0510 6YD36 369 등록번호 123062336 도서상태 대출가능 반납예정일 예약 서비스 B M
No. 소장처 청구기호 등록번호 도서상태 반납예정일 예약 서비스
No. 1 소장처 세종학술정보원/5층 학위논문실/ 청구기호 0510 6YD36 369 등록번호 153083332 도서상태 대출가능 반납예정일 예약 서비스 M

컨텐츠정보

초록

In this thesis, we investigate the construction of open domain knowledge base which
consists of thousands of domains and its relevant verbs. We define a domain to be an
atomic unit representing the semantics of texts, such as topics, themes, etc. The relevant
verbs are a set of verbs semantically related to each domain. In particular, they
are essential to clarifying domains according to its context. To this end, we firstly build
a taxonomy of domains from a domain knowledge, i.e., Open Directory Project (ODP).
We then identify domain-related verbs from different information sources. From the experimental
results, we confirm that the proposed approach significantly outperforms an
existing verb identification approach.
Based on the ODP-based open domain knowledge base, we present a couple of knowledgeempowered
applications to verify its effectiveness. We describe knowledge-empowered
contextual advertising as the first application. In the application, we infer domains of
webpages (i.e., what a user wants) and models verbs associated with the inferred domains
in the action perspective (i.e., what a user wants do). This way enables us to model
the intent of a user visiting a webpage as pairs of the domain and its associated verbs.
Subsequently, we incorporate such two features into and ads ranking framework.
In addition to contextual advertising, we utilize the ODP-based open domain knowledge
base in a traditional natural language processing task, i.e., keyphrase extraction from
short texts. In this application, we extract keyphrases relevant to domains of texts. In
particular, we identify domains of a short text and extract quality keyphrases from terms
that contribute considerably to the identified domains. We also extract representative
verb words as verb keyphrases which represent the domains of a short text in the action
perspective.
We show that our knowledge-empowered applications ourperform state-of-the-art techniques
on real-world datasets. In knowledge-empowered contextual advertising, the proposed
methodology delivers the right ads satisfying a user’s information needs. The
proposed keyphrase extraction methodology effectively extracts both quality keyphrases
and verb keyphrases. From in-depth analysis, we verify that our ODP-based open domain
knowledge base is indeed effective to text understanding, including contextual advertising
and keyphrase extraction.

목차

Abstract
Contents i
List of Figures iv
List of Tables vi
1 Introduction 1
 1.1 Background and Motivation  2
 1.2 Problem Statement  4
  1.2.1 Building Taxonomy of Domains  5
  1.2.2 Identifying Domain-related Verbs  6
 1.3 Knowledge-empowered Applications  7
  1.3.1 Contextual Advertising  7
  1.3.2 Keyphrase Extraction  8
 1.4 Contributions of Thesis  9
  1.4.1 Organization of Thesis  10
2 Background 12
 2.1 Open Directory Project 13
 2.2 ODP-based Semantic Classification  14
 2.3 ODP-based Semantic Ranking  20
 2.4 ODP-based Intelligent Services on Smartphones  22
  2.4.1 Content Curation Service 22
  2.4.2 Personalized Intelligent Interface 27
  2.4.3 Conversational Photo Sharing Service  30
  2.4.4 Others  33
3 Constructing Open Domain Knowledge Base 37
 3.1 Building Taxonomy of Domains  38
 3.2 Preliminary 39
 3.3 Identifying Relevant Verbs to Domains  40
  3.3.1 Searching Relevant Documents  41
  3.3.2 Extracting Relevant Verbs  43
 3.4 Performance Evaluation  44
 3.5 Summary  48
4 Knowledge-empowered Contextual Advertising 50
 4.1 Introduction 50
 4.2 Methodology 53
  4.2.1 Verbal Intent Modeling 53
  4.2.2 Ads Ranking  54
 4.3 Contextual Advertising Engine  55
  4.3.1 Topic Classifier  56
  4.3.2 Verbal Feature Generator  57
  4.3.3 Ads Ranker 57
 4.4 Evaluation 57
  4.4.1 Settings and Dataset  57
  4.4.2 Evaluation of Ranking Ads  59
  4.4.3 Qualitative Analysis 63
 4.5 Related Works  65
  4.5.1 Contextual Advertising  65
  4.5.2 Search Task, User Intent Identification and Verb Representation  66
 4.6 Summary  67
5 Knowledge-empowered Keyphrase Extraction 68
 5.1 Introduction  69
 5.2 Methodology  71
  5.2.1 Identifying Domains  72
  5.2.2 Selecting Keywords  72
  5.2.3 Extracting Quality Keyphrases 74
  5.2.4 Extracting Verb Keyphrases  76
 5.3 Experimental Study  77
  5.3.1 Datasets and Settings  77
  5.3.2 Keyphrase Extraction Results  81
  5.3.3 Ablation Study 87
  5.3.4 Keyword Extraction Results  91
  5.3.5 Domain Identification Results  92
 5.4 Related Works  95
  5.4.1 Keyphrase Extraction  95
  5.4.2 Semantic Classification 96
 5.5 Summary  97
6 Conclusion 98
 6.1 Summary of Thesis  98
 6.2 Future Work 100
Bibliography 103