Please use this identifier to cite or link to this item: doi:10.22028/D291-30078
Title: Search and Analytics Using Semantic Annotations
Author(s): Gupta, Dhruv
Language: English
Year of Publication: 2019
Free key words: Information Retrieval
Information Extraction
Temporal Information Retrieval
Question Answering
Knowledge-Centric Tasks
Text Analytics
DDC notations: 600 Technology
Publikation type: Doctoral Thesis
Abstract: Search systems help users locate relevant information in the form of text documents for keyword queries. Using text alone, it is often difficult to satisfy the user's information need. To discern the user's intent behind queries, we turn to semantic annotations (e.g., named entities and temporal expressions) that natural language processing tools can now deliver with great accuracy. This thesis develops methods and an infrastructure that leverage semantic annotations to efficiently and effectively search large document collections. This thesis makes contributions in three areas: indexing, querying, and mining of semantically annotated document collections. First, we describe an indexing infrastructure for semantically annotated document collections. The indexing infrastructure can support knowledge-centric tasks such as information extraction, relationship extraction, question answering, fact spotting and semantic search at scale across millions of documents. Second, we propose methods for exploring large document collections by suggesting semantic aspects for queries. These semantic aspects are generated by considering annotations in the form of temporal expressions, geographic locations, and other named entities. The generated aspects help guide the user to relevant documents without the need to read their contents. Third and finally, we present methods that can generate events, structured tables, and insightful visualizations from semantically annotated document collections.
Link to this record: urn:nbn:de:bsz:291--ds-300780
Advisor: Berberich, Klaus
Date of oral examination: 12-Dec-2019
Date of registration: 20-Dec-2019
Faculty: MI - Fakultät für Mathematik und Informatik
Department: MI - Informatik
Collections:SciDok - Der Wissenschaftsserver der Universität des Saarlandes

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