Efficient knowledge management for named entities from text

of entity-centric information for constructing knowledge bases catering to various applications such as semantic
search, question answering, and information retrieval. The vast amounts of natural language texts available
across diverse domains on the Web provide rich sources for discovering facts about named entities such as
people, places, and organizations.
A key challenge, in this regard, entails the need for precise identification and disambiguation of entities across
documents for extraction of attributes/relations and their proper representation in knowledge bases. Additionally,
the applicability of such repositories not only involves the quality and accuracy of the stored information,
but also storage management and query processing efficiency.
This dissertation aims to tackle the above problems by presenting efficient approaches for entity-centric
knowledge acquisition from texts and its representation in knowledge repositories.
This dissertation presents a robust approach for identifying text phrases pertaining to the same named entity
across huge corpora, and their disambiguation to canonical entities present in a knowledge base, by using
enriched semantic contexts and link validation encapsulated in a hierarchical clustering framework.
This work further presents language and consistency features for classification models to compute the
credibility of obtained textual facts, ensuring quality of the extracted information.
Finally, an encoding algorithm, using frequent term detection and improved data locality, to represent entities
for enhanced knowledge base storage and query performance is presented.