DOCTORAL ABSTRACT

Text Representation, Retrieval, andUnderstanding with Knowledge Graphs
Chenyan Xiong
Carnegie Mellon University
cx@cs.cmu.edu
September 25, 2018
Abstract
This dissertation aims to improve text representation, retrieval, and understanding withknowledge graphs. Previous information retrieval systems were mostly built upon bag-ofwords representations and frequency-based retrieval models. Effective as they are, wordbased representations and frequency signals only provide shallow text understanding and havevarious intrinsic challenges. Utilizing entities and their structured semantics from knowledge
graphs, this dissertation goes beyond bag-of-words and improves search with richer text
representations, customized semantic structures, sophisticated ranking models and neuralnetworks.
This thesis research starts by enriching query representations with entities and their
textual attributes. It first presents query expansion methods that better represent the querywith words from entity descriptions. Then it develops a supervised latent-space ranking
model that connects query and documents through related entities from the knowledge graph.
It also provides a novel supervised related entity finding technique in the entity search setup.
Then this dissertation presents our entity-oriented search framework that represents queryand documents with entity-based text representations and matches them in the entity space.
We construct a bag-of-entities model that represents texts using automatically linked entities with a customized linking strategy. Ranking with bag-of-entities can be done either
solely with discrete match—as in classic retrieval models—or by our Explicit Semantic Ranking approach that soft matches the query and documents with continuous knowledge graphembeddings. The entity-based text representations are then combined with word-based representations in a word-entity duet representation method. In the duet, query and documents
are represented by both bag-of-words and bag-of-entities; the ranking of them goes throughboth in-space matches and cross-space matches which together incorporates various types ofsemantics from knowledge graphs. The duet framework also introduces a hierarchical ranking
model that learns the linking of entities and the ranking of documents jointly from relevancelabels.
This thesis research concludes with a neural entity salience estimation model that provides
a deeper text understanding capability. We developed a Kernel Entity Salience Model that
better estimates the importance of entities in text with distributed representations and kernelbased interactions. Not only does it improve the salience estimation accuracy, it can also

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be used to estimate the importance of query entities in documents, which provides effective
ranking features that transfer the model’s deeper text understanding capability to improve
retrieval.
With the effective usage of entities, their structured semantics, customized semantic
grounding techniques and novel machine learning models, this dissertation formulates a newentity-oriented search paradigm that overcomes the limitation of bag-of-words and frequency
based retrieval. The better text representation, retrieval, and understanding ability provided
by this dissertation is a solid step towards the next generation of intelligent information systems.

ACM SIGIR Forum

181

Vol. 52 No. 2 December 2018