Xiaozhao Zhao

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The development of knowledge graph construction has prompted more and more commercial engines to improve the retrieval performance by using knowledge graphs as the basic semantic web. Knowledge graph is often used for knowledge inference and entity search, however, the potential ability of its entities and properties for better improving search performance(More)
The classical bag-of-word models for information retrieval (IR) fail to capture contextual associations between words. In this article, we propose to investigate <i>pure high-order dependence</i> among a number of words forming an unseparable semantic entity, that is, the high-order dependence that cannot be reduced to the random coincidence of lower-order(More)
Quantum theory (QT) has recently been employed to advance the theory of information retrieval (IR). A typical method, namely the Quantum Probability Ranking Principle (QPRP), was proposed to re-rank top retrieved documents by considering the inter-dependencies between documents through the “quantum interference”. In this paper, we attempt to explore another(More)
Estimating the probability of relevance for a document is fundamental in information retrieval. From a theoretical point of view, risk exists in the estimation process, in the sense that the estimated probabilities may not be the actual ones precisely. The estimation risk is often considered to be dependent on the rank. For example, the probability ranking(More)
The classical bag-of-word models fail to capture contextual associations between words. We propose to investigate the “high-order pure dependence” among a number of words forming a semantic entity, i.e., the high-order dependence that cannot be reduced to the random coincidence of lower-order dependence. We believe that identifying these high-order pure(More)
The principle of extreme physical information (EPI) can be used to derive many known laws and distributions in theoretical physics by extremizing the physical information loss K, i.e., the difference between the observed Fisher information I and the intrinsic information bound J of the physical phenomenon being measured. However, for complex cognitive(More)
Typical dimensionality reduction (DR) methods are data-oriented, focusing on directly reducing the number of random variables (or features) while retaining the maximal variations in the high-dimensional data. Targeting unsupervised situations, this paper aims to address the problem from a novel perspective and considers model-oriented DR in parameter spaces(More)
We report our participation in the contextual suggestion track of TREC 2014 for which we submitted two runs using a novel approach to complete the competition. The goal of the track is to generate suggestions that users might fond of given the history of users’ preference where he or she used to live in when they travel to a new city. We tested our new(More)