Learning Representations of Bi-level Knowledge Graphs for Reasoning beyond Link Prediction

Knowledge graphs represent known facts using triplets. While existing knowledge graph embedding methods only consider the connections between entities, we propose considering the relationships between triplets. For example, let us consider two triplets $T_1$ and $T_2$ where $T_1$ is (Academy_Awards, Nominates, Avatar) and $T_2$ is (Avatar, Wins, Academy_Awards). Given these two base-level triplets, we see that $T_1$ is a prerequisite for $T_2$. In this paper, we define a higher-level triplet to represent a relationship between triplets, e.g., $\langle T_1$, PrerequisiteFor, $T_2\rangle$ where PrerequisiteFor is a higher-level relation. We define a bi-level knowledge graph that consists of the base-level and the higher-level triplets. We also propose a data augmentation strategy based on the random walks on the bi-level knowledge graph to augment plausible triplets. Our model called BiVE learns embeddings by taking into account the structures of the base-level and the higher-level triplets, with additional consideration of the augmented triplets. We propose two new tasks: triplet prediction and conditional link prediction. Given a triplet $T_1$ and a higher-level relation, the triplet prediction predicts a triplet that is likely to be connected to $T_1$ by the higher-level relation, e.g., $\langle T_1$, PrerequisiteFor, ?$\rangle$. The conditional link prediction predicts a missing entity in a triplet conditioned on another triplet, e.g., $\langle T_1$, PrerequisiteFor, (Avatar, Wins, ?)$\rangle$. Experimental results show that BiVE significantly outperforms all other methods in the two new tasks and the typical base-level link prediction in real-world bi-level knowledge graphs.Comment: 14 pages, 3 figures, 15 tables. 37th AAAI Conference on Artificial Intelligence (AAAI 2023

Representation Learning on Hyper-Relational and Numeric Knowledge Graphs with Transformers

A hyper-relational knowledge graph has been recently studied where a triplet is associated with a set of qualifiers; a qualifier is composed of a relation and an entity, providing auxiliary information for a triplet. While existing hyper-relational knowledge graph embedding methods assume that the entities are discrete objects, some information should be represented using numeric values, e.g., (J.R.R., was born in, 1892). Also, a triplet (J.R.R., educated at, Oxford Univ.) can be associated with a qualifier such as (start time, 1911). In this paper, we propose a unified framework named HyNT that learns representations of a hyper-relational knowledge graph containing numeric literals in either triplets or qualifiers. We define a context transformer and a prediction transformer to learn the representations based not only on the correlations between a triplet and its qualifiers but also on the numeric information. By learning compact representations of triplets and qualifiers and feeding them into the transformers, we reduce the computation cost of using transformers. Using HyNT, we can predict missing numeric values in addition to missing entities or relations in a hyper-relational knowledge graph. Experimental results show that HyNT significantly outperforms state-of-the-art methods on real-world datasets.Comment: 11 pages, 5 figures, 12 tables. 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2023

InGram: Inductive Knowledge Graph Embedding via Relation Graphs

Inductive knowledge graph completion has been considered as the task of predicting missing triplets between new entities that are not observed during training. While most inductive knowledge graph completion methods assume that all entities can be new, they do not allow new relations to appear at inference time. This restriction prohibits the existing methods from appropriately handling real-world knowledge graphs where new entities accompany new relations. In this paper, we propose an INductive knowledge GRAph eMbedding method, InGram, that can generate embeddings of new relations as well as new entities at inference time. Given a knowledge graph, we define a relation graph as a weighted graph consisting of relations and the affinity weights between them. Based on the relation graph and the original knowledge graph, InGram learns how to aggregate neighboring embeddings to generate relation and entity embeddings using an attention mechanism. Experimental results show that InGram outperforms 14 different state-of-the-art methods on varied inductive learning scenarios.Comment: 14 pages, 4 figures, 6 tables, 40th International Conference on Machine Learning (ICML 2023

Non-perturbative corrections to mean-field behavior: spherical model on spider-web graph

We consider the spherical model on a spider-web graph. This graph is effectively infinite-dimensional, similar to the Bethe lattice, but has loops. We show that these lead to non-trivial corrections to the simple mean-field behavior. We first determine all normal modes of the coupled springs problem on this graph, using its large symmetry group. In the thermodynamic limit, the spectrum is a set of $\delta$-functions, and all the modes are localized. The fractional number of modes with frequency less than $\omega$ varies as $\exp (-C/\omega)$ for $\omega$ tending to zero, where $C$ is a constant. For an unbiased random walk on the vertices of this graph, this implies that the probability of return to the origin at time $t$ varies as $\exp(- C' t^{1/3})$, for large $t$, where $C'$ is a constant. For the spherical model, we show that while the critical exponents take the values expected from the mean-field theory, the free-energy per site at temperature $T$, near and above the critical temperature $T_c$, also has an essential singularity of the type $\exp[ -K {(T - T_c)}^{-1/2}]$.Comment: substantially revised, a section adde

Characterization of Four Novel Nonsense Mediated Decay Homologs in Tetrahymena thermophila

From the Washington University Office of Undergraduate Research Digest (WUURD), Vol. 12, 05-01-2017. Published by the Office of Undergraduate Research. Joy Zalis Kiefer, Director of Undergraduate Research and Associate Dean in the College of Arts & Sciences; Lindsey Paunovich, Editor; Helen Human, Programs Manager and Assistant Dean in the College of Arts and Sciences Mentor: Douglas Chalke

Relationships between some maternal variables and lexical diversity in three-year-old Cantonese-speaking children

A dissertation submitted in partial fulfilment of the requirements for the Bachelor of Science (Speech and Hearing Sciences), The University of Hong Kong, June 30, 2007.Also available in print.Thesis (B.Sc)--University of Hong Kong, 2007.published_or_final_versionSpeech and Hearing SciencesBachelorBachelor of Science in Speech and Hearing Science

Creatinine, diet, micronutrients, and arsenic methylation in West Bengal, India.

BackgroundIngested inorganic arsenic (InAs) is methylated to monomethylated (MMA) and dimethylated metabolites (DMA). Methylation may have an important role in arsenic toxicity, because the monomethylated trivalent metabolite [MMA(III)] is highly toxic.ObjectivesWe assessed the relationship of creatinine and nutrition--using dietary intake and blood concentrations of micronutrients--with arsenic metabolism, as reflected in the proportions of InAS, MMA, and DMA in urine, in the first study that incorporated both dietary and micronutrient data.MethodsWe studied methylation patterns and nutritional factors in 405 persons who were selected from a cross-sectional survey of 7,638 people in an arsenic-exposed population in West Bengal, India. We assessed associations of urine creatinine and nutritional factors (19 dietary intake variables and 16 blood micronutrients) with arsenic metabolites in urine.ResultsUrinary creatinine had the strongest relationship with overall arsenic methylation to DMA. Those with the highest urinary creatinine concentrations had 7.2% more arsenic as DMA compared with those with low creatinine (p < 0.001). Animal fat intake had the strongest relationship with MMA% (highest tertile animal fat intake had 2.3% more arsenic as MMA, p < 0.001). Low serum selenium and low folate were also associated with increased MMA%.ConclusionsUrine creatinine concentration was the strongest biological marker of arsenic methylation efficiency, and therefore should not be used to adjust for urine concentration in arsenic studies. The new finding that animal fat intake has a positive relationship with MMA% warrants further assessment in other studies. Increased MMA% was also associated, to a lesser extent, with low serum selenium and folate

Assessing the Impact of Digital Alternative News Media in a Hybrid News Environment: Cases from Taiwan and Hong Kong

As consumption of mainstream news media declines and alternative news media proliferates, in this paper, we seek to assess the impact of digital alternative news media (DANM) in relation to mainstream news media (MNM). We examine the range of DANM, especially public Facebook pages, related to two large-scale social movements neighbouring mainland China as case studies of social movement media exerting maximalist effects. The assessment relies on academic sources, archival materials, descriptive social media metrics, and an original analysis of external content shared on public Facebook pages and groups using data collected from the Facebook Graph API. A six-dimensional scheme is proposed to guide the assessment. Sorting through and piecing together multiple sources, we arrive at a multi-faceted description, comparison, and analysis of the impact of DANM during two social movements