Ontology completion using graph convolutional networks

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Hierarchical Losses and New Resources for Fine-grained Entity Typing and Linking

Extraction from raw text to a knowledge base of entities and fine-grained types is often cast as prediction into a flat set of entity and type labels, neglecting the rich hierarchies over types and entities contained in curated ontologies. Previous attempts to incorporate hierarchical structure have yielded little benefit and are restricted to shallow ontologies. This paper presents new methods using real and complex bilinear mappings for integrating hierarchical information, yielding substantial improvement over flat predictions in entity linking and fine-grained entity typing, and achieving new state-of-the-art results for end-to-end models on the benchmark FIGER dataset. We also present two new human-annotated datasets containing wide and deep hierarchies which we will release to the community to encourage further research in this direction: MedMentions, a collection of PubMed abstracts in which 246k mentions have been mapped to the massive UMLS ontology; and TypeNet, which aligns Freebase types with the WordNet hierarchy to obtain nearly 2k entity types. In experiments on all three datasets we show substantial gains from hierarchy-aware training.Comment: ACL 201

Machine Learning Meets the Semantic Web

Remarkable progress in research has shown the efficiency of Knowledge Graphs (KGs) in extracting valuable external knowledge in various domains. A Knowledge Graph (KG) can illustrate high-order relations that connect two objects with one or multiple related attributes. The emerging Graph Neural Networks (GNN) can extract both object characteristics and relations from KGs. This paper presents how Machine Learning (ML) meets the Semantic Web and how KGs are related to Neural Networks and Deep Learning. The paper also highlights important aspects of this area of research, discussing open issues such as the bias hidden in KGs at different levels of graph representation

Graph Deep Learning: Methods and Applications

The past few years have seen the growing prevalence of deep neural networks on various application domains including image processing, computer vision, speech recognition, machine translation, self-driving cars, game playing, social networks, bioinformatics, and healthcare etc. Due to the broad applications and strong performance, deep learning, a subfield of machine learning and artificial intelligence, is changing everyone\u27s life.Graph learning has been another hot field among the machine learning and data mining communities, which learns knowledge from graph-structured data. Examples of graph learning range from social network analysis such as community detection and link prediction, to relational machine learning such as knowledge graph completion and recommender systems, to mutli-graph tasks such as graph classification and graph generation etc.An emerging new field, graph deep learning, aims at applying deep learning to graphs. To deal with graph-structured data, graph neural networks (GNNs) are invented in recent years which directly take graphs as input and output graph/node representations. Although GNNs have shown superior performance than traditional methods in tasks such as semi-supervised node classification, there still exist a wide range of other important graph learning problems where either GNNs\u27 applicabilities have not been explored or GNNs only have less satisfying performance.In this dissertation, we dive deeper into the field of graph deep learning. By developing new algorithms, architectures and theories, we push graph neural networks\u27 boundaries to a much wider range of graph learning problems. The problems we have explored include: 1) graph classification; 2) medical ontology embedding; 3) link prediction; 4) recommender systems; 5) graph generation; and 6) graph structure optimization.We first focus on two graph representation learning problems: graph classification and medical ontology embedding.For graph classification, we develop a novel deep GNN architecture which aggregates node features through a novel SortPooling layer that replaces the simple summing used in previous works. We demonstrate its state-of-the-art graph classification performance on benchmark datasets. For medical ontology embedding, we propose a novel hierarchical attention propagation model, which uses attention mechanism to learn embeddings of medical concepts from hierarchically-structured medical ontologies such as ICD-9 and CCS. We validate the learned embeddings on sequential procedure/diagnosis prediction tasks with real patient data.Then we investigate GNNs\u27 potential for predicting relations, specifically link prediction and recommender systems. For link prediction, we first develop a theory unifying various traditional link prediction heuristics, and then design a framework to automatically learn suitable heuristics from a given network based on GNNs. Our model shows unprecedented strong link prediction performance, significantly outperforming all traditional methods. For recommender systems, we propose a novel graph-based matrix completion model, which uses a GNN to learn graph structure features from the bipartite graph formed by user and item interactions. Our model not only outperforms various matrix completion baselines, but also demonstrates excellent transfer learning ability -- a model trained on MovieLens can be directly used to predict Douban movie ratings with high performance.Finally, we explore GNNs\u27 applicability to graph generation and graph structure optimization. We focus on a specific type of graphs which usually carry computations on them, namely directed acyclic graphs (DAGs). We develop a variational autoencoder (VAE) for DAGs and prove that it can injectively map computations into a latent space. This injectivity allows us to perform optimization in the continuous latent space instead of the original discrete structure space. We then apply our VAE to two types of DAGs, neural network architectures and Bayesian networks. Experiments show that our model not only generates novel and valid DAGs, but also finds high-quality neural architectures and Bayesian networks through performing Bayesian optimization in its latent space

Knowledge Graph Completion to Predict Polypharmacy Side Effects

The polypharmacy side effect prediction problem considers cases in which two drugs taken individually do not result in a particular side effect; however, when the two drugs are taken in combination, the side effect manifests. In this work, we demonstrate that multi-relational knowledge graph completion achieves state-of-the-art results on the polypharmacy side effect prediction problem. Empirical results show that our approach is particularly effective when the protein targets of the drugs are well-characterized. In contrast to prior work, our approach provides more interpretable predictions and hypotheses for wet lab validation.Comment: 13th International Conference on Data Integration in the Life Sciences (DILS2018