Learning heterogeneous subgraph representations for team discovery

The team discovery task is concerned with finding a group of experts from a collaboration network who would collectively cover a desirable set of skills. Most prior work for team discovery either adopt graph-based or neural mapping approaches. Graph-based approaches are computationally intractable often leading to sub-optimal team selection. Neural mapping approaches have better performance, however, are still limited as they learn individual representations for skills and experts and are often prone to overfitting given the sparsity of collaboration networks. Thus, we define the team discovery task as one of learning subgraph representations from a heterogeneous collaboration network where the subgraphs represent teams which are then used to identify relevant teams for a given set of skills. As such, our approach captures local (node interactions with each team) and global (subgraph interactions between teams) characteristics of the representation network and allows us to easily map between any homogeneous and heterogeneous subgraphs in the network to effectively discover teams. Our experiments over two real-world datasets from different domains, namely DBLP bibliographic dataset with 10,647 papers and IMDB with 4882 movies, illustrate that our approach outperforms the state-of-the-art baselines on a range of ranking and quality metrics. More specifically, in terms of ranking metrics, we are superior to the best baseline by approximately 15 % on the DBLP dataset and by approximately 20 % on the IMDB dataset. Further, our findings illustrate that our approach consistently shows a robust performance improvement over the baselines

Low-rank estimation and embedding learning: theory and applications

In many real-world applications of data mining, datasets can be represented using matrices, where rows of the matrix correspond to objects (or data instances) and columns to features (or attributes). Often the datasets are in high-dimensional feature space. For example, in the vector space model of text data, the feature dimension is the vocabulary size. If representing a social network using an adjacency matrix, the feature dimension corresponds to the number of objects in the network. Many other datasets also fall into this category, such as genetic datasets, images, and medical datasets. Even though the feature dimension is enormous, a common observation is that the high-dimensional datasets may (approximately) lie in a subspace of smaller dimensionality, due to dependency or correlation among features. This thesis studies the problem of automatically identifying the low-dimensional space that high-dimensional datasets (approximately) lie in based on dimension reduction models: one is low-rank estimation models and the other is embedding learning models. For data matrices, low-rank estimation is to recover an underlying data matrix, subject to the constraint the matrix is of reduced rank. Such analysis is also generalized to the high-dimensional higher-order tensor data. Meanwhile, embedding learning models are to directly project the observation data into a low-dimensional vector space. In the first part, the theoretical analysis of low-rank estimation models is established in the regime of high-dimensional statistics. For matrices, the low-rank structure corresponds to the sparsity of the singular values; while for tensors, the low-rank model can be defined as the low-rankness of the unfolding matrices of the tensor. To achieve low-rank solutions, two categories of regularization are imposed. Firstly, the problem of robust tensor decomposition with gross corruption is considered. To recover the underlying true tensor and corruption of large magnitude, structure assumptions of low-rankness and sparsity are imposed on the tensor and corruption, respectively. The Schatten-1 norm is applied as convex regularization for the low-rank structure. Secondly, the problem of matrix estimation is considered with a nonconvex penalty. Compared with convex regularization, nonconvex penalty takes advantage of the large singular values, which leads to faster statistical convergence rate and oracle property under a mild condition on the magnitude of the singular values. For both problems, efficient optimization algorithms are proposed, and extensive numerical experiments are conducted to corroborate the efficacy of the proposed algorithms and the theoretical analysis. In the second part, embedding learning models for real-world applications are presented. The high-dimensional data is projected into a low-dimensional vector space via preserving the proximity among objects. Each object is represented by a low-dimensional vector, called embedding or distributed representation. In the first application, the heterogeneity of the objects is considered. Based on the observation that several interactions among the strongly-typed objects happen simultaneously as an event, the embeddings of objects in each event are learned as a whole. In other words, the model preserves the proximity among all the participating objects in each event. Experimental results provide evidence that the learned embeddings are more effective while being robust to data sparsity and noises for various classification tasks. In the second application, the task of expert finding is studied, which is to rank candidates with appropriate expertise based on a given query. To capture the subtle semantic information regarding specific queries with narrow semantic meanings, locally-trained embedding learning with concept hierarchy as guidance is proposed for query expansion. The locally-trained embeddings preserve the proximity among terms constrained on a sub-corpus. Compared with global embedding trained on the whole dataset, locally-trained embedding has stronger representation power. Experimental results show that the proposed embedding learning method achieves high precision regarding the task of expert finding. To summarize, this thesis provides important results of low-rank estimation and embedding learning models for high-dimensional data analysis and real-world applications

AI-assisted patent prior art searching - feasibility study

This study seeks to understand the feasibility, technical complexities and effectiveness of using artificial intelligence (AI) solutions to improve operational processes of registering IP rights. The Intellectual Property Office commissioned Cardiff University to undertake this research. The research was funded through the BEIS Regulators’ Pioneer Fund (RPF). The RPF fund was set up to help address barriers to innovation in the UK economy

Deep representation learning: Fundamentals, Perspectives, Applications, and Open Challenges

Machine Learning algorithms have had a profound impact on the field of computer science over the past few decades. These algorithms performance is greatly influenced by the representations that are derived from the data in the learning process. The representations learned in a successful learning process should be concise, discrete, meaningful, and able to be applied across a variety of tasks. A recent effort has been directed toward developing Deep Learning models, which have proven to be particularly effective at capturing high-dimensional, non-linear, and multi-modal characteristics. In this work, we discuss the principles and developments that have been made in the process of learning representations, and converting them into desirable applications. In addition, for each framework or model, the key issues and open challenges, as well as the advantages, are examined