Entity Resolution in graph databases: comparison study

Graph embedding learns low-dimensional representations for nodes in a graph and effectively preserves the graph structure. Recently, a significant amount of progress has been made toward this emerging research area. However, there are several fundamental problems that remain open. First, existing methods fail to preserve the out-degree distributions on directed graphs. Second, many existing methods employ random walk based proximities and thus suffer from conflicting optimization goals on undirected graphs. Finally, existing factorization methods are unable to achieve scalability and non-linearity simultaneously. This paper presents an in-depth study on graph embedding techniques on both directed and undirected graphs. We analyze the fundamental reasons that lead to the distortion of out-degree distributions and to the conflicting optimization goals. We propose transpose proximity, a unified approach that solves both problems. Based on the concept of transpose proximity, we design STRAP, a factorization based graph embedding algorithm that achieves scalability and non-linearity simultaneously. STRAP makes use of the backward push algorithm to efficiently compute the sparse Personalized PageRank (PPR) as its transpose proximities. By imposing the sparsity constraint, we are able to apply non-linear operations to the proximity matrix and perform efficient matrix factorization to derive the embedding vectors. Finally, we present an extensive experimental study that evaluates the effectiveness of various graph embedding algorithms, and we show that \strap outperforms the state-of-the-art methods in terms of effectiveness and scalability.

Graph embedding algorithms embed a graph into a vector space where the structure and the inherent properties of the graph are preserved. The existing graph embedding methods cannot preserve the asymmetric transitivity well, which is a critical property of directed graphs. Asymmetric transitivity depicts the correlation among directed edges, that is, if there is a directed path from u to v, then there is likely a directed edge from u to v. Asymmetric transitivity can help in capturing structures of graphs and recovering from partially observed graphs. To tackle this challenge, we propose the idea of preserving asymmetric transitivity by approximating high-order proximity which are based on asymmetric transitivity. In particular, we develop a novel graph embedding algorithm, High-Order Proximity preserved Embedding (HOPE for short), which is scalable to preserve high-order proximities of large scale graphs and capable of capturing the asymmetric transitivity. More specifically, we first derive a general formulation that cover multiple popular high-order proximity measurements, then propose a scalable embedding algorithm to approximate the high-order proximity measurements based on their general formulation. Moreover, we provide a theoretical upper bound on the RMSE (Root Mean Squared Error) of the approximation. Our empirical experiments on a synthetic dataset and three real-world datasets demonstrate that HOPE can approximate the high-order proximities significantly better than the state-of-art algorithms and outperform the state-of-art algorithms in tasks of reconstruction, link prediction and vertex recommendation.

ExEm: Expert embedding using dominating set theory with deep learning approaches

Entity resolution is the process of identifying the records that refer to the same entity. Blocking is the most important step in the entity resolution process, which allows optimizing the matching step by grouping the records into blocks in a way that only the records in the same block will be compared to each other. However, the blocking process can’t be done with certainty and confidence, such that records may be assigned to the wrong blocks causing the ignorance of many necessary comparisons. In this paper, we propose a Fuzzy/Probabilistic approach to overcome the uncertainty problem. Our approach defines two steps: Fuzzy Block Generator and Probabilistic filtering which aim to minimize the effect of uncertainty by assigning records to multiple possible blocks, and to reduce the number of unnecessary comparisons based on a probabilistic approach, respectively. Conducted experiments showcased the effectiveness and efficiency of our solution.

Emerging technologies for drug repurposing: Harnessing the potential of text and graph embedding approaches

Advances in machine learning and deep learning methods, together with the increasing availability of large-scale pharmacological, genomic, and chemical datasets, have created opportunities for identifying potentially useful relationships within biochemical networks. Knowledge embedding models have been found to have value in detecting knowledge-based correlations among entities, but little effort has been made to apply them to networks of biochemical entities. This is because such networks tend to be unbalanced and sparse, and knowledge embedding models do not work well on them. However, to some extent, the shortcomings of knowledge embedding models can be compensated for if they are used in association with graph embedding. In this paper, we combine knowledge embedding and graph embedding to represent biochemical entities and their relations as dense and low-dimensional vectors. We build a cascade learning framework which incorporates semantic features from the knowledge embedding model, and graph features from the graph embedding model, to score the probability of linking. The proposed method performs noticeably better than the models with which it is compared. It predicted links and entities with an accuracy of 93%, and its average hits@10 score has an average of 8.6% absolute improvement compared with original knowledge embedding model, 1.1% to 9.7% absolute improvement compared with other knowledge and graph embedding algorithm. In addition, we designed a meta-path algorithm to detect path relations in the biomedical network. Case studies further verify the value of the proposed model in finding potential relationships between diseases, drugs, genes, treatments, etc. Amongst the findings of the proposed model are the suggestion that VDR (vitamin D receptor) may be linked to prostate cancer. This is backed by evidence from medical databases and published research, supporting the suggestion that our proposed model could be of value to biomedical researchers.

This tutorial provides an overview of the key research results in the area of entity resolution that are relevant to addressing the new challenges in entity resolution posed by the Web of data, in which real world entities are described by interlinked data rather than documents. Since such descriptions are usually partial, overlapping and sometimes evolving, entity resolution emerges as a central problem both to increase dataset linking but also to search the Web of data for entities and their relations.

Traditionally, the study of complex networks has employed mathematical toolsfrom graph theory and statistical physics. More recently, topological data analysis has introduced the application of algebraic topology to network science, opening up new possibilities. This dissertation takes a similar direction by drawing on topological and geometrical aspects of graph theory to develop data mining algorithms that provide new scientific insights into complex networks. I consider the geometric structure of networks at many levels. First, I study the intrinsic metric structure of a graph using the theory of length spectrum and its relation to the eigenvalues of the non-backtracking matrix of the graph. I use this relationship to develop a principled algorithm for measuring graph distance. Second, I study the geometric structure of graph embeddings. Graph embedding techniques place a graph in a surrounding metric space. I use the geometry of this space to develop two embedding algorithms that enable the performance of such tasks such as link prediction and anomaly detection in an efficient and interpretable manner. Third, because considering distances between graphs depends on the assumption that graphs with similar structure exhibit similar behavior, I study how certain dynamical properties of graphs change under small perturbations of their structure. In particular, I focus on how small perturbations to the structure of the graph affect the epidemic threshold of certain dynamics. I introduce two algorithms that effectively manipulate the epidemic threshold by exploiting the relationship between the graph structure and its spectral properties. I conclude with a reflection on the current trend in network science to rethink the study of complex systems in terms of polyadic relations. My collaborators and I find that a necessary step in this direction is to focus on the properties of real-world systems that determine which relationships affect the existence of other relationships. I discuss this property, which I call dependency, in the context of graphs, simplicial complexes, and hypergraphs.--Author's abstract

The 2019 FEIII CALI data challenge aims at linking different representations of the same real-world entities across multiple public datasets that collect identification and activity data about small to medium enterprises (SMEs) in California. We formalize this challenge as a learning-based entity resolution (ER) task, the goal of which is to learn a high-precision and high-recall pair-wise ER model that classifies small business entity pairs into matches and non-matches. Realistic ER tasks usually involve a pipeline of laborintensive and error-prone tasks, such as data preprocesing, gathering of training data, feature engineering, and model tuning. In this task, we apply an advanced human-in-the-loop system, named SystemER, to learn ER algorithms for SME entities. Powered by active learning and via a carefully designed user interface, SystemER can learn high-quality explainable ER algorithms with low human effort, while achieving high-accuracy on the datasets provided by the FEIII CALI data challenge.

Entity Resolution (ER) is the process of identifying records in a database that refer to the same real-world entity. A top-N query in a database is to find a sorted set of N tuples that best, but not necessarily completely, satisfy the query condition. Traditional techniques of top-N query processing have not yet integrated with ER, and as a result, they will become invalid for dirty datasets with duplicate tuples. In this paper, we propose two methods for processing top-N queries with real-time ER. One employs learning mechanisms and utilizes the Select-From-Where SQL statements based on a relational database management system. The second method, while not using such SQL statements, is a database-friendly threshold algorithm using simple lists. Extensive experiments are conducted to measure the performance of the methods for both clean and dirty datasets.

Record linkage deals with finding records that identify the same real world entity, such as an individual or a business, from a given file or set of files. Record linkage problem is also referred to as the entity resolution or record recognition problem. To locate those records identifying the same real world entity, in principle, pairwise record analyses have to be performed among all records. Analytical operations between two records vary from comparing corresponding fields to enhancing records through large knowledge bases and querying large databases. Hence, these operations are complex and take time. To reduce the number of pairwise record comparisons, blocking techniques are introduced to partition the records into blocks. After that records in each block are analyzed against one and another. One of the effective blocking methods is the closure approach, where a “related” equivalence relation is used to partition the records into equivalence classes. This paper introduces the closure problem and describes the design and implementation of a parallel and distributed closure prototype system running in an enterprise grid.

The rapid growth of Android malware has posed severe security threats to smartphone users. On the basis of the familial trait of Android malware observed by previous work, the familial analysis is a promising way to help analysts better focus on the commonalities of malware samples within the same families, thus reducing the analytical workload and accelerating malware analysis. The majority of existing approaches rely on supervised learning and face three main challenges, i.e., low accuracy, low efficiency, and the lack of labeled dataset. To address these challenges, we first construct a fine-grained behavior model by abstracting the program semantics into a set of subgraphs. Then, we propose SRA, a novel feature that depicts the similarity relationships between the Structural Roles of sensitive API call nodes in subgraphs. An SRA is obtained based on graph embedding techniques and represented as a vector, thus we can effectively reduce the high complexity of graph matching. After that, instead of training a classifier with labeled samples, we construct malware link network based on SRAs and apply community detection algorithms on it to group the unlabeled samples into groups. We implement these ideas in a system called GefDroid that performs Graph embedding based familial analysis of AnDroid malware using unsupervised learning. Moreover, we conduct extensive experiments to evaluate GefDroid on three datasets with ground truth. The results show that GefDroid can achieve high agreements (0.707--0.883 in term of NMI) between the clustering results and the ground truth. Furthermore, GefDroid requires only linear run-time overhead and takes around 8.6s to analyze a sample on average, which is considerably faster than the previous work.

The relationship between gene co-expression and chromatin conformation is of great biological interest. Thanks to high-throughput chromosome conformation capture technologies (Hi-C), researchers are gaining insights on the tri-dimensional organization of the genome. Given the high complexity of Hi-C data and the difficult definition of gene co-expression networks, the development of proper computational tools to investigate such relationship is rapidly gaining the interest of researchers. One of the most fascinating questions in this context is how chromatin topology correlates with gene co-expression and which physical interaction patterns are most predictive of co-expression relationships. To address these questions, we developed a computational framework for the prediction of co-expression networks from chromatin conformation data. We first define a gene chromatin interaction network where each gene is associated to its physical interaction profile; then, we apply two graph embedding techniques to extract a low-dimensional vector representation of each gene from the interaction network; finally, we train a classifier on gene embedding pairs to predict if they are co-expressed. Both graph embedding techniques outperform previous methods based on manually designed topological features, highlighting the need for more advanced strategies to encode chromatin information. We also establish that the most recent technique, based on random walks, is superior. Overall, our results demonstrate that chromatin conformation and gene regulation share a non-linear relationship and that gene topological embeddings encode relevant information, which could be used also for downstream analysis. The source code for the analysis is available at: https://github.com/marcovarrone/gene-expression-chromatin. Supplementary data are available at Bioinformatics online.

In recent years, graph embedding techniques have exhibited great potential for various downstream tasks, which can leverage both topological structures and the temporal dependencies of nodes in their representations, leading to remarkable achievements. However, the multi-role nature of nodes during their temporally interacting is neglected. To tackle this problem, we propose a novel model, Temporal graph Multi-Aspect Embedding (TMAE), to capture the latent multi-aspect characteristics of nodes in temporal graphs, thereby enhancing the quality of graph embeddings. Specifically, we propose to learn the aspect embeddings of nodes and their weights at different timestamps separately for a better adaptation. In contrast to the conventional fixed aspect number assumption, a Hierarchical Dirichlet Process-based approach is employed to dynamically determine the weight of aspects for nodes at different times. Through this framework, we effectively learn the multi-aspect information through Time-reversed Temporal Walks (TTWs). Extensive experiments performed across eight publicly accessible datasets have demonstrated the significant improvements of the proposed TMAE model over state-of-the-art algorithms by taking advantage of the multi-aspect nature.

Automated detection of chronic kidney disease using image fusion and graph embedding techniques with ultrasound images