Graph-based Aspect Representation Learning for Entity Resolution (2020)

Abstract: Entity Resolution (ER) identifies records that refer to the same real-world entity. Deep learning approaches improved the generalization ability of entity matching models, but hardly overcame the impact of noisy or incomplete data sources. In real scenes, an entity usually consists of multiple semantic facets, called aspects. In this paper, we focus on entity augmentation, namely retrieving the values of missing aspects. The relationship between aspects is naturally suitable to be represented by a knowledge graph, where entity augmentation can be modeled as a link prediction problem. Our paper proposes a novel graph-based approach to solve entity augmentation. Specifically, we apply a dedicated random walk algorithm, which uses node types to limit the traversal length, and encodes graph structure into low-dimensional embeddings. Thus, the missing aspects could be retrieved by a link prediction model. Furthermore, the augmented aspects with fixed orders are served as the input of a deep Siamese BiLSTM network for entity matching. We compared our method with state-of-the-art methods through extensive experiments on downstream ER tasks. According to the experiment results, our model outperforms other methods on evaluation metrics (accuracy, precision, recall, and f1-score) to a large extent, which demonstrates the effectiveness of our method.

## introduction entity resolution has a tremendous impact on applications and research, such as deduplication, record linkage and canonicalization. it is a common challenge in various domains including digital libraries, ecommerce, natural language understanding, etc. applying deep learning methods to solve er problems has become a current research hotspot. these kinds of approaches have good generalization capability to improve the accuracy of prediction values on unseen data. one of the remaining challenges in tackling er tasks is the poor quality of data, such as missing values and ambiguity. this makes pairwise distance measures approaches less effective with noisy content and context. in real-world applications, different types of aspects often interact with each other to form heterogeneous relations @xcite in almost all networks. another challenge of er lies in how to express the relationships between the heterogeneous nature of aspects by proper data structure. advanced graph representation learning (grl), also called graph embedding, aiming to learn lowdimensional representations of nodes in networks, has attracted considerable attention in many real applications of networks @xcite . the universal pattern of these learning approaches is employing various types of random walk to generate node sequences and applying language models to map nodes into the same semantic vector space. in knowledge graph (kg), several related nodes often jointly represent a structural identity. an example of graph embedding in an aspect-based kg is shown in figure 1 . thickness of the edge represents its weight. this method learns a latent space representation of aspects, which can be applied by downstream machine learning tasks. an entity is composed of a set of aspects, and the relationship of aspects is easy to be represented in the form of graphs. in this paper, grl is introduced to resolve entity augmentation in er problems. we apply a heuristic feedback mechanism to the grl field, which has long been proven successful in handling combinatorial optimization problems. this mechanism can significantly reduce the aggregation phenomenon caused by the long tail distribution of aspects, and generate more diverse and reasonable traversal sequences. we develop an algorithm (aspect2vec) that learns the latent representation of aspects in a kg, by modeling a stream of random walks. aspect2vec applies neural language models to process a special language composed of a set of heuristically-generated walks. the latent space representation of aspects would capture neighborhood similarity. we apply aspect2vec to resolve entity augmentation. in the first place, link prediction in kg is implemented and used to estimate the likelihood of linkages between aspects. this step can retrieve missing aspects of entities. then, deep siamese networks are constructed to generate high-quality hash codes based on semantic-preserving vectors of aspect sequences. finally, the hashing method is employed to evaluate the performance of pairwise matching in er. our main contributions are as follows: normally kg is constructed from the observed interactions between aspects, which may be incomplete or inaccurate. thus the challenge of data augmentation lies in measuring the likelihood of links between aspects. ## experimental evaluation our experiments on aspect2vec consist of two parts, namely link prediction and entity resolution. each experiment compares aspect2vec with several state-of-the-art graph embedding methods, including deepwalk @xcite , line @xcite , node2vec @xcite and @xcite on two e-commerce datasets. the comparison includes link prediction as well as pairwise matching by hash codes. ## related work entity resolution has attracted the interest of a large number of researchers in recent years. with the development of deep learning (dl), a growing number of dl methods are applied to solve er problems @xcite . end-to-end deep matching models @xcite @xcite adopt similarity measures or semantic features of attributes for er, especially dealing with heterogeneous entities. dl often requires a lot of labeled data as a training set, which is expensive to obtain. therefore, transfer learning methods, based on a pre-trained model, are employed to solve er tasks with little or no training data @xcite . besides, there have been many unsupervised methods to solve the data labeling problem, particularly focusing on machine labeling and error label correction @xcite @xcite . some of the methods mentioned above pay attention to overcoming the dirty or heterogeneous data. however, how to deal with incomplete data and augment data quality in er still needs further research. the method we proposed applies graph representation learning to resolve this problem. graph representation learning is dedicated to mapping nodes in networks into the same vector space, while maintaining the semantic association between nodes @xcite @xcite @xcite @xcite . this kind of technique has received significant attention in the last few years with the development of natural language processing. the quality of the generated vectors is often measured by link prediction and node classification @xcite @xcite . previous researchers focused on the breadth and depth of graph traversal, but few of them take the node type into consideration during the progress of the random walk. in addition, how to avoid the long tail phenomenon as well as generating reasonable sequences in the traversal process is also a problem worth exploring. ## conclusion in this paper, we proposed a novel aspect representation learning framework aspect2vec, which resolves the entity augmentation problem in er by modeling it as a link prediction problem in kg. as-pect2vec collaboratively explores dedicated random walks and captures semantic information between nodes in a network. moreover, through extensive experiments on link prediction and deep semantic hashing, we demonstrated the superiority of the proposed framework to several state-of-the-art methods. furthermore, dedicated random walk is flexible and also has great potential capability of parallelism to be explored in future research.