text: This paper explores the problem of matching entities across different knowledge graphs. Given a query entity in one knowledge graph, we wish to find the corresponding real-world entity in another knowledge graph. We formalize this problem and present two large-scale datasets for this task based on exiting cross-ontology links between DBpedia and Wikidata, focused on several hundred thousand ambiguous entities. Using a classification-based approach, we find that a simple multi-layered perceptron based on representations derived from RDF2Vec graph embeddings of entities in each knowledge graph is sufficient to achieve high accuracy, with only small amounts of training data. The contributions of our work are datasets for examining this problem and strong baselines on which future work can be based.
  @cite_3: Entity matching (EM) has been a long-standing challenge in data management. Most current EM works focus only on developing matching algorithms. We argue that far more efforts should be devoted to building EM systems. We discuss the limitations of current EM systems, then present as a solution Magellan, a new kind of EM systems. Magellan is novel in four important aspects. (1) It provides how-to guides that tell users what to do in each EM scenario, step by step. (2) It provides tools to help users do these steps; the tools seek to cover the entire EM pipeline, not just matching and blocking as current EM systems do. (3) Tools are built on top of the data analysis and Big Data stacks in Python, allowing Magellan to borrow a rich set of capabilities in data cleaning, IE, visualization, learning, etc. (4) Magellan provides a powerful scripting environment to facilitate interactive experimentation and quick "patching" of the system. We describe research challenges raised by Magellan, then present extensive experiments with 44 students and users at several organizations that show the promise of the Magellan approach.
  @cite_8: Despite the efforts in 70+ years in all aspects of entity resolution (ER), there is still a high demand for democratizing ER - by reducing the heavy human involvement in labeling data, performing feature engineering, tuning parameters, and defining blocking functions. With the recent advances in deep learning, in particular distributed representations of words (a.k.a. word embeddings), we present a novel ER system, called DeepER, that achieves good accuracy, high efficiency, as well as ease-of-use (i.e., much less human efforts). We use sophisticated composition methods, namely uni- and bi-directional recurrent neural networks (RNNs) with long short term memory (LSTM) hidden units, to convert each tuple to a distributed representation (i.e., a vector), which can in turn be used to effectively capture similarities between tuples. We consider both the case where pre-trained word embeddings are available as well the case where they are not; we present ways to learn and tune the distributed representations that are customized for a specific ER task under different scenarios. We propose a locality sensitive hashing (LSH) based blocking approach that takes all attributes of a tuple into consideration and produces much smaller blocks, compared with traditional methods that consider only a few attributes. We evaluate our algorithms on multiple datasets (including benchmarks, biomedical data, as well as multi-lingual data) and the extensive experimental results show that DeepER outperforms existing solutions.
summary: Research related to knowledge graph integration comes from the database community and focuses on ontology matching---referred to as record-linkage, entity resolution, or deduplication. Examples include @cite_3 , @cite_8 , and the work of . The primary difference between this work and ours is that they assume relational structure and that the tables to be matched have been already aligned using schema matching techniques. These systems cannot be directly applied to entity matching across knowledge graphs due to differences in structure between the relational model and the RDF model.