Entity alignment (EA) is a critical task in knowledge fusion, focusing on identifying equivalent entities in different knowledge graphs (KGs). As representation learning techniques have advanced, EA methods have achieved notable improvements on current EA datasets, and several benchmark studies have been conducted. However, we have identified two limitations with respect to existing benchmarks. (1) They perform coarse-grained evaluation, which analyzes each EA approach as a whole. However, a typical EA framework consists of multiple modules, each of which has different strategies. The combinations of these strategies may provide more optimization opportunities, which are unexplored in current studies. (2) Current EA datasets tested in existing studies always contain dense information. However, real-world applications are often with noisy and missing data, which introduces complexities for EA tasks. To address this, we propose a new benchmark that explores the design space of EA framework, which consists of the embedding, relation, attribute and alignment module. Each module has multiple strategies. We also synthesize multiple datasets based on real-world datasets to cover different complex scenarios. Based on the design space and various datasets, we aim to provide a general guideline that recommends the most effective strategy for EA under practical settings. We conduct extensive experiments via comparing 13 baseline methods over 4 real datasets and 12 synthesized datasets. Based on the experimental observations, we also propose a new EA method that outperforms existing baselines.

The success of blockchain technology in cryptocurrencies reveals its potential in the data management field. Recently, there is a trend in the database community to integrate blockchains and traditional databases to obtain security, efficiency, and privacy from the two distinctive but related systems. In this survey, we discuss the use of blockchain technology in the data management field and focus on the fusion system of blockchains and databases. We first classify existing blockchain-related data management technologies by their locations on the blockchain-database spectrum. Based on the taxonomy, we discuss three types of fusion systems and analyze their design spaces and trade-offs. Then, by further investigating the typical systems and techniques of each type of fusion system and comparing the solutions, we provide insights of each fusion model. Finally, we outline the unsolved challenges and promising directions in this field and believe that fusion systems will take a more important role in data management tasks. We hope this survey can help both academia and industry to better understand the advantages and limitations of blockchain-related data management systems and develop fusion systems that meet various requirements in practice.

Knowledge-based data service has become an emerging form of service in the world wide web (WWW). To ensure the service quality, a comprehensive knowledge base has to be constructed. Knowledge base integration is often a primary way to improve the completeness. In this paper, we focus on the fundamental problem in knowledge base integration, i.e., entity alignment (EA). EA has been studied for years. Traditional approaches focus on the symbolic features of entities and propose various similarity measures to identify equivalent entities. With recent development in knowledge graph representation learning, embedding-based entity alignment has emerged, which encodes the entities into vectors according to the semantic or structural information and computes the relatedness of entities based on the vector representation. While embedding-based approaches achieve promising results, we identify some important information that are not well exploited in existing works: 1) The neighboring entities contribute differently in the EA process, and should be carefully assigned the importance in learning the relatedness of entities; 2) The attribute values (especially the long texts) contain rich semantics that can build supplementary associations between entities. To this end, we propose SDEA - a Semantics Driven entity embedding method for Entity Alignment. SDEA consists of two modules, namely attribute embedding and relation embedding. The attribute embedding captures the semantic information from attribute values with a pre-trained transformer-based language model. The relation embedding selectively aggregates the semantic information from neighbors using a GRU model equipped with an attention mechanism. Both attribute embedding and relation embedding are driven by semantics, building bridges between entities. Experimental results show that our method significantly outperforms the state-of-the-art approaches on three benchmarks.

Databases in the past have helped businesses maintain and extract insights from their data. Today, it is common for a business to involve multiple independent, distrustful parties. This trend towards decentralization introduces a new and important requirement to databases: the integrity of the data, the history, and the execution must be protected. In other words, there is a need for a new class of database systems whose integrity can be verified (or verifiable databases). In this paper, we identify the requirements and the design challenges of verifiable databases. We observe that the main challenges come from the need to balance data immutability, tamper evidence, and performance. We first consider approaches that extend existing OLTP and OLAP systems with support for verification. We next examine a clean-slate approach, by describing a new system, Spitz, specifically designed for efficiently supporting immutable and tamper-evident transaction management. We conduct a preliminary performance study of both approaches against a baseline system, and provide insights on their performance.