Titlebook: Database Systems for Advanced Applications; 27th International C Arnab Bhattacharya,Janice Lee Mong Li,Rage Uday Ki Conference proceedings

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LeKAN: Extracting Long-tail Relations via Layer-Enhanced Knowledge-Aggregation Networkse the ancestors of long-tailed relation classes into new relation representations to prevent the long-tailed relations from being ignored. Specifically, we use GraphSAGE to learn the relational knowledge from an existing knowledge graph via class embedding. Moreover, we aggregate the acquired relati

有其法作用

TRHyTE: Temporal Knowledge Graph Embedding Based on Temporal-Relational Hyperplanes relation space first, and then explicitly projects transformed entities and relations into temporal-relational hyperplanes to learn time-relation-aware embeddings. Moreover, Gate Recurrent Unit is leveraged to simulate TKG evolution so as to capture temporal dependency between adjacent hyperplanes.

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Improving Core Path Reasoning for the Weakly Supervised Knowledge Base Question Answeringard Expectation-Maximization to learn the best matching path iteratively. Extensive experiments on two popular KBQA datasets demonstrate the strong competitiveness of our model compared to previous state-of-the-art methods, especially in solving long path and spurious path problem.

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Approximate Continuous Top-K Queries over Memory Limitation-Based Streaming Datalides. Existing efforts include exact-based algorithms and approximate-based algorithms. Their common idea is maintaining a small subset of objects in the window. When the window slides, query results could be found from this set as much as possible. However, the space cost of all existing efforts i

协定

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Leveraging Search History for Improving Person-Job Fit However, existing studies mainly focus on the . scenario, while neglecting another important channel for linking positions with job seekers, . .. Intuitively, search history contains rich user behavior in job seeking, reflecting important evidence for job intention of users..In this paper, we prese