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  • Graph Embeddings for One-pass Processing of Heterogeneous Queries

    Author(s)
    Duong, Chi Thang
    Yin, Hongzhi
    Hoang, Dung
    Nguyen, Minn Hung
    Weidlich, Matthias
    Nguyen, Quoc Viet Hung
    Aberer, Karl
    Griffith University Author(s)
    Nguyen, Henry
    Year published
    2020
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    Abstract
    Effective information retrieval (IR) relies on the ability to comprehensively capture a user's information needs. Traditional IR systems are limited to homogeneous queries that define the information to retrieve by a single modality. Support for heterogeneous queries that combine different modalities has been proposed recently. Yet, existing approaches for heterogeneous querying are computationally expensive, as they require several passes over the data to construct a query answer.In this paper, we propose an IR system that overcomes the computational challenges imposed by heterogeneous queries by adopting graph embeddings. ...
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    Effective information retrieval (IR) relies on the ability to comprehensively capture a user's information needs. Traditional IR systems are limited to homogeneous queries that define the information to retrieve by a single modality. Support for heterogeneous queries that combine different modalities has been proposed recently. Yet, existing approaches for heterogeneous querying are computationally expensive, as they require several passes over the data to construct a query answer.In this paper, we propose an IR system that overcomes the computational challenges imposed by heterogeneous queries by adopting graph embeddings. Specifically, we propose graph-based models in which both, data and queries, incorporate information of different modalities. Then, we show how either representation is transformed into a graph embedding in the same space, capturing relations between information of different modalities. By grounding query processing in graph embeddings, we enable processing of heterogeneous queries with a single pass over the data representation. Our experiments on several real-world and synthetic datasets illustrate that our technique is able to return twice the amount of relevant information in comparison with several baselines, while being scalable to large-scale data.
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    Conference Title
    2020 IEEE 36th International Conference on Data Engineering (ICDE)
    DOI
    https://doi.org/10.1109/icde48307.2020.00222
    Subject
    Artificial intelligence
    Publication URI
    http://hdl.handle.net/10072/399257
    Collection
    • Conference outputs

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