Linking graph entities with multiplicity and provenance

Jixue Liu, Selasi Kwashie, Jiuyong Li, Lin Liu, Michael Bewong

Research output: Book chapter/Published conference paperConference paperpeer-review

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Entity linking and resolution is a fundamental database problem with applications in data integration, data cleansing, information retrieval, knowledge fusion, and knowledge-base population. It is the task of accurately identifying multiple, differing, and possibly contradicting representations of the same real-world entity in data.In this work, we propose an entity linking and resolution system capable of linking entities across different databases and mentioned entities extracted from text data. Our entity linking/resolution solution, called Certus, uses a graph model to represent the profiles of entities. The graph model is versatile, thus, it is capable of handling multiple values for an attribute or a relationship, as well as the provenance descriptions of the values. Provenance descriptions of a value provide the settings of the value, such as validity periods, sources, security requirements, etc. This paper presents the architecture for the entity linking system, the logical, physical, and indexing models used in the system, and the general linking process.Furthermore, we demonstrate the performance of update operations of the physical storage models when the system is implemented in two state-of-the-art database management systems, HBase andPostgres.
Original languageEnglish
Title of host publicationProceedings of the 2nd International workshop on EntitY REtrieval (EYRE 2019), Beijing, China, November 3, 2019
EditorsGong Cheng, Kalpa Gunaratna, Jun Wang
PublisherAssociation for Computing Machinery (ACM)
Number of pages7
Publication statusPublished - 2019
Event2nd International Workshop on EntitY REtrieval: EYRE 2019 - Beijing, China
Duration: 03 Nov 201903 Nov 2019


Conference2nd International Workshop on EntitY REtrieval
OtherAn analysis of real query logs from a commercial search engine shows that the intention of more than half of Web queries is to find a particular entity, or entities of a particular type. This problem of entity retrieval, or more generally, semantic search, has received increasing research attention from both the Information Retrieval (IR) and Semantic Web communities. Beyond the traditional text-based retrieval problem, the recent surge in entity-centered structured data on the Web such as Wikidata enables more powerful entity retrieval solutions, but also brings new challenges. This hybrid of unstructured and structured retrieval has led to diversified research from not only the IR community but also researchers and practitioners in the areas of Database, Semantic Web, and Artificial Intelligence (AI). This workshop series provides a platform where interdisciplinary studies of entity retrieval can be presented, and focused discussions can take place.
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