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E-book Entity Alignment : Concepts, Recent Advances and Novel Approaches

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Penilaian

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In the past few years, there has been a significant increase in the use and development of KGs and their various applications. These KGs are designed to store world knowledge, represented as triples (i.e., .) consisting of entities, relations, and other entities, with each entity referring to a distinct real-world object, and each relation representing a connection between those objects. Since these entities serve as the foundation for the triples in a KG, the triples are inherently interconnected, creating a large and complex graph of knowledge. Currently, we have a large number of general KGs (e.g., DBpedia [1], YAGO [52], Google’s Knowledge Vault [14]) and domain-specific KGs (e.g., medical [48] and scientific KGs [56]). KGs have been utilized to improve a wide range of downstream applications, including but not limited to keyword search [64], fact-checking [30], and question answering [12, 28]. A knowledge graph, denoted as .G = (E,R,T), is a graph that consists of three main components: a set of entities E, a set of relations R, and a set of triples T , where.T ? E ×R ×Erepresents the directed edges in the graph. In the set of triples T , a single triple.(h,r,t)represents a relationship between a head entity hand a tail entity t through a specific relation r. Each entity in the graph is identified by a unique identifier, such as http://dbpedia.org/resource/Spain in the case of DBpedia. In practice, KGs are typically constructed from a single data source, making it difficult to achieve comprehensive coverage of a given domain [46]. To improve the completeness of a KG, one popular strategy is to integrate information from other KGs that may contain supplementary or complementary data. For instance, a general KG may only include basic information about a scientist, while scientific domain-specific KGs may have additional details like biographies and lists of publications. o combine knowledge across multiple KGs, a crucial step is to align equivalent entities in different KGs, which is known as entity alignment (EA) [7, 25].1 Given a source KG.G1= (E1,R1,T1), a target KG.G2= (E2,R2,T2), and seed entity pairs (training set), i.e., .S ={(u, v) | u ? E1,v ? E2,u ? v}, where .?represents equivalence (i.e., uand vrefer to the same real-world object), the task of EA can be defined as discovering the equivalent entity pairs in the test set.

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Ketersediaan

Informasi Detail

Judul Seri

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No. Panggil

658.05 ZHA e

Penerbit

Singapore : Springer Nature., 2023

Deskripsi Fisik

252 hlm

Bahasa

English

ISBN/ISSN

9789819942503

Klasifikasi

658.05

Tipe Isi

text

Tipe Media

computer

Tipe Pembawa

online resource

Edisi

-

Subjek

Data Processing and Analysis of Management

Info Detail Spesifik

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Pernyataan Tanggungjawab

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Versi lain/terkait

Lampiran Berkas

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