國立虎尾科技大學 |

Multi-modal data fusion based on embeddings

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Multi-modal data fusion based on embeddings/ Steffen Thoma, FZI Forschungszentrum Informatik, Karslruhe, Germany.
作者:	Thoma, Steffen.
出版者:	Netherlands :IOS Press, : 2019,
面頁冊數:	1 online resource (xxii, 150 p.)
標題:	Semantic Web. -
電子資源:	http://ebooks.windeal.com.tw/ios/cover.asp?isbn=9781643680286
ISBN:	9781643680293 (ebk.)

Multi-modal data fusion based on embeddings
Thoma, Steffen.

Multi-modal data fusion based on embeddings[electronic resource] /Steffen Thoma, FZI Forschungszentrum Informatik, Karslruhe, Germany. - Netherlands :IOS Press,2019 - 1 online resource (xxii, 150 p.) - Studies on the Semantic Web.

Includes bibliographical references (p. 113-133).

"Many web pages include structured data in the form of semantic markup, which can be transferred to the Resource Description Framework (RDF) or provide an interface to retrieve RDF data directly. This RDF data enables machines to automatically process and use the data. When applications need data from more than one source the data has to be integrated, and the automation of this can be challenging. Usually, vocabularies are used to concisely describe the data, but because of the decentralized nature of the web, multiple data sources can provide similar information with different vocabularies, making integration more difficult. This book, Multi-modal Data Fusion based on Embeddings, describes how similar statements about entities can be identified across sources, independent of the vocabulary and data modeling choices. Previous approaches have relied on clean and extensively modeled ontologies for the alignment of statements, but the often noisy data in a web context does not necessarily adhere to these prerequisites. In this book, the use of RDF label information of entities is proposed to tackle this problem. In combination with embeddings, the use of label information allows for a better integration of noisy data, something that has been empirically confirmed by experiment. The book presents two main scientific contributions: the vocabulary and modeling agnostic fusion approach on the purely textual label information, and the combination of three different modalities into one multi-modal embedding space for a more human-like notion of similarity. The book will be of interest to all those faced with the problem of processing data from multiple web-based sources."-- Back cover.

ISBN: 9781643680293 (ebk.)

LCCN: 2021288559Subjects--Topical Terms:

559260
Semantic Web.

LC Class. No.: QA76.76.R34 / T66 2019

Dewey Class. No.: 025.0427

Multi-modal data fusion based on embeddings
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