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Multi-modal data fusion based on embeddings

Author: Steffen Thoma
Publisher: Amsterdam : IOS Press, [2019] ©2019
Series: Studies on the Semantic Web, volume 041.
Edition/Format:   eBook : Document : EnglishView all editions and formats
Summary:
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,  Read more...
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Genre/Form: Electronic books
Additional Physical Format: (OCoLC)1127843339
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Steffen Thoma
ISBN: 9781643680293 1643680293
OCLC Number: 1127567489
Description: 1 online resource.
Contents: Intro; Title Page; Introduction; Motivation; Challenges; Hypotheses and Research Questions; Contributions; Outline; Foundations; Semantic Web; Representation Learning; Data Fusion; Introduction; Motivating Example; Related Work; Pipeline; Experiments; Summary; Multi-modal Fusion and Transfer; Introduction; Motivating Example; Related Work; Multi-modal Fusion; Experiments on Multi-modal Fusion; Multi-modal Transfer; Experiments on Multi-modal Transfer; Summary; Conclusion; Summary; Future Work; Bibliography; Appendix; Full Michael Jordan Example; Evaluation Tables; Evaluation Heatmaps
Series Title: Studies on the Semantic Web, volume 041.
Responsibility: Steffen Thomas, FZI Forschungszentrum Informatik, Karslruhe, Germany.

Abstract:

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.

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