Multi-modal data fusion based on embeddings /:
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 fro...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Amsterdam :
IOS Press,
[2019]
|
| Schriftenreihe: | Studies on the Semantic Web ;
volume 041. |
| Schlagworte: | |
| Online-Zugang: | DE-862 DE-863 |
| Zusammenfassung: | 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. |
| Beschreibung: | 1 online resource |
| Bibliographie: | Includes bibliographical references. |
| ISBN: | 9781643680293 1643680293 |
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| 520 | |a 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. | ||
| 505 | 0 | |a 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 | |
| 650 | 0 | |a RDF (Document markup language) |0 http://id.loc.gov/authorities/subjects/sh2003010124 | |
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| 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 |
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| discipline | Allgemeines Informatik |
| format | Electronic eBook |
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| id | ZDB-4-EBA-on1127567489 |
| illustrated | Not Illustrated |
| indexdate | 2025-04-11T08:47:06Z |
| institution | BVB |
| isbn | 9781643680293 1643680293 |
| language | English |
| lccn | 2021288559 |
| oclc_num | 1127567489 |
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| series | Studies on the Semantic Web ; |
| series2 | Studies on the Semantic Web ; |
| spelling | Thoma, Steffen, author. Multi-modal data fusion based on embeddings / Steffen Thomas, FZI Forschungszentrum Informatik, Karslruhe, Germany. Amsterdam : IOS Press, [2019] ©2019 1 online resource text txt rdacontent computer c rdamedia online resource cr rdacarrier Studies on the Semantic Web ; volume 041 Online resource; title from PDF title page (EBSCO, viewed November 15, 2019). Includes bibliographical references. 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. 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 RDF (Document markup language) http://id.loc.gov/authorities/subjects/sh2003010124 Semantic Web. http://id.loc.gov/authorities/subjects/sh2002000569 RDF (Langage de balisage) Web sémantique. RDF (Document markup language) fast Semantic Web fast has work: Multi-modal data fusion based on embeddings (Text) https://id.oclc.org/worldcat/entity/E39PCGhT3vRc63Qmpp9YdkMF8C https://id.oclc.org/worldcat/ontology/hasWork Print version: Thoma, Steffen. Multi-modal data fusion based on embeddings. Amsterdam : IOS Press, [2019] 1643680285 9781643680286 (OCoLC)1127843339 Studies on the Semantic Web ; volume 041. |
| spellingShingle | Thoma, Steffen Multi-modal data fusion based on embeddings / Studies on the Semantic Web ; 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 RDF (Document markup language) http://id.loc.gov/authorities/subjects/sh2003010124 Semantic Web. http://id.loc.gov/authorities/subjects/sh2002000569 RDF (Langage de balisage) Web sémantique. RDF (Document markup language) fast Semantic Web fast |
| subject_GND | http://id.loc.gov/authorities/subjects/sh2003010124 http://id.loc.gov/authorities/subjects/sh2002000569 |
| title | Multi-modal data fusion based on embeddings / |
| title_auth | Multi-modal data fusion based on embeddings / |
| title_exact_search | Multi-modal data fusion based on embeddings / |
| title_full | Multi-modal data fusion based on embeddings / Steffen Thomas, FZI Forschungszentrum Informatik, Karslruhe, Germany. |
| title_fullStr | Multi-modal data fusion based on embeddings / Steffen Thomas, FZI Forschungszentrum Informatik, Karslruhe, Germany. |
| title_full_unstemmed | Multi-modal data fusion based on embeddings / Steffen Thomas, FZI Forschungszentrum Informatik, Karslruhe, Germany. |
| title_short | Multi-modal data fusion based on embeddings / |
| title_sort | multi modal data fusion based on embeddings |
| topic | RDF (Document markup language) http://id.loc.gov/authorities/subjects/sh2003010124 Semantic Web. http://id.loc.gov/authorities/subjects/sh2002000569 RDF (Langage de balisage) Web sémantique. RDF (Document markup language) fast Semantic Web fast |
| topic_facet | RDF (Document markup language) Semantic Web. RDF (Langage de balisage) Web sémantique. Semantic Web |
| work_keys_str_mv | AT thomasteffen multimodaldatafusionbasedonembeddings |