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|Title=Is Aligning Embedding Spaces a Challenging Task? An Analysis of the Existing Methods. | |Title=Is Aligning Embedding Spaces a Challenging Task? An Analysis of the Existing Methods. | ||
| − | |Year= | + | |Year=2020 |
|Month=Februar | |Month=Februar | ||
|Journal=CoRR abs/2002.09247 | |Journal=CoRR abs/2002.09247 | ||
| + | |Tags=Entity Alignment, Word-Entity Alignment, Knowledge Graph Embeddings, Word Embeddings, Vector Space Alignment | ||
}} | }} | ||
{{Publikation Details | {{Publikation Details | ||
| + | |Abstract=Representation Learning of words and Knowledge Graphs (KG) into low dimensional vector spaces along with its applications to many real-world scenarios have recently gained momentum. In order to make use of multiple KG embeddings for knowledge-driven applications such as question answering, named entity disambiguation, knowledge graph completion, etc., alignment of different KG embedding spaces is necessary. In addition to multilinguality and domain-specific informa- tion, different KGs pose the problem of structural differences making the alignment of the KG embeddings more challenging. This paper pro- vides a theoretical analysis and comparison of the state-of-the-art align- ment methods between two embedding spaces representing entity-entity and entity-word. This paper also aims at assessing the capability and short-comings of the existing alignment methods on pretext of different applications. | ||
| + | |Download=AlignmentSurvey.pdf | ||
| + | |Link=https://arxiv.org/pdf/2002.09247.pdf | ||
|Forschungsgruppe=Information Service Engineering | |Forschungsgruppe=Information Service Engineering | ||
}} | }} | ||
Aktuelle Version vom 17. November 2022, 08:44 Uhr
Is Aligning Embedding Spaces a Challenging Task? An Analysis of the Existing Methods.
Is Aligning Embedding Spaces a Challenging Task? An Analysis of the Existing Methods.
Veröffentlicht: 2020 Februar
Journal: CoRR abs/2002.09247
Nicht-referierte Veröffentlichung
Tags:Entity Alignment, Word-Entity Alignment, Knowledge Graph Embeddings, Word Embeddings, Vector Space Alignment
Kurzfassung
Representation Learning of words and Knowledge Graphs (KG) into low dimensional vector spaces along with its applications to many real-world scenarios have recently gained momentum. In order to make use of multiple KG embeddings for knowledge-driven applications such as question answering, named entity disambiguation, knowledge graph completion, etc., alignment of different KG embedding spaces is necessary. In addition to multilinguality and domain-specific informa- tion, different KGs pose the problem of structural differences making the alignment of the KG embeddings more challenging. This paper pro- vides a theoretical analysis and comparison of the state-of-the-art align- ment methods between two embedding spaces representing entity-entity and entity-word. This paper also aims at assessing the capability and short-comings of the existing alignment methods on pretext of different applications.
Download: Media:AlignmentSurvey.pdf
Weitere Informationen unter: Link
Information Service Engineering