Combining knowledge graphs
http://www.semanlink.net/tag/combining_knowledge_graphs
Documents tagged with Combining knowledge graphsBERT-INT: A BERT-based Interaction Model For Knowledge Graph Alignment
http://www.semanlink.net/doc/2022/05/bert_int_a_bert_based_interact
2022-05-11T18:03:42Z[1806.06478] Co-training Embeddings of Knowledge Graphs and Entity Descriptions for Cross-lingual Entity Alignment
http://www.semanlink.net/doc/2020/09/1806_06478_co_training_embedd
> Since many multilingual KGs also provide
literal descriptions of entities, in this paper,
we introduce an embedding-based approach which
leverages a weakly aligned multilingual KG for
semi-supervised cross-lingual learning using entity
descriptions
2020-09-06T16:59:29Z[1807.08447] LinkNBed: Multi-Graph Representation Learning with Entity Linkage
http://www.semanlink.net/doc/2020/05/1807_08447_linknbed_multi_gr
> a deep relational learning framework that **learns entity and relationship representations across multiple graphs**. We identify entity linkage across graphs as a vital component to achieve our goal. We design a novel objective that leverage entity linkage and build an efficient multi-task training procedure
>
> We posit that **combining
graph alignment task with deep representation
learning across multi-relational graphs** has potential
to induce a synergistic effect on both tasks
2020-05-11T22:30:47ZBootstrapping Entity Alignment with Knowledge Graph Embedding | IJCAI
http://www.semanlink.net/doc/2020/05/bootstrapping_entity_alignment_
Embedding-based entity alignment: finds entity alignment by measuring the similarities between entity embeddings
> Existing approaches are challenged by the lack of enough prior alignment as labeled training data.
> A bootstrapping approach: it iteratively labels likely entity alignment as training data for learning alignment-oriented KG embeddings.
[GitHub](https://github.com/nju-websoft/BootEA)
2020-05-11T21:59:04ZIterative Entity Alignment with Improved Neural Attribute Embedding
http://www.semanlink.net/doc/2020/04/iterative_entity_alignment_with
2020-04-29T19:04:03ZCombining knowledge graphs, quickly and accurately (2020)
http://www.semanlink.net/doc/2020/03/combining_knowledge_graphs_qui
Entity matching at Amazon: a new [#entity alignment](/tag/entity_alignment) technique that factors in information about the graph in the vicinity of the entity name.
[#Graph neural network](/tag/graph_neural_networks) that specifically addresses the problem of **merging multi-type knowledge graphs**.
2020-03-19T21:33:27ZAdvancing Natural Language Processing (NLP) for Enterprise Domains
http://www.semanlink.net/doc/2020/01/advancing_natural_language_proc
Reviews 4 papers by IBM research.
Introductive remark: the specificities of search in enterprises when compared to the web:
content stored in silos with much less repetition of key information,
intricate questions expecting detailed answers,
reluctance to blackbox.
Regarding NLP: silos, incomplete data, small data, changing environment.
-> 3 themes of research at IBM Research to improve NLP for enterprises:
- systems that can work with small data, external knowledge and use neurosymbolic approaches to language
- explainability on how a system reached a conclusion
- scaling to allow continuous adaptation
2020-01-07T12:05:46Z