PIDGIN: ontology alignment using web text as interlingua

By Derry Tanti Wijaya, Partha P. Talukdar, Tom Michael Mitchell. 2013

41 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Molecular Biology, Natural Language Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. William W. Cohen
2. Jiawei Han
3. Ruslan Salakhutdinov
4. Zhilin Yang
5. Mohit Bansal
6. Jianfeng Gao
7. Luke S. Zettlemoyer
8. Percy Liang

In Information Systems

1. Carl Yang
2. Quan Yuan
3. Ruslan Salakhutdinov
4. Jiawei Han
5. Chao Zhang
6. Fabian M. Suchanek
7. Lanxiao Bai
8. William W. Cohen

In Molecular Biology

1. William W. Cohen
2. Joanna Asia Biega
3. Carl Yang
4. Ruslan Salakhutdinov
5. Jiawei Han
6. Fabian M. Suchanek
7. Zhilin Yang
8. Gerhard Weikum

In Natural Language Processing

1. Ruslan Salakhutdinov
2. Zhilin Yang
3. William W. Cohen
4. Jiawei Han
5. Mohit Bansal
6. Chao Zhang
7. Chenyi Zhuang
8. Carl Yang

Citations in the corpus

• Revisiting Semi-Supervised Learning with Graph Embeddings (2016, background): “The graph A can either be derived from distances between instances (Zhu et al., 2003), or be explicitly derived from external data, such as a knowledge graph (Wijaya et al., 2013) or a citation network between documents (Ji et al., 2010).”
• Dual Graph Convolutional Networks for Graph-Based Semi-Supervised Classification (2018, background): “The similarity can be derived by calculating the distances among data points [33], or may be explicitly given by structured data, such as knowledge graphs [29], citation graphs [14], hyperlinks between documents [20], and so on.”
• An Overview of Deep Semi-Supervised Learning (2020, unknown): “The adjacency matrix A can either be derived using a similarity measure between the data points [189, 72], or be explicitly derived from external data, such as a knowledge graph [163], and provided as input.”
• Bridging Collaborative Filtering and Semi-Supervised Learning: A Neural Approach for POI Recommendation (2017, methodology): “Œe major approaches are based on anity graphs, which can be either computed as distances among instances [1, 42, 58, 59], or derived from external data, such as knowledge graphs [43], social networks or citation networks [5, 18].”
• YAGO3: A Knowledge Base from Multilingual Wikipedias (2015, background): “A large number of works have looked into the alignment of entities, relations, and classes across KBs (see, e.g., [24, 29] and references therein for recent works).”