Toward Never Ending Language Learning

By Justin Betteridge, Andrew Carlson, S. Hong, Estevam R. Hruschka, Edith Law, Tom Michael Mitchell, Sophie H. Wang. 2009

46 citations recorded by the source corpus.

Subfields: Information Extraction, Knowledge Graphs & Representation

How researchers see this paper

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

In Information Extraction

1. Ce Zhang
2. Christopher R'e
3. Yi Yang

In Knowledge Graphs & Representation

1. Ce Zhang
2. Christopher De Sa
3. Christopher R'e

Citations in the corpus

• Structured Embedding via Pairwise Relations and Long-Range Interactions in Knowledge Base (2015, methodology): “…which is very useful for the incremental knowledge learning because we do not need to re-train all of the entities/relations when new (unobserved) entities or relations are added into a growing knowledge graph like NELL (Betteridge et al. 2009; Carlson et al. 2010) or KV (Dong et al. 2014).”
• DeepDive: Declarative Knowledge Base Construction (2016, methodology): “Within this space, there are three styles of systems that based on classification-based frameworks [2,3,6,12,45], maximum a posteriori (MAP) [21,29, 40], and probabilistic graphical models [10, 35, 49].”
• Incremental Knowledge Base Construction Using DeepDive (2015, methodology): “Knowledge Base Construction (KBC) KBC has been an area of intense study over the last decade, moving from pattern matching [19] and rule-based systems [25] to systems that use machine learning for KBC [5, 8, 14, 15, 28].”