Elementary: Large-Scale Knowledge-Base Construction via Machine Learning and Statistical Inference

By Feng Niu, Ce Zhang, Christopher R'e, Jude W. Shavlik. 2012

101 citations recorded by the source corpus.

Researchers have approached knowledge-base construction (KBC) with a wide range of data resources and techniques. The authors present Elementary, a prototype KBC system that is able to combine diverse resources and different KBC techniques via machine learning and statistical inference to construct knowledge bases. Using Elementary, they have implemented a solution to the TAC-KBP challenge with quality comparable to the state of the art, as well as an end-to-end online demonstration that automatically and continuously enriches Wikipedia with structured data by reading millions of webpages on a daily basis. The authors describe several challenges and their solutions in designing, implementing, and deploying Elementary. In particular, the authors first describe the conceptual framework and architecture of Elementary to integrate different data resources and KBC techniques in a principled manner. They then discuss how they address scalability challenges to enable Web-scale deployment. The authors empirically show that this decomposition-based inference approach achieves higher performance than prior inference approaches. To validate the effectiveness of Elementary’s approach to KBC, they experimentally show that its ability to incorporate diverse signals has positive impacts on KBC quality.

Subfields: Artificial Intelligence, Management Science and Operations Research, 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 Artificial Intelligence

1. Christopher R'e
2. Xinyi Zhou
3. Li Fei-Fei
4. David Poole
5. Alexander J. Ratner
6. Mohit Bansal
7. Quoc V. Le
8. Yuke Zhu

In Management Science and Operations Research

1. Filippo Menczer
2. Alessandro Flammini
3. Sriraam Natarajan
4. Luc De Raedt
5. Christopher R'e
6. David Poole
7. Kristian Kersting
8. Alexander J. Ratner

In Information Extraction

1. Sriraam Natarajan
2. Johan Bollen
3. Ce Zhang
4. Ni Lao
5. Xiao Liu
6. Christopher R'e
7. Yuke Zhu
8. Li Fei-Fei

In Knowledge Graphs & Representation

1. Xinyi Zhou
2. David Poole
3. Kristian Kersting
4. Sriraam Natarajan
5. Kevin P. Murphy
6. Evgeniy Gabrilovich
7. Maximilian Nickel
8. Luc De Raedt

Citations in the corpus

• A Review of Relational Machine Learning for Knowledge Graphs (2015, methodology): “One approach for computing posterior marginals is to use Gibbs sampling (see, or example, [31] and [127]) or MC-SAT [128].”
• Language Models are Open Knowledge Graphs (2020, background): “, 2010), DeepDive (Niu et al., 2012), Knowledge Vault (Dong et al.”
• Incremental Knowledge Base Construction Using DeepDive (2015, background): “As with Google’s Knowledge Vault [14] and others [31], DeepDive also produces marginal probabilities that are calibrated: if one examined all facts with probability 0.”
• A Survey of Fake News (2020, background): “…can rely on existing large-scale ones, such as YAGO [Hoffart et al. 2013; Suchanek et al. 2007], Freebase [Bollacker et al. 2008], NELL [Carlson et al. 2010], PATTY [Nakashole et al. 2012], DBpedia [Auer et al. 2007], Elementary/DeepDive [Niu et al. 2012], and Knowledge Vault [Dong et al. 2014].”
• Reasoning about Object Affordances in a Knowledge Base Representation (2014, methodology): “Similar to this work, StatSnowball [38] and Elementary [26] use Markov Logic Networks [28] as the underlying knowledge representation and perform statistical inference for knowledge base construction.”
• Knowledge vault: a web-scale approach to probabilistic knowledge fusion (2014, methodology): “In recent years, several large-scale knowledge bases (KBs) have been constructed, including academic projects such as YAGO [39], NELL [8], DBpedia [3], and Elementary/ DeepDive [32], as well as commercial projects, such as those by Microsoft, Google, Facebook, Walmart [9], and others.”
• DeepDive: Declarative Knowledge Base Construction (2016, background): “As with Google’s Knowledge Vault [11] and others [32], DeepDive also produces marginal probabilities that are calibrated : if one examined all facts with proba-http://deepdive.stanford.edu bility 0.9, we would expect approximately 90% of these facts to be correct.”
• Fake News: A Survey of Research, Detection Methods, and Opportunities (2018, background): “…knowledge graphs have been constructed, e.g., YAGO [Hoffart et al. 2013; Suchanek et al. 2007], Freebase18 [Bollacker et al. 2008], NELL [Carlson et al. 2010], PATTY [Nakashole et al. 2012], DBpedia [Auer et al. 2007], Elementary/DeepDive [Niu et al. 2012], and Knowledge Vault [Dong et al. 2014].”
• Statistical Relational Artificial Intelligence: Logic, Probability, and Computation (2016, background): “StarAI helps bring dark data to light (see, e.g., [Niu et al., 2012, Venugopal et al., 2014]), making the knowledge base construction, as illustrated in Fig.”
• Computational Fact Checking from Knowledge Networks (2015, background): “…(fellowship 142353, http://snf.ch), the Lilly Endowment (http:// www.lillyendowment.org/), the James S. McDonnell Foundation (https://www.jsmf.org/), the National Science Foundation (grant CCF-1101743, http://www. nsf.gov/), and Department of Defense (grant W911NF-12-1-0037, http://www.darpa.mil/).”