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Incremental knowledge base construction using DeepDive

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Abstract

Populating a database with information from unstructured sources—also known as knowledge base construction (KBC)—is a long-standing problem in industry and research that encompasses problems of extraction, cleaning, and integration. In this work, we describe DeepDive, a system that combines database and machine learning ideas to help develop KBC systems, and we present techniques to make the KBC process more efficient. We observe that the KBC process is iterative, and we develop techniques to incrementally produce inference results for KBC systems. We propose two methods for incremental inference, based, respectively, on sampling and variational techniques. We also study the trade-off space of these methods and develop a simple rule-based optimizer. DeepDive includes all of these contributions, and we evaluate DeepDive on five KBC systems, showing that it can speed up KBC inference tasks by up to two orders of magnitude with negligible impact on quality.

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Notes

  1. http://deepdive.stanford.edu.

  2. DeepDive has some technical differences from Markov Logic that we have found useful in building applications. We discuss these differences in Sect. 4.2.

  3. http://www.itl.nist.gov/iad/mig/tests/ace/2000/.

  4. http://www.freebase.com/.

  5. http://macrostrat.org/.

  6. There is a justification for probabilistic reasoning as Cox’s theorem asserts (roughly) that if one uses numbers as degrees of belief, then one must either use probabilistic reasoning or risk contradictions in one’s reasoning system, i.e., a probabilistic framework is the only sound system for reasoning in this manner. We refer the reader to Jaynes [34].

  7. For more information, including examples, please see http://deepdive.stanford.edu. Note that our engine is built on PostgreSQL and Greenplum for all SQL processing and UDFs. There is also a port to MySQL.

  8. Our system Tuffy introduced this feature to MLNs, but its semantics had not been described in the literature.

  9. For example, for the grounding procedure illustrated in Fig. 5, the delta rule for F1 is \(q^{\delta }(x) :- R^{\delta }(x,y)\).

  10. Compared with running inference from scratch, the strawman approach does not materialize any factors. Therefore, it is necessary for strawman to enumerate each possible world and save their probability because we do not know a priori which possible world will be used in the inference phase.

  11. In Fig. 7, the numbers are reported for a factor graph whose factor weights are sampled at random from \([-0.5,0.5]\). We also experimented with different intervals (\([-0.1,0.1]\), \([-1,1]\), \([-10,10]\)), but these had no impact on the trade-off

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Acknowledgments

We gratefully acknowledge the support of the Defense Advanced Research Projects Agency (DARPA) XDATA program under No. FA8750-12-2-0335 and DEFT program under No. FA8750-13-2-0039, DARPA’s MEMEX program and SIMPLEX program, the National Science Foundation (NSF) CAREER Award under No. IIS-1353606, the Office of Naval Research (ONR) under awards No. N000141210041 and No. N000141310129, the National Institutes of Health Grant U54EB020405 awarded by the National Institute of Biomedical Imaging and Bioengineering (NIBIB) through funds provided by the trans-NIH Big Data to Knowledge (BD2K) initiative, the Sloan Research Fellowship, the Moore Foundation, American Family Insurance, Google, and Toshiba. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of DARPA, AFRL, NSF, ONR, NIH, or the U.S. government.

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    Christopher De Sa, Alex Ratner, Christopher Ré, Jaeho Shin, Feiran Wang, Sen Wu & Ce Zhang

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  1. Christopher De Sa
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The original version of this paper is entitled “Incremental Knowledge Base Construction Using DeepDive” and was published in the Proceedings of the VLDB Endowment, 2015. This is an extended version of the original paper. This paper also contains content from the paper “DeepDive: Declarative Knowledge Base Construction” [62] published in the SIGMOD Record 2015.

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De Sa, C., Ratner, A., Ré, C. et al. Incremental knowledge base construction using DeepDive. The VLDB Journal 26, 81–105 (2017). https://doi.org/10.1007/s00778-016-0437-2

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