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System overview and terminologies |
This document presents an overview of DeepDive as a system. It assumes that you are familiar with some general concepts like inference and factor graphs, relation extraction, and distant supervision. It describes each step performed during the execution of a DeepDive application:
The extraction step is a data transformation during which DeepDive processes the data to extract entities, perform entity linking, feature extraction, distant supervision, and any other task necessary to create the variables on which it will then perform inference, and, if needed, to generate the training data used for learning the factor weights. The tasks to perform during extraction are specified by defining extractors, which can also be user-defined functions (UDFs). The results of extraction are stored in the application database and will be then used to build the factor graph according to rules specified by the user.
DeepDive uses a factor graph to perform inference. The user writes SQL queries to instruct the system about which variables to create. These queries usually involve tables populated during the extraction step. The variable nodes of the factor graph are connected to factors according to inference rules specified by the user, who also defines the factor functions which describe how the variables are related. The user can specify whether the factor weights should be constant or learned by the system (refer to the 'Writing inference rules' document ).
Grounding is the process of writing the graph to disk so that it can be used to perform inference. DeepDive writes the graph to a set of five files: one for variables, one for factors, one for edges, one for weights, and one for metadata useful to the system. The format of these file is special so that they can be accepted as input by our sampler.
DeepDive can learn the weights of the factor graph from training data that can be either obtained through distant supervision or specified by the user while populating the database during the extraction phase. The main general way for learning the weights is maximum likelihood.
The learned weights are then written to a specific database table so that the user can inspect them during the calibration of the process.
The final step consists in performing marginal inference on the factor graph variables to learn the probabilities of different values they can take over all possible worlds. DeepDive uses our high-throughput DimmWitted sampler to perform Gibbs sampling, i.e., to go through many possible worlds and to estimate the probabilities. The sampler takes the grounded graph (i.e., the five files written during the grounding step) as input, together with a number of arguments to specify the parameters for the learning procedure. The results of the inference step are written to the database. The user can write queries to analyze the results. DeepDive also provides calibration data to evaluate the accuracy of the inference.