# Supervision Completion

LoMRF employs online supervision completion in order to complete potentially missing labels in a sequence of training files (micro-batches). Then these completed training files can be used as an input into any learning algorithm provided by the LoMRF CLIs. The main benefit of the on-line approach is that it can scale to problems with large amount of data.

Note: Supervision completion is a new feature and its currently experimental.

## Types of supervision completion in LoMRF

In order to perform supervision completion in LoMRF the following definitions are required:

• Input theory file containing the predicate schema and a function schema if any exists, e.g., schema.mln.
• A directory of many training data files (micro-batches) containing evidence and partial supervision, e.g., /path/to/training/data/micro/batches/.
• A mode declaration file, e.g., name.modes
• The atomic signatures (identities) that define the non-evidence predicates ('-ne' option), that is the predicates for which training data contains supervision.

### Supervision completion using the lomrf supervision command-line tool

To demonstrate the usage of LoMRF from command-line interface for supervision completion, assume that we have one knowledge base file, named as schema.mln containing predicate and function schema, and a sequence of training files, named as training1.db, training2.db etc, containing evidence and the partial supervision.

In our example, lets assume a knowledge-base having the following predicates:

Predicate Name Number of arguments Predicate identity Description
NonEvidence_A 2 NonEvidence_A/2 first non-evidence predicate
NonEvidence_B 2 NonEvidence_B/2 second non-evidence predicate
Ev_A 1 EV_A/1 first evidence predicate
Ev_B 1 EV_B/1 second evidence predicate

As it is presented in the above table, there are two non-evidence predicates, NonEvidence_A and NonEvidence_B, where each one takes two terms as arguments. Therefore their atomic signatures are NonEvidence_A/2 and NonEvidence_B/2. Similarly, there are two evidence predicates Ev_A and Ev_B that they take one term as argument. Therefore, the atomic signatures of Ev_A and Ev_B are Ev_A/1 and Ev_B/1, respectively.

#### supervision completion

lomrf supervision -i schema.mln -t /path/to/training/batches/ -ne NonEvidence_A/2,NonEvidence_B/2 -m schema.modes


The resulting completed micro-batches are stored in a folder having the name of the strategy used (by default will be kNN.2.something)

## Command-line Interface Options

By executing the lomrf supervision -h (or lomrf supervision --help) command from the command-line interface, we get a print of multiple parameters. Below we explain all LoMRF supervision completion command-line interface parameters:

### Basic supervision completion options

• -i, --input <kb file> [Required] Specify the input knowledge base file, that is the file that contains the predicate schema and optionally function schema (see Syntax and Quick Start for further information). You can specify either full or relative path or only the filename (when the file is in the current working path). For example, (1) full path -i /full/path/to/theory.mln in a Unix-based OS or -i c:\full\path\to\theory.mln in Windows, (2) relative path -i path/to/theory.mln in a Unix-based OS or -i path\to\theory.mln in Windows and (3) current working path -i theory.mln.

• -t, --training <training file | directory> [Required] Specify the input directory of the training micro-batches (see Syntax and Quick Start for further information). Similarly with the -i option, you can specify either full or relative path or only the filename (when the file is in the current working path).

• -a, --annotation <annotation file | directory> [Optional] Specify the input directory of annotation files for calculating statistics on the performance of the online supervision completion. Similarly with the -i option, you can specify either full or relative path or only the filename (when the file is in the current working path).

• -r, --result <result file> [Optional] Specify the output file name to write statistics about supervision completion. For example, -r output.result. Similarly with the -i option, you can specify either full or relative path or only the filename (when the file is in the current working path).

• -ne, --non-evidence atoms <string> [Required] Specify the atomic signatures or identities (i.e., predicate_name/arity) of predicates for which supervision should be completed. For example, the non-evidence predicate Male(person) has name Male and arity 1 (single argument), therefore we should give the argument -ne Male/1. Multiple non-evidence atoms are allowed in LoMRF and they are defined as comma-separated identities without white-spaces. For example, -ne Male/1,Female/1.

• -m, --modes <mode file> [Required] Specify the input mode declaration file, that is the file that contains the predicate and functions modes (see OSL Examples for further information). Similarly with the -i option, you can specify either full or relative path or only the filename (when the file is in the current working path).

• -c --connector <kNN | eNN> [Optional] "Specify a connection heuristic for the graph (default is kNN).

• -e --epsilon <value> [Optional] Epsilon parameter for eNN connector (default is 0.75).

• -k --kappa <value> [Optional] Kappa parameter for the kNN connector (default is 2).

• -cache --cache-labels [Optional] Cache labels for online supervision completion. We strongly recommend to enable