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Discovering Context-Aware Constraints in Multimodal Knowledge Graphs

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Multimodal Context-Aware Knowledge Graph Constraints

Mine multimodal constraints from (RDF) knowledge graphs

Note that this work is proof-of-concept and experimental, and has not been fully optimized.

Description

A constraint p: head <- body states that all entities e that satisfy the body must also satisfy the head, with probability p.

Here, the head consists of a single assertion P(e, v), which states that entity e should have element v for property P. The body can take any number of assertions and represents the domain (subgraph) to which the head applies. Constraint element v can be any entity, an attribute value, a type restriction, or a multimodal cluster.

Usage:

usage: run.py    [-h] -d DEPTH -s MIN_SUPPORT -c MIN_CONFIDENCE
                 [-o {tsv,pkl}] -i INPUT [INPUT ...] [--max_size MAX_SIZE]
                 [--max_width MAX_WIDTH] [--mode {AA,AT,TA,TT,AB,BA,TB,BT,BB}]
                 [--multimodal] [--p_explore P_EXPLORE] [--p_extend P_EXTEND]
                 [--noprune] [--valopt] [--test]

usage: run_mp.py [-h] [-n NPROC] -d DEPTH -s MIN_SUPPORT -c MIN_CONFIDENCE
                 [-o {tsv,pkl}] -i INPUT [INPUT ...] [--max_size MAX_SIZE]
                 [--max_width MAX_WIDTH] [--mode {AA,AT,TA,TT,AB,BA,TB,BT,BB}]
                 [--multimodal] [--p_explore P_EXPLORE] [--p_extend P_EXTEND]
                 [--noprune] [--valopt] [--test]

required arguments:
  -d DEPTH, --depth DEPTH
                        Depths to explore
  -s MIN_SUPPORT, --min_support MIN_SUPPORT
                        Minimal clause support
  -c MIN_CONFIDENCE, --min_confidence MIN_CONFIDENCE
                        Minimal clause confidence
  -i INPUT [INPUT ...], --input INPUT [INPUT ...]
                        One or more RDF-encoded graphs

optional arguments:
  -h, --help            show this help message and exit
  -n NPROC, --nproc NPROC
                        Number of cores to utilize
  -o {tsv,pkl}, --output {tsv,pkl}
                        Preferred output format
  --max_size MAX_SIZE   Maximum context size
  --max_width MAX_WIDTH
                        Maximum width of shell
  --mode {AA,AT,TA,TT,AB,BA,TB,BT,BB}
                        A[box], T[box], or B[oth] as candidates for head and
                        body
  --multimodal          Enable multimodal support
  --p_explore P_EXPLORE
                        Probability of exploring candidate endpoint
  --p_extend P_EXTEND   Probability of extending at endpoint
  --noprune             Do not prune the output set
  --valopt              Prepare output for validation (only relevant to pkl)
  --test                Dry run without saving results

Validation

To validate, use https://gitlab.com/wxwilcke/mkgfdv with generated generation forest (pkl) as input

Cite

While we await our paper to be accepted, please cite us as follows if you use this code in your own research.

@article{wilcke2020constraints,
  title={Bottom-up Discovery of Context-aware Quality Constraints for Heterogeneous Knowledge Graphs},
  author={Wilcke, WX and de Kleijn, MTM and de Boer, V and Scholten, HJ},
  year={2020}
}

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