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propan authored and propan committed Sep 14, 2022
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2 changes: 1 addition & 1 deletion README.md
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Expand Up @@ -18,7 +18,7 @@ RDFRules is divided into five main modules. They are:

### Quick and easy run of RDFRules

1. Download the latest [release](https://github.com/propi/rdfrules/releases) in the .zip format (currently v1.6.0) and unpack it into a folder.
1. Download the latest [release](https://github.com/propi/rdfrules/releases) in the .zip format (currently v1.6.1) and unpack it into a folder.
2. Go to the unpacked RDFRules home folder (with /bin, /webapp and /lib folders) and run RDFRules HTTP API
- On Linux: ```sh bin/main```
- On Windows: ```.\bin\main.bat```
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2 changes: 1 addition & 1 deletion build.sbt
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Expand Up @@ -2,7 +2,7 @@ name := "rdfrules"

val basicSettings = Seq(
organization := "com.github.propi.rdfrules",
version := "1.6.0",
version := "1.6.1",
scalaVersion := "2.13.8",
scalacOptions := Seq("-unchecked", "-deprecation", "-feature", "-encoding", "utf8")
)
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2 changes: 1 addition & 1 deletion core/build.sbt
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Expand Up @@ -2,7 +2,7 @@ name := "core"

organization := "com.github.propi.rdfrules"

version := "1.6.0"
version := "1.6.1"

scalaVersion := "2.13.8"

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53 changes: 26 additions & 27 deletions experiments/README.md
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@@ -1,52 +1,51 @@
# RDFRules: Experiments

This module has implemented some experiments with the RDFRules Scala Core API and Java API. There are 3 basic examples (as 3 main classes) and one main class for complex benchmark and comparison with the original AMIE+ implementation. The complex benchmark was launched on the CESNET Metacentrum computing cluster - results are also available within this GitHub repository.
This module has implemented some experiments with the RDFRules Scala Core API. There are 3 basic examples (as 3 main classes) and one main class for complex benchmark and comparison with the original AMIE+ implementation. The complex benchmark was launched on the CESNET Metacentrum computing cluster - results are also available within this GitHub repository.

## Getting Started

Clone the RDFRules repository and run following SBT commands:
```sbt
```
> project experiments
> run
```

After execution of the *run* command we can choose from three basic (fast) examples and one complex benchmark:
- **YagoAndDbpediaSamples**: 5 samples with DBpedia and YAGO datasets. It operates with the Scala API.
- **CompleteWorkflowScala**: one example with the complete workflow of rule mining. It operates with the Scala API.
- **~~CompleteWorkflowJava~~**: DEPRECATED! the same example as in the CompleteWorkflowScala experiments, but it operates with the Java API.
- **OriginalAmieComparison**: complex bechmark with comparison to original AMIE+.
- **AllTests**: run all experiments with default settings and with `-input experiments/data/yago2core_facts.clean.notypes.tsv.bz2`.

## Benchmark

The main class *OriginalAmieComparison* requires some parameters to determine which kind of experiment should be launched:

```sbt
```
> runMain com.github.propi.rdfrules.experiments.OriginalAmieComparison parameters...
```

| *Parameter name* | *Description* | *Default* |
|------------------|-----------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------|
| coresc | List of numbers of threads to be used | Nil |
| cores | If *coresc* is not specified, it performs experiments for number of threads from 1 to the *cores* value. | available cores |
| minhcs | List of values with min head coverage thresholds. | 0.005,0.01,0.02,0.05,0.1,0.2,0.3 |
| times | Number of repetition of each task | 7 |
| input | Input TSV dataset. Dataset can be compressed by GZIP or BZIP2. | experiments/data/yago2core_facts.clean.notypes.tsv.bz2 |
| output | Output file with benchmark results. | experiments/data/results.txt |
| runtopk | Run rule mining in the top-k mode. It contains *runconstants* and *runlogical* tasks with topK = 100. | |
| runconstants | Run rule mining with constants. Default confidence thresholds are: minConfidence = 0.1, minPcaConfidence = 0.1, numberOfThreads = $cores | |
| runlogical | Run rule mining only with variables. Default confidence thresholds are: minConfidence = 0.1, minPcaConfidence = 0.1, numberOfThreads = $cores | |
| runcores | Run the experiment of scalability. Default thresholds are: minHeadCoverage = ${minhcs.head}, minConfidence = 0.1, minPcaConfidence = 0.1 | |
| runpatterns | Run rule mining with patterns | |
| runconfidence | Run confidence computing separately. The default threshold is minPcaConfidence=0.1, input 10000 rules with/without constants. | |
| rungraphs | Run mining of rules from two different graphs and from merged graphs together by owl:sameAs links. YAGO and DBpedia samples are used. | |
| rundiscretization | Run a heuristic for automatic discretization of numerical values in order to discover more interesting rules. There should by used an input dataset with numerical literals, e.g., experiments/data/mappingbased_literals_sample.ttl.bz2 | |
| runclusters | Run DBScan clustering for mined rules by different settings with calculated inter/intra cluster similarities and final score. MinNeigbours is set to 1 and Eps (min similarity) is set by the *minsims* option. Rules are clusteted based on their body and head contents. | |
| minsims | Minimum similarities to make a cluster by the clustering experiment. | 0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8 |
| runpruning | Run CBA pruning strategy for reducing number of rules and compare results with the original ruleset. The original ruleset is mined by topK approach with different values set by the *topks* option and with MinHC=0.01 and MinConf=0.1 | |
| topks | TopK values for the pruning experiment. | 500,1000,2000,4000,8000,16000,32000 |
| rdfrulesonly | Run only RDFRules | |
| amieonly | Run only AMIE+ | |
| *Parameter name* | *Description* | *Default* |
|-------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------|
| coresc | List of numbers of threads to be used | Nil |
| cores | If *coresc* is not specified, it performs experiments for number of threads from 1 to the *cores* value. | available cores |
| minhcs | List of values with min head coverage thresholds. | 0.005,0.01,0.02,0.05,0.1,0.2,0.3 |
| times | Number of repetition of each task | 7 |
| input | Input TSV dataset. Dataset can be compressed by GZIP or BZIP2. | experiments/data/yago2core_facts.clean.notypes.tsv.bz2 |
| output | Output file with benchmark results. | experiments/data/results.txt |
| runtopk | Run rule mining in the top-k mode. It contains *runconstants* and *runlogical* tasks with topK = 100. | |
| runconstants | Run rule mining with constants. Default confidence thresholds are: minConfidence = 0.1, minPcaConfidence = 0.1, numberOfThreads = $cores | |
| runlogical | Run rule mining only with variables. Default confidence thresholds are: minConfidence = 0.1, minPcaConfidence = 0.1, numberOfThreads = $cores | |
| runcores | Run the experiment of scalability. Default thresholds are: minHeadCoverage = ${minhcs.head}, minConfidence = 0.1, minPcaConfidence = 0.1 | |
| runpatterns | Run rule mining with patterns | |
| runconfidence | Run confidence computing separately. The default threshold is minPcaConfidence=0.1, input 10000 rules with/without constants. | |
| rungraphs | Run mining of rules from two different graphs and from merged graphs together by owl:sameAs links. YAGO and DBpedia samples are used. | |
| rundiscretization | Run a heuristic for automatic discretization of numerical values in order to discover more interesting rules. There should by used an input dataset with numerical literals, e.g., experiments/data/mappingbased_literals_sample.ttl.bz2 | |
| runclusters | Run DBScan clustering for mined rules by different settings with calculated inter/intra cluster similarities and final score. MinNeigbours is set to 1 and Eps (min similarity) is set by the *minsims* option. Rules are clusteted based on their body and head contents. | |
| minsims | Minimum similarities to make a cluster by the clustering experiment. | 0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8 |
| runpruning | Run CBA pruning strategy for reducing number of rules and compare results with the original ruleset. The original ruleset is mined by topK approach with different values set by the *topks* option and with MinHC=0.01 and MinConf=0.1 | |
| topks | TopK values for the pruning experiment. | 500,1000,2000,4000,8000,16000,32000 |
| rdfrulesonly | Run only RDFRules | |
| amieonly | Run only AMIE+ | |

### Performed experiments

Expand Down Expand Up @@ -93,6 +92,6 @@ The results of individual tasks are placed in the [results](./results) folder. T

All experiments can be launched with default settings with the *yago2core_facts.clean.notypes.tsv.bz2* dataset and with the *mappingbased_literals_sample.ttl.bz2* dataset for discretization by the main class *AllTests*:

```sbt
```
> runMain com.github.propi.rdfrules.experiments.AllTests
```
2 changes: 1 addition & 1 deletion experiments/build.sbt
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Expand Up @@ -2,7 +2,7 @@ name := "experiments"

organization := "com.github.propi.rdfrules"

version := "1.0.0"
version := "1.6.1"

scalaVersion := "2.13.8"

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2 changes: 1 addition & 1 deletion gui/build.sbt
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Expand Up @@ -2,7 +2,7 @@ name := "gui"

organization := "com.github.propi.rdfrules"

version := "1.6.0"
version := "1.6.1"

scalaVersion := "2.13.8"

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2 changes: 1 addition & 1 deletion gui/webapp/index.html
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Expand Up @@ -8,7 +8,7 @@
<body>
<header>
<h1>RDFRules</h1>
<div class="version">v1.6.0</div>
<div class="version">v1.6.1</div>
</header>
<main id="rdfrules"></main>
<script>
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2 changes: 1 addition & 1 deletion http/build.sbt
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Expand Up @@ -2,7 +2,7 @@ name := "http"

organization := "com.github.propi.rdfrules"

version := "1.6.0"
version := "1.6.1"

scalaVersion := "2.13.8"

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