Accessing data in structured formats such as XML, CSV and JSON in statically typed languages is difficult, because the languages do not understand the structure of the data. Dynamically typed languages make this syntactically easier, but lead to error-prone code. Despite numerous efforts, most of the data available on the web do not come with a schema. The only information available to developers is a set of examples, such as typical server responses. We describe an inference algorithm that infers a type of structured formats including CSV, XML and JSON. The algorithm is based on finding a common supertype of types representing individual samples (or values in collections). We use the algorithm as a basis for an F# type provider that integrates the inference into the F# type system. As a result, users can access CSV, XML and JSON data in a statically-typed fashion just by specifying a representative sample document.

Real estate has already recorded target data......

Public information: refers to various financial statistical reports, policy news, information services, laws and other related data sets

To download XML and JSON files, click the CSV option below and click the down arrow next to the Download button in the upper right on its page.

Paid dataset with city names, coordinates, regions, and administrative divisions of World Cities. Available in Excel (.xlsx), CSV, JSON, XML, and SQL formats after purchase.

Paid dataset with city names, coordinates, regions, and administrative divisions of Ecuador. Available in Excel (.xlsx), CSV, JSON, XML, and SQL formats after purchase.

Real estate has been auctioned off................

도서명, 저자명, 출판사명, 형태사항, 주기사항,원문정보등

Real estate has been auctioned off................

To download XML and JSON files, click the CSV option below and click the down arrow next to the Download button in the upper right on its page.

The tdt (The DataTank) extension for CKAN enhances the accessibility of datasets by automatically integrating them with The DataTank when a dataset is added and is determined to be proxyable. This process makes data available in various formats, like XML, JSON, and CSV, through The DataTank's adapters. By reconfiguring the dataset's URL to use The DataTank, the extension simplifies the consumption of CKAN-managed data in multiple formats. Key Features: Automatic Data Format Conversion: Upon dataset creation and detectability, the system will automatically activate The DataTank adapters to provide XML, JSON, and CSV formats. URL Reconfiguration: The core functionality lies in rewriting the dataset URL to point to The DataTank access point. Integration with The DataTank: Operates as a hook that utilizes The DataTank's proxy capabilities. Technical Integration: The extension requires configuration values to be added to the CKAN .ini file (typically /etc/ckan/default/development.ini). These configuration settings are essential for the extension to correctly identify and interact with datasets to be proxied through The DataTank. Benefits & Impact: The primary benefit of the tdt extension is the streamlined data access for end-users. By transparently integrating with The DataTank, this extension simplifies the process of consuming data managed by CKAN in multiple, commonly used formats, without requiring manual conversion processes.

Real estate bidding data (CSV)....................

This repository contains the Europe PMC full text corpus, a collection of 300 articles from the Europe PMC Open Access subset. Each article contains 3 core entity types, manually annotated by curators: Gene/Protein, Disease and Organism.

Corpus Directory Structure

annotations/: contains annotations of the 300 full-text articles in the Europe PMC corpus. Annotations are provided in 3 different formats.

hypothesis/csv/: contains raw annotations fetched from the annotation platform Hypothes.is in comma-separated values (CSV) format.
GROUP0/: contains raw manual annotations made by curator GROUP0. GROUP1/: contains raw manual annotations made by curator GROUP1. GROUP2/: contains raw manual annotations made by curator GROUP2.

IOB/: contains automatically extracted annotations using raw manual annotations in hypothesis/csv/, which is in Inside–Outside–Beginning tagging format.
dev/: contains IOB format annotations of 45 articles, suppose to be used a dev set in machine learning task. test/: contains IOB format annotations of 45 articles, suppose to be used a test set in machine learning task. train/: contains IOB format annotations of 210 articles, suppose to be used a training set in machine learning task.

JSON/: contains automatically extracted annotations using raw manual annotations in hypothesis/csv/, which is in JSON format. README.md: a detailed description of all the annotation formats.

articles/: contains the full-text articles annotated in Europe PMC corpus.

Sentencised/: contains XML articles whose text has been split into sentences using the Europe PMC sentenciser. XML/: contains XML articles directly fetched using Europe PMC Article Restful API. README.md: a detailed description of the sentencising and fetching of XML articles.

docs/: contains related documents that were used for generating the corpus.

Annotation guideline.pdf: annotation guideline that is provided to curators to assist the manual annotation. demo to molecular conenctions.pdf: annotation platform guideline that is provided to curator to help them get familiar with the Hypothes.is platform. Training set development.pdf: initial document that details the paper selection procedures.

pilot/: contains annotations and articles that were used in a pilot study.

annotations/csv/: contains raw annotations fetched from the annotation platform Hypothes.is in comma-separated values (CSV) format. articles/: contains the full-text articles annotated in the pilot study.

 Sentencised/: contains XML articles whose text has been split into sentences using the Europe PMC sentenciser.
 XML/: contains XML articles directly fetched using Europe PMC Article Restful API.

README.md: a detailed description of the sentencising and fetching of XML articles.

src/: source codes for cleaning annotations and generating IOB files

metrics/ner_metrics.py: Python script contains SemEval evaluation metrics. annotations.py: Python script used to extract annotations from raw Hypothes.is annotations. generate_IOB_dataset.py: Python script used to convert JSON format annotations to IOB tagging format. generate_json_dataset.py: Python script used to extract annotations to JSON format. hypothesis.py: Python script used to fetch raw Hypothes.is annotations.

License

CCBY

Feedback

For any comment, question, and suggestion, please contact us through helpdesk@europepmc.org or Europe PMC contact page.

Knowledge Graph Construction Workshop 2023: challenge

Knowledge graph construction of heterogeneous data has seen a lot of uptake
in the last decade from compliance to performance optimizations with respect
to execution time. Besides execution time as a metric for comparing knowledge
graph construction, other metrics e.g. CPU or memory usage are not considered.
This challenge aims at benchmarking systems to find which RDF graph
construction system optimizes for metrics e.g. execution time, CPU,
memory usage, or a combination of these metrics.

Task description

The task is to reduce and report the execution time and computing resources
(CPU and memory usage) for the parameters listed in this challenge, compared
to the state-of-the-art of the existing tools and the baseline results provided
by this challenge. This challenge is not limited to execution times to create
the fastest pipeline, but also computing resources to achieve the most efficient
pipeline.

We provide a tool which can execute such pipelines end-to-end. This tool also
collects and aggregates the metrics such as execution time, CPU and memory
usage, necessary for this challenge as CSV files. Moreover, the information
about the hardware used during the execution of the pipeline is available as
well to allow fairly comparing different pipelines. Your pipeline should consist
of Docker images which can be executed on Linux to run the tool. The tool is
already tested with existing systems, relational databases e.g. MySQL and
PostgreSQL, and triplestores e.g. Apache Jena Fuseki and OpenLink Virtuoso
which can be combined in any configuration. It is strongly encouraged to use
this tool for participating in this challenge. If you prefer to use a different
tool or our tool imposes technical requirements you cannot solve, please contact
us directly.

Part 1: Knowledge Graph Construction Parameters

These parameters are evaluated using synthetic generated data to have more
insights of their influence on the pipeline.

Data

• Number of data records: scaling the data size vertically by the number of records with a fixed number of data properties (10K, 100K, 1M, 10M records).
• Number of data properties: scaling the data size horizontally by the number of data properties with a fixed number of data records (1, 10, 20, 30 columns).
• Number of duplicate values: scaling the number of duplicate values in the dataset (0%, 25%, 50%, 75%, 100%).
• Number of empty values: scaling the number of empty values in the dataset (0%, 25%, 50%, 75%, 100%).
• Number of input files: scaling the number of datasets (1, 5, 10, 15).

Mappings

• Number of subjects: scaling the number of subjects with a fixed number of predicates and objects (1, 10, 20, 30 TMs).
• Number of predicates and objects: scaling the number of predicates and objects with a fixed number of subjects (1, 10, 20, 30 POMs).
• Number of and type of joins: scaling the number of joins and type of joins (1-1, N-1, 1-N, N-M)

Part 2: GTFS-Madrid-Bench

The GTFS-Madrid-Bench provides insights in the pipeline with real data from the
public transport domain in Madrid.

Scaling

• GTFS-1 SQL
• GTFS-10 SQL
• GTFS-100 SQL
• GTFS-1000 SQL

Heterogeneity

• GTFS-100 XML + JSON
• GTFS-100 CSV + XML
• GTFS-100 CSV + JSON
• GTFS-100 SQL + XML + JSON + CSV

Example pipeline

The ground truth dataset and baseline results are generated in different steps
for each parameter:

1. The provided CSV files and SQL schema are loaded into a MySQL relational database.
2. Mappings are executed by accessing the MySQL relational database to construct a knowledge graph in N-Triples as RDF format.
3. The constructed knowledge graph is loaded into a Virtuoso triplestore, tuned according to the Virtuoso documentation.
4. The provided SPARQL queries are executed on the SPARQL endpoint exposed by Virtuoso.

The pipeline is executed 5 times from which the median execution time of each
step is calculated and reported. Each step with the median execution time is
then reported in the baseline results with all its measured metrics.
Query timeout is set to 1 hour and knowledge graph construction timeout
to 24 hours. The execution is performed with the following tool: https://github.com/kg-construct/challenge-tool,
you can adapt the execution plans for this example pipeline to your own needs.

Each parameter has its own directory in the ground truth dataset with the
following files:

• Input dataset as CSV.
• Mapping file as RML.
• Queries as SPARQL.
• Execution plan for the pipeline in metadata.json.

Datasets

Knowledge Graph Construction Parameters

The dataset consists of:

• Input dataset as CSV for each parameter.
• Mapping file as RML for each parameter.
• SPARQL queries to retrieve the results for each parameter.
• Baseline results for each parameter with the example pipeline.
• Ground truth dataset for each parameter generated with the example pipeline.

Format

All input datasets are provided as CSV, depending on the parameter that is being
evaluated, the number of rows and columns may differ. The first row is always
the header of the CSV.

GTFS-Madrid-Bench

The dataset consists of:

• Input dataset as CSV with SQL schema for the scaling and a combination of XML,
• CSV, and JSON is provided for the heterogeneity.
• Mapping file as RML for both scaling and heterogeneity.
• SPARQL queries to retrieve the results.
• Baseline results with the example pipeline.
• Ground truth dataset generated with the example pipeline.

Format

CSV datasets always have a header as their first row.
JSON and XML datasets have their own schema.

Evaluation criteria

Submissions must evaluate the following metrics:

• Execution time of all the steps in the pipeline. The execution time of a step is the difference between the begin and end time of a step.
• CPU time as the time spent in the CPU for all steps of the pipeline. The CPU time of a step is the difference between the begin and end CPU time of a step.
• Minimal and maximal memory consumption for each step of the pipeline. The minimal and maximal memory consumption of a step is the minimum and maximum calculated of the memory consumption during the execution of a step.

Expected output

Duplicate values

Empty values

Mappings

Properties

Records

Joins

1-1 joins

1-N joins

To download XML and JSON files, click the CSV option below and click the down arrow next to the Download button in the upper right on its page.

Public information: refers to various financial statistical reports, policy news, information services, laws and other related data sets

Paid dataset with city names, coordinates, regions, and administrative divisions of United States Minor Outlying Islands. Available in Excel (.xlsx), CSV, JSON, XML, and SQL formats after purchase.

Knowledge Graph Construction Workshop 2024: challenge

Knowledge graph construction of heterogeneous data has seen a lot of uptakein the last decade from compliance to performance optimizations with respectto execution time. Besides execution time as a metric for comparing knowledgegraph construction, other metrics e.g. CPU or memory usage are not considered.This challenge aims at benchmarking systems to find which RDF graphconstruction system optimizes for metrics e.g. execution time, CPU,memory usage, or a combination of these metrics.

Task description

The task is to reduce and report the execution time and computing resources(CPU and memory usage) for the parameters listed in this challenge, comparedto the state-of-the-art of the existing tools and the baseline results providedby this challenge. This challenge is not limited to execution times to createthe fastest pipeline, but also computing resources to achieve the most efficientpipeline.

We provide a tool which can execute such pipelines end-to-end. This tool alsocollects and aggregates the metrics such as execution time, CPU and memoryusage, necessary for this challenge as CSV files. Moreover, the informationabout the hardware used during the execution of the pipeline is available aswell to allow fairly comparing different pipelines. Your pipeline should consistof Docker images which can be executed on Linux to run the tool. The tool isalready tested with existing systems, relational databases e.g. MySQL andPostgreSQL, and triplestores e.g. Apache Jena Fuseki and OpenLink Virtuosowhich can be combined in any configuration. It is strongly encouraged to usethis tool for participating in this challenge. If you prefer to use a differenttool or our tool imposes technical requirements you cannot solve, please contactus directly.

Track 1: Conformance

The set of new specification for the RDF Mapping Language (RML) established by the W3C Community Group on Knowledge Graph Construction provide a set of test-cases for each module:

RML-Core

RML-IO

RML-CC

RML-FNML

RML-Star

These test-cases are evaluated in this Track of the Challenge to determine their feasibility, correctness, etc. by applying them in implementations. This Track is in Beta status because these new specifications have not seen any implementation yet, thus it may contain bugs and issues. If you find problems with the mappings, output, etc. please report them to the corresponding repository of each module.

Note: validating the output of the RML Star module automatically through the provided tooling is currently not possible, see https://github.com/kg-construct/challenge-tool/issues/1.

Through this Track we aim to spark development of implementations for the new specifications and improve the test-cases. Let us know your problems with the test-cases and we will try to find a solution.

Track 2: Performance

Part 1: Knowledge Graph Construction Parameters

These parameters are evaluated using synthetic generated data to have moreinsights of their influence on the pipeline.

Data

Number of data records: scaling the data size vertically by the number of records with a fixed number of data properties (10K, 100K, 1M, 10M records).

Number of data properties: scaling the data size horizontally by the number of data properties with a fixed number of data records (1, 10, 20, 30 columns).

Number of duplicate values: scaling the number of duplicate values in the dataset (0%, 25%, 50%, 75%, 100%).

Number of empty values: scaling the number of empty values in the dataset (0%, 25%, 50%, 75%, 100%).

Number of input files: scaling the number of datasets (1, 5, 10, 15).

Mappings

Number of subjects: scaling the number of subjects with a fixed number of predicates and objects (1, 10, 20, 30 TMs).

Number of predicates and objects: scaling the number of predicates and objects with a fixed number of subjects (1, 10, 20, 30 POMs).

Number of and type of joins: scaling the number of joins and type of joins (1-1, N-1, 1-N, N-M)

Part 2: GTFS-Madrid-Bench

The GTFS-Madrid-Bench provides insights in the pipeline with real data from thepublic transport domain in Madrid.

Scaling

GTFS-1 SQL

GTFS-10 SQL

GTFS-100 SQL

GTFS-1000 SQL

Heterogeneity

GTFS-100 XML + JSON

GTFS-100 CSV + XML

GTFS-100 CSV + JSON

GTFS-100 SQL + XML + JSON + CSV

Example pipeline

The ground truth dataset and baseline results are generated in different stepsfor each parameter:

The provided CSV files and SQL schema are loaded into a MySQL relational database.

Mappings are executed by accessing the MySQL relational database to construct a knowledge graph in N-Triples as RDF format

The pipeline is executed 5 times from which the median execution time of eachstep is calculated and reported. Each step with the median execution time isthen reported in the baseline results with all its measured metrics.Knowledge graph construction timeout is set to 24 hours. The execution is performed with the following tool: https://github.com/kg-construct/challenge-tool,you can adapt the execution plans for this example pipeline to your own needs.

Each parameter has its own directory in the ground truth dataset with thefollowing files:

Input dataset as CSV.

Mapping file as RML.

Execution plan for the pipeline in metadata.json.

Datasets

Knowledge Graph Construction Parameters

The dataset consists of:

Input dataset as CSV for each parameter.

Mapping file as RML for each parameter.

Baseline results for each parameter with the example pipeline.

Ground truth dataset for each parameter generated with the example pipeline.

Format

All input datasets are provided as CSV, depending on the parameter that is beingevaluated, the number of rows and columns may differ. The first row is alwaysthe header of the CSV.

GTFS-Madrid-Bench

The dataset consists of:

Input dataset as CSV with SQL schema for the scaling and a combination of XML,

CSV, and JSON is provided for the heterogeneity.

Mapping file as RML for both scaling and heterogeneity.

SPARQL queries to retrieve the results.

Baseline results with the example pipeline.

Ground truth dataset generated with the example pipeline.

Format

CSV datasets always have a header as their first row.JSON and XML datasets have their own schema.

Evaluation criteria

Submissions must evaluate the following metrics:

Execution time of all the steps in the pipeline. The execution time of a step is the difference between the begin and end time of a step.

CPU time as the time spent in the CPU for all steps of the pipeline. The CPU time of a step is the difference between the begin and end CPU time of a step.

Minimal and maximal memory consumption for each step of the pipeline. The minimal and maximal memory consumption of a step is the minimum and maximum calculated of the memory consumption during the execution of a step.

Expected output

Duplicate values

Scale Number of Triples

0 percent 2000000 triples

25 percent 1500020 triples

50 percent 1000020 triples

75 percent 500020 triples

100 percent 20 triples

Empty values

Scale Number of Triples

0 percent 2000000 triples

25 percent 1500000 triples

50 percent 1000000 triples

75 percent 500000 triples

100 percent 0 triples

Mappings

Scale Number of Triples

1TM + 15POM 1500000 triples

3TM + 5POM 1500000 triples

5TM + 3POM 1500000 triples

15TM + 1POM 1500000 triples

Properties

Scale Number of Triples

1M rows 1 column 1000000 triples

1M rows 10 columns 10000000 triples

1M rows 20 columns 20000000 triples

1M rows 30 columns 30000000 triples

Records

Scale Number of Triples

10K rows 20 columns 200000 triples

100K rows 20 columns 2000000 triples

1M rows 20 columns 20000000 triples

10M rows 20 columns 200000000 triples

Joins

1-1 joins

Scale Number of Triples

0 percent 0 triples

25 percent 125000 triples

50 percent 250000 triples

75 percent 375000 triples

100 percent 500000 triples

1-N joins

Scale Number of Triples

1-10 0 percent 0 triples

1-10 25 percent 125000 triples

1-10 50 percent 250000 triples

1-10 75 percent 375000 triples

1-10 100 percent 500000 triples

1-5 50 percent 250000 triples

1-10 50 percent 250000 triples

1-15 50 percent 250005 triples

1-20 50 percent 250000 triples

1-N joins

Scale Number of Triples

10-1 0 percent 0 triples

10-1 25 percent 125000 triples

10-1 50 percent 250000 triples

10-1 75 percent 375000 triples

10-1 100 percent 500000 triples

5-1 50 percent 250000 triples

10-1 50 percent 250000 triples

15-1 50 percent 250005 triples

20-1 50 percent 250000 triples

N-M joins

Scale Number of Triples

5-5 50 percent 1374085 triples

10-5 50 percent 1375185 triples

5-10 50 percent 1375290 triples

5-5 25 percent 718785 triples

5-5 50 percent 1374085 triples

5-5 75 percent 1968100 triples

5-5 100 percent 2500000 triples

5-10 25 percent 719310 triples

5-10 50 percent 1375290 triples

5-10 75 percent 1967660 triples

5-10 100 percent 2500000 triples

10-5 25 percent 719370 triples

10-5 50 percent 1375185 triples

10-5 75 percent 1968235 triples

10-5 100 percent 2500000 triples

GTFS Madrid Bench

Generated Knowledge Graph

Scale Number of Triples

1 395953 triples

10 3959530 triples

100 39595300 triples

1000 395953000 triples

Queries

Query Scale 1 Scale 10 Scale 100 Scale 1000

Q1 58540 results 585400 results No results available No results available

Q2 636 results 11998 results
125565 results 1261368 results

Q3 421 results 4207 results 42067 results 420667 results

Q4 13 results 130 results 1300 results 13000 results

Q5 35 results 350 results 3500 results 35000 results

Q6 1 result 1 result 1 result 1 result

Q7 68 results 67 results 67 results 53 results

Q8 35460 results 354600 results No results available No results available

Q9 130 results 1300

Paid dataset with city names, coordinates, regions, and administrative divisions of Montenegro. Available in Excel (.xlsx), CSV, JSON, XML, and SQL formats after purchase.