Metadata-Version: 2.1
Name: kglab
Version: 0.2.1
Summary: A simple abstraction layer in Python for building knowledge graphs
Home-page: https://derwen.ai/docs/kgl/
Author: Paco Nathan
Author-email: paco@derwen.ai
License: MIT
Project-URL: Source Code, https://github.com/DerwenAI/kglab
Project-URL: Issue Tracker, https://github.com/DerwenAI/kglab/issues
Project-URL: Community Survey, https://forms.gle/FMHgtmxHYWocprMn6
Project-URL: Discussion Forum, https://www.linkedin.com/groups/6725785/
Project-URL: Hands-on Tutorial, https://derwen.ai/docs/kgl/tutorial/
Description: # kglab
        
        [![DOI](https://zenodo.org/badge/307214458.svg)](https://zenodo.org/badge/latestdoi/307214458)
        ![GitHub commit activity](https://img.shields.io/github/commit-activity/w/DerwenAI/kglab?style=plastic)
        [![Checked with mypy](http://www.mypy-lang.org/static/mypy_badge.svg)](http://mypy-lang.org/)
        
        The **kglab** library provides a simple abstraction layer in Python
        for building knowledge graphs.
        
        Welcome to *graph-based data science*:
        <https://derwen.ai/docs/kgl/>
        
        > **SPECIAL REQUEST**:   
        > Which features would you like in an open source Python library for building knowledge graphs?  
        > Please add your suggestions through this survey:  
        > https://forms.gle/FMHgtmxHYWocprMn6  
        > This will help us prioritize the **kglab** roadmap.
        
        
        ## Getting Started
        
        See the ["Getting Started"](https://derwen.ai/docs/kgl/start/)
        section of the online documentation.
        
        To install from [PyPi](https://pypi.python.org/pypi/kglab):
        ```
        pip install kglab
        ```
        
        If you work directly from this Git repo, be sure to install the 
        dependencies as well:
        ```
        pip install -r requirements.txt
        ```
        
        Then to use the library with a simple use case:
        ```
        import kglab
        
        # create a KnowledgeGraph object
        kg = kglab.KnowledgeGraph()
        
        # load RDF from a URL
        kg.load_rdf("http://bigasterisk.com/foaf.rdf", format="xml")
        
        # measure the graph
        measure = kglab.Measure()
        measure.measure_graph(kg)
        
        print("edges: {}\n".format(measure.get_edge_count()))
        print("nodes: {}\n".format(measure.get_node_count()))
        
        # serialize as a string in "Turtle" TTL format
        ttl = kg.save_rdf_text()
        print("```")
        print(ttl[:999])
        print("```")
        ```
        
        See the **tutorial notebooks** in the `examples` subdirectory for
        sample code and patterns to use in integrating **kglab** with other
        graph libraries in Python:
        <https://derwen.ai/docs/kgl/tutorial/>
        
        
        ## Semantic Versioning
        
        Before **kglab** reaches release `v1.0.0` the types and classes may
        undergo substantial changes and the project is not guaranteed to have
        a consistent API.
        Even so, we will try to minimize breaking changes and provide careful
        notes in the `changelog.txt` file.
        
        
        ## Contributing Code
        
        We welcome people getting involved as contributors to this open source
        project!
        Please see the
        [CONTRIBUTING.md](https://github.com/DerwenAI/kglab/blob/main/CONTRIBUTING.md)
        file for instructions.
        
        
        ## Build Instructions
        
        **Note: unless you are contributing code and updates,
        in most use cases won't need to build this package locally.**
        
        Instead, simply install from
        [PyPi](https://pypi.python.org/pypi/kglab)
        or [Conda](https://docs.conda.io/).
        
        To set up the build environment locally, see the 
        ["Build Instructions"](https://derwen.ai/docs/kgl/build/)
        section of the online documentation.
        
        ![illustration of a knowledge graph, plus laboratory glassware](https://raw.githubusercontent.com/DerwenAI/kglab/main/docs/assets/logo.png)
        
        
        ## License and Copyright
        
        Source code for **kglab** plus its logo, documentation, and examples
        have an [MIT license](https://spdx.org/licenses/MIT.html) which is
        succinct and simplifies use in commercial applications.
        
        All materials herein are Copyright &copy; 2020-2021 Derwen, Inc.
        
        
        ## Attribution
        
        Please use the following BibTeX entry for citing **kglab** if you use
        it in your research or software.
        Citations are helpful for the continued development and maintenance of
        this library.
        
        ```
        @software{kglab,
          author = {Paco Nathan},
          title = {{kglab: a simple abstraction layer in Python for building knowledge graphs}},
          year = 2020,
          publisher = {Derwen},
          doi = {10.5281/zenodo.4516509},
          url = {https://github.com/DerwenAI/kglab}
        }
        ```
        
        
        ## Kudos
        
        Many thanks to our contributors:
        [@ceteri](https://github.com/ceteri),
        [@gauravjaglan](https://github.com/gauravjaglan),
        [@louisguitton](https://github.com/louisguitton),
        [@jake-aft](https://github.com/jake-aft),
        [@dmoore247](https://github.com/dmoore247),
        plus general support from [Derwen, Inc.](https://github.com/DerwenAI),
        [KFocus](https://kfocus.org/),
        the [NVidia RAPIDS team](https://rapids.ai/),
        [Gradient Flow](https://gradientflow.com/),
        the [KGC Community](https://github.com/KGConf),
        [Connected Data London](https://connected-data.london/),
        and
        [Manning Publications](https://www.manning.com/).
        
Keywords: knowledge graph,graph algorithms,interactive visualization,validation,inference,rdf,owl,skos,sparql,shacl,controlled vocabulary,managing namespaces,serialization,n3,turtle,json-ld,parquet,psl,probabilistic soft logic,pandas,networkx,igraph
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Information Technology
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Human Machine Interfaces
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: Topic :: Text Processing :: Indexing
Requires-Python: >=3.6
Description-Content-Type: text/markdown
Provides-Extra: base
Provides-Extra: docs
