SALI LMSS Python Library

A Python library for working with the SALI LMSS OWL

Overview

What is the Legal Matter Standard Specification (LMSS)?

The LMSS is a taxonomy/ontology of over 10,000 law-focused tags/nodes, as well as relationships/edges between those tags/nodes. Each tag/node has a unique identifier (IRI). That identifier is being used — and standardized — by three stakeholder groups:

• Vendors (e.g., Thomson Reuters, LexisNexis, Fastcase, Docket Alarm, NetDocuments, iManage, and many others)
• Providers/Firms (e.g., DLA Piper, Clifford Chance, Perkins Coie, Crowell, Fredrikson & Byron, and many others)
• Corporate Law Departments (e.g., Microsoft, Intel)

The end result: LMSS permits those stakeholders (vendors, firms, corporate law departments) to increase interoperability, fuel AI, and produce better outcomes.

What is Standards Advancement for the Legal Industry (SALI)?

SALI is the nonprofit consortium of Vendors, Providers (e.g., firms), and Clients — all quickly and broadly moving towards standardizing with SALI tags (LMSS). Those 10,000+ tags serve as the industry’s common language, improving outcomes and value. And they are used in conjunction with the SALI API, where all stakeholders can transfer data using a standardized API protocol — leveraging SALI tags/nodes/identifiers. (edited)

Installation

This module is not yet available on PyPI. To install, use the following command:

$ pip3 install https://github.com/273v/python-lmss/archive/refs/heads/main.zip

PyPI and Conda packages will be available as soon as the API is confirmed to be stable after additional testing.

Current Functionality

This library currently supports:

• loading LMSS from a local file or remote URL, e.g., SALI GitHub
• loading a specific version or branch of LMSS
• loading as a raw lxml.etree or rdflib.Graph object
• loading as an lmss.graph.LMSSGraph object, which provides additional functionality
• searching for IRIs by label, including basic fuzzy matching
• searching for IRIs by definition, including basic fuzzy matching
• listing common key concepts (i.e., the ones visible listed in SALI's Protégé instance)

Additional functionality, including connections to public and private data models and ontologies, is available in Kelvin Graph.

Usage

Additional documentation will be provided as the API is finalized and more use cases are solicited.

# Load the LMSS graph
lmss_graph = LMSSGraph()

# Search for a label in the graph by definition
results = lmss_graph.search_definitions("nautical")
print(results[0])

# Output:
{
    'alt_labels': ['Admiralty and Maritime Law'],
    'children': [],
    'definitions': ['Law that governs nautical issues and private maritime disputes.'],
    'distance': 100.0,
    'exact': False,
    'hidden_labels': ['ADMR'],
    'iri': 'http://lmss.sali.org/RCXiUipi6wkkqalyBH20P5A',
    'label': 'Admiralty and Maritime Law',
    'parents': ['http://lmss.sali.org/RqNYDJtQ1pAsGPKZNEUlETN1635a33287ad46c9986274ed71d37997'],
    'substring': True
}

results = lmss_graph.search_definitions("nautical")
print(results[0])

# Search for a label in the graph
for result in lmss_graph.search_labels(
        "resources", 
        concept_type=lmss_graph.key_concepts["Area of Law"], 
        num_results=3): 
    # output iri and name
    print(f'{result["iri"]} -> {result["label"]}')

# Output:
http://lmss.sali.org/R8NS2clrdzMdZxaKZRr45Tj -> Forest Resources Law
http://lmss.sali.org/RBioeVhKaUUVmf39x6B5LRh -> Mineral Resources Law
http://lmss.sali.org/R7KYATnZcNxJsZ1MQBADZ0x -> Water Resources and Wetlands Law

Versions

• 0.1.1, 2021-03-16: Minor fixes and enhancements to support Suggester API and new LMSS develop version
• 0.1.0, 2023-03-15: Initial public release to support Suggester API release on Docker Hub

License

This project is licensed under the MIT License - see the LICENSE file for details.