Semantic similarity framework for knowledge graphs

semantic, similarity, taxonomy, knowledge, graph, analysis, base, WordNet, DBpedia, YAGO, ontology
pip install sematch==1.0.4




The name of Sematch is composed based on Spanish "se" and English "match". The logo of Sematch is based on the Chinese Yin and Yang which is written in I Ching. Somehow, it correlates to 0 and 1 in computer science. It is also the abbreviation of semantic matching. The sematch tool kit aims to provide you a framework for matching your required information from public Knowledge Graphs semantically.

The first demo system overview is shown as below. overview

This is the corresponding online demo.

The goal of the sematch is to provide several core tools for applications based on Knowledge Graphs (KGs).

  • semantic similarity of concepts in KG
  • entity linking and disambiguation with KG
  • semantic parsing
  • question answering based on knowledge graph

In the current prototype of sematch, you can try the following examples in your project to retrieve a list of entities using combination of keywords e.g. a entity type and a related entity.

    from sematch.QueryEngine import Engine

    engine = Engine()
    print engine.query('university Spain')
    print engine.query('lakes China')
    print engine.query('airports in France')
    print engine.query('mountains Italy')
    print engine.query('lakes United States')

We have provided a RESTful web server based on python Flask. By running


you can have a running web-based interface.

This project is still under development.

The project is mainly maintained by Ganggao Zhu.

You can contact me via


Sematch: Semantic Entity Search from Knowledge Graph Zhu, G. & Iglesias, C. A (2015). In SumPre 2015 ESWC


Copyright 2015 UPM-GSI: Group of Intelligent Systems - Universidad Politécnica de Madrid (UPM)

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