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semantic similarity framework for knowledge graph
| Date | Stars |
|---|---|
| 2026-07-31 | 441 |
| 2026-08-05 | 441 |
| 2026-08-06 | 441 |
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# Introduction
Sematch is an integrated framework for the development, evaluation, and application of semantic similarity for Knowledge Graphs (KGs). It is easy to use Sematch to compute semantic similarity scores of concepts, words and entities. Sematch focuses on specific knowledge-based semantic similarity metrics that rely on structural knowledge in taxonomy (e.g. depth, path length, least common subsumer), and statistical information contents (corpus-IC and graph-IC). Knowledge-based approaches differ from their counterpart corpus-based approaches relying on co-occurrence (e.g. Pointwise Mutual Information) or distributional similarity (Latent Semantic Analysis, Word2Vec, GLOVE and etc). Knowledge-based approaches are usually used for structural KGs, while corpus-based approaches are normally applied in textual corpora.
In text analysis applications, a common pipeline is adopted in using semantic similarity from concept level, to word and sentence level. For example, word similarity is first computed based on similarity scores of WordNet concepts, and sentence similarity is computed by composing word similarity scores. Finally, document similarity could be computed by identifying important sentences, e.g. TextRank.
KG based applications also meet similar pipeline in using semantic similarity, from concept similarity (e.g. `http://dbpedia.org/class/yago/Actor109765278`) to entity similarity (e.g. `http://dbpedia.org/resource/Madrid`). Furthermore, in computing document similarity, entities are extracted and document similarity is computed by composing entity similarity scores.
In KGs, concepts usually denote ontology classes while entities refer to ontology instances. Moreover, those concepts are usually constructed into hierarchical taxonomies, such as DBpedia ontology class, thus quantifying concept similarity in KG relies on similar semantic information (e.g. path length, depth, least common subsumer, information content) and semantic similarity metrics (e.g. Path, Wu & Palmer,Li, Resnik, Lin, Jiang & Conrad and WPath). In consequence, Sematch provides an integrated framework to develop and evaluate semantic similarity metrics for concepts, words, entities and their applications.
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## Getting started: 20 minutes to Sematch
### Install Sematch
You need to install scientific computing libraries **numpy** and **scipy** first. An example of installing them with pip is shown below.
```
pip install numpy scipy
```
Depending on different OS, you can use different ways to install them. After sucessful installation of **numpy** and **scipy**, you can install sematch with following commands.
```
pip install sematch
python -m sematch.download
```
Alternatively, you can use the development version to clone and install Sematch with setuptools. We recommend you to update your pip and setuptools.
```
git clone https://github.com/gsi-upm/sematch.git
cd sematch
python setup.py install
```
We also provide a [Sematch-Demo Server](https://github.com/gsi-upm/sematch-demo). You can use it for experimenting with main functionalities or take it as an example for using Sematch to develop applications. Please check our [Documentation](http://gsi-upm.github.io/sematch/) for more details.
### Computing Word Similarity
The core module of Sematch is measuring semantic similarity between concepts that are represented as concept taxonomies. Word similarity is computed based on the maximum semantic similarity of WordNet concepts. You can use Sematch to compute multi-lingual word similarity based on WordNet with various of semantic similarity metrics.
```python
from sematch.semantic.similarity import WordNetSimilarity
wns = WordNetSimilarity()
# Computing English word similarity using Li method
wns.word_similarity('dog', 'cat', 'li') # 0.449327301063
# Computing Spanish wordExcerpt of 14,430 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:2490c5ad783d7bc5, desc:knowledge graph