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A Python library for hubness reduced nearest neighbor search for the task of entity alignment with knowledge graph embeddings. The term kiez is a german word that refers to a city neighborhood.

Hubness is a phenomenon that arises in high-dimensional data and describes the fact that a couple of entities are nearest neighbors (NN) of many other entities, while a lot of entities are NN to no one. For entity alignment with knowledge graph embeddings we rely on NN search. Hubness therefore is detrimental to our matching results. This library is intended to make hubness reduction techniques available to data integration projects that rely on (knowledge graph) embeddings in their alignment process. Furthermore kiez incorporates several approximate nearest neighbor (ANN) libraries, to pair the speed advantage of approximate neighbor search with increased accuracy of hubness reduction.

You can install kiez via pip:

pip install kiez

Simple nearest neighbor search for source entities in target space:

from kiez import Kiez
import numpy as np
# create example data
rng = np.random.RandomState(0)
source = rng.rand(100,50)
target = rng.rand(100,50)
# fit and get neighbors
k_inst = Kiez()
k_inst.fit(source, target)
nn_dist, nn_ind = k_inst.kneighbors()

Using ANN libraries and hubness reduction methods:

from kiez import Kiez
import numpy as np
# create example data
rng = np.random.RandomState(0)
source = rng.rand(100,50)
target = rng.rand(100,50)
# prepare algorithm and hubness reduction
from kiez.neighbors import HNSW
hnsw = HNSW(n_candidates=10)
from kiez.hubness_reduction import CSLS
hr = CSLS()
# fit and get neighbors
k_inst = Kiez(n_neighbors=5, algorithm=hnsw, hubness=hr)
k_inst.fit(source, target)
nn_dist, nn_ind = k_inst.kneighbors()

You can find more documentation on readthedocs

The results and configurations of our experiments can be found in a seperate benchmarking repository

PRs and enhancement ideas are always welcome. If you want to build kiez locally use:

git clone [email protected]:dobraczka/kiez.git
cd kiez
poetry install

To run the tests (given you are in the kiez folder):

poetry run pytest tests

kiez is licensed under the terms of the BSD-3-Clause license. Several files were modified from scikit-hubness, distributed under the same license. The respective files contain the following tag instead of the full license text.

    SPDX-License-Identifier: BSD-3-Clause

This enables machine processing of license information based on the SPDX License Identifiers that are here available: https://spdx.org/licenses/