Updated in RefutationModel.fit documentation.

Author: pi-kappa-devel

README.md

@@ -42,8 +42,8 @@ the package’s functionality (without neural-symbolic components) are
 given by (Karapanagiotis and Liebald 2023).
 
 The training loops for both deep and symbolic learning models are
-implemented in [tensorflow](https://www.tensorflow.org) (Martín Abadi et
-al. 2015). The pure deep learning model inherits from the
+implemented in [tensorflow](https://www.tensorflow.org) (Abadi et al.
+2015). The pure deep learning model inherits from the
 [keras](https://keras.io) model class (Chollet et al. 2015). The
 neural-symbolic model is implemented using the logic tensor network
 ([LTN](https://pypi.org/project/ltn/)) framework (Badreddine et al.
@@ -257,6 +257,15 @@ The package is distributed under the [MIT license](LICENSE.txt).
 <div id="refs" class="references csl-bib-body hanging-indent"
 entry-spacing="0">
 
+<div id="ref-tensorflow2015" class="csl-entry">
+
+Abadi, Martín, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen,
+Craig Citro, Greg S. Corrado, et al. 2015. “TensorFlow:
+<span class="nocase">Large-scale</span> Machine Learning on
+Heterogeneous Systems.” <https://www.tensorflow.org/>.
+
+</div>
+
 <div id="ref-badreddine2022" class="csl-entry">
 
 Badreddine, Samy, Artur d’Avila Garcez, Luciano Serafini, and Michael
@@ -267,7 +276,7 @@ Spranger. 2022. “Logic Tensor Networks.” *Artificial Intelligence* 303:
 
 <div id="ref-keras2015" class="csl-entry">
 
-Chollet, François et al. 2015. “Keras.”
+Chollet, François et al. 2015. “Keras.” <https://keras.io>.
 
 </div>
 
@@ -302,7 +311,7 @@ Adaptation.” In *Proceedings of the VLDB Endowment*, 15:465–77.
 
 Karapanagiotis, Pantelis, and Marius Liebald. 2023. “Entity Matching
 with Similarity Encoding: A Supervised Learning Recommendation Framework
-for Linking (Big) Data.”
+for Linking (Big) Data.” <http://dx.doi.org/10.2139/ssrn.4541376>.
 
 </div>
 
@@ -324,15 +333,6 @@ Systems* 293 (June): 111678.
 
 </div>
 
-<div id="ref-tensorflow2015" class="csl-entry">
-
-Martín Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen,
-Craig Citro, Greg S. Corrado, et al. 2015. “TensorFlow:
-<span class="nocase">Large-scale</span> Machine Learning on
-Heterogeneous Systems.”
-
-</div>
-
 <div id="ref-mudgal2018" class="csl-entry">
 
 Mudgal, Sidharth, Han Li, Theodoros Rekatsinas, AnHai Doan, Youngchoon

src/neer_match/__init__.py

@@ -3,4 +3,4 @@
 Neural-symbolic Entity Reasoning and Matching.
 """
 
-__version__ = '0.7.33'
+__version__ = '0.7.34'

src/neer_match/reasoning.py

@@ -17,8 +17,8 @@ class RefutationModel(NSMatchingModel):
 
     Inherits :class:`neer_match.matching_model.NSMatchingModel` and provides additional
     functionality for refutation logic. The built-in refutation logic allows one to
-    refute the significance of one or more conjectured associations in detecting
-    entity matches.
+    refute the significance of one or more similarities of a  conjectured association
+    in detecting entity matches.
     """
 
     def __make_claims(self, axiom_generator, refutation):
@@ -181,9 +181,9 @@ def fit(
                 a dictionary, the keys are association names and the values are
                 similarity names. If the value is None, all similarities in the
                 association are used.
-            satisfiability_threshold: The satisfiability threshold.
-            axioms_non_sat_scale: The non-satisfiability scale.
-            axioms_sat_scale: The satisfiability scale.
+            penalty_threshold: The penalty threshold.
+            penalty_scale: The non-satisfiability scale.
+            penalty_decay: The non-satisfiability decay.
             satisfiability_weight: The satisfiability weight.
             verbose: The verbosity level.
             log_mod_n: The logging frequency.