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|authors=Andrea Baraldi,Francesco Del Buono,Matteo Paganelli,Francesco Guerra | |authors=Andrea Baraldi,Francesco Del Buono,Matteo Paganelli,Francesco Guerra | ||
|dblpUrl=https://dblp.org/rec/conf/sebd/0002BP022 | |dblpUrl=https://dblp.org/rec/conf/sebd/0002BP022 | ||
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==Landmark Explanation: a Tool for Entity Matching== | ==Landmark Explanation: a Tool for Entity Matching== | ||
Latest revision as of 17:53, 30 March 2023
Paper
| Paper | |
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 edit
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| description | |
| id | Vol-3194/paper8 |
| wikidataid | Q117344882→Q117344882 |
| title | Landmark Explanation: a Tool for Entity Matching |
| pdfUrl | https://ceur-ws.org/Vol-3194/paper8.pdf |
| dblpUrl | https://dblp.org/rec/conf/sebd/0002BP022 |
| volume | Vol-3194→Vol-3194 |
| session | → |
Landmark Explanation: a Tool for Entity Matching
Landmark Explanation: a Tool for Entity Matching
(Discussion Paper)
Andrea Baraldi1 , Francesco Del Buono1 , Matteo Paganelli1 and Francesco Guerra1
1
DIEF - University of Modena and Reggio Emilia, Modena, Italy
Abstract
We introduce Landmark Explanation, a framework that extends the capabilities of a post-hoc perturbation-
based explainer to the EM scenario. Landmark Explanation leverages on the specific schema typically
adopted by the EM datasets, representing pairs of entity descriptions, for generating word-based expla-
nations that effectively describe the matching model.
Keywords
Entity Matching, Post-hoc Explanation, Perturbation of EM datasets
1. Introduction
Machine Learning (ML) and Deep Learning (DL) models have been successfully applied to the
Entity Matching (EM) problem as the state-of-the-art approaches demonstrate (e.g., DeepER [1],
DeepMatcher [2], DITTO [3], AutoML [4] and others [5, 6, 7]). Nevertheless, they are black-box
models: the difficulty to evaluate [8] and to interpret their behaviors [9] hampers their adoption
in business scenarios.
Although many explanation systems have already been proposed in the literature (e.g.,
LIME [10], Shapley [11], Anchor [12], and Skater1 ), their application to EM tasks is not straight-
forward and only few approaches have partially addressed it [13, 14, 15, 16]. EM is conceived
as a binary classification problem, where the classes show if the pairs of entities described in
the dataset records are or are not matching. The structure of the datasets is then “unusual"
for ML and DL techniques used to manage single evidence records and generic techniques for
explaining ML and DL models cannot be straightforwardly applied.
In this paper, we present Landmark Explanation a post-hoc perturbation-based local explainer
for EM approaches. Post-hoc perturbation-based explainers build a surrogate linear model that
approximates the model locally to the instance to explain. The surrogate linear model is trained
with synthetic data. The dataset is generated by creating a number of alterations of the record
to explain (in the so-called perturbation phase) and predicting their class by applying them the
original model (in the so-called reconstruction phase). The explanation is directly obtained from
SEBD 2022: The 30th Italian Symposium on Advanced Database Systems, June 19-22, 2022, Tirrenia (PI), Italy
$ andrea.baraldi96@unimore.it (A. Baraldi); francesco.delbuono@unimore.it (F. D. Buono);
matteo.paganelli@unimore.it (M. Paganelli); francesco.guerra@unimore.it (F. Guerra)
http://morespace.unimore.it/francescoguerra/ (F. Guerra)
� 0000-0001-8119-895X (M. Paganelli); 0000-0001-6864-568X (F. Guerra)
© 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
Workshop
Proceedings CEUR Workshop Proceedings (CEUR-WS.org)
http://ceur-ws.org
ISSN 1613-0073
1
https://github.com/oracle/Skater
� left_description left_name right_description right_name
sony white cybershot t series digi- sony white cybershot t se- top loading leather black sony lcs-csl cyber-shot
tal camera jacket case with stylus ries digital camera jacket camera case
lcjthcw for 2007 cybershot t series case with stylus lcjthcw
camera stylus include...
Table 1
Pairs of non-matching entity descriptions.
the surrogate model. The importance of a feature in the decision is computed by multiplying its
value in the record with the linear coefficient of the surrogate model. In textual databases, as
the ones considered in this paper, the features of the model are typically the words used in the
entity descriptions.
Example 1. Table 1 shows an example of non-matching descriptions. Both the entities refer to
camera cases produced by the same brand, but since their product code is different they are not be
considered as the same entity. An explanation of for this record consists of a values associated to
each word in the description. Words are extracted from the descriptions via a tokenization process
(we evaluated the application of stemming techniques and the deletion of stop words). For this
reason the terms “token" and “word" are used as synonym in this paper.
Landmark Explanation leverages the specificity of the EM dataset by introducing two main
innovations. The first is the generation of two explanations per dataset entry, one for each entity
described in the record. The second is a mechanism for computing meaningful explanations,
especially for records belonging to non-matching classes. The descriptions of a non-matching
entity are composed of different words, and selecting the ones that mostly contributed in the
decision is a complex task even for humans. To address the problem, we inject additional words
extracted from one entity into the second entity before the perturbation. The result is that the
number of different words in non-matching entities decreases, while the similarity increases,
thus enabling the approach to select the most relevant elements for the decision.
We implemented Landmark Explanation as an add-on component of the LIME system. The
results of the experiments show that the explanations generated for EM datasets outperform
the ones of the competing approaches in accuracy and “interest" for the users. This paper
summarizes the Landmark Explanation presentations in [17, 18].
2. The Landmark Explanation approach
2.1. Landmark Explanation principles
Landmark Explanation adapts a local post-hoc explanation technique to the EM scenario. Indeed,
the direct application of a perturbation mechanism based on token removals is not effective for
EM datasets. The reason is that removing random tokens is likely to affect both the entities
represented by the two descriptions. The generated synthetic records may then contain null
or non coherent perturbations where the same tokens referring to the different entities are
removed. These inconsistent perturbations lead to biased explanations. Moreover, post-hoc
� L entity R entity Class Model
LIME components
Landmark generation Perturbation Reconstruction Explanation via
+ augmentation generation & prediction surrogate model
Landmark Explanation
Explanation Explanation
Figure 1: Landmark Explanation workflow
explanation systems adopt techniques for generating perturbations based on token removal. The
resulting explanations for non-matching entity descriptions (the greatest parts of the records
generally in EM datasets) are not useful as we will describe later on. Landmark Explanation
addresses these issues by introducing the following two main innovations.
Double explanation. The first innovation consists of the generation of two explanations for
each dataset entry. When we compute an explanation, we perturb a description (the varying
entity) and keep unchanged its paired description (the landmark entity). The explanation assigns
an impact to each token of the perturbed description. We repeat the computation by exchanging
varying and landmark entities. Each result explains the model decision from the perspective of
one of the two entities described in the record.
Injection of features. The second is a mechanism is for contrasting the asymmetric nature
of the EM problem: an explanation of a matching pair is always composed of “interesting"
tokens since they express the reasons why the entities have been considered as matching. The
same does not happen for non-matching entities that have many reasons to be different. We
address this issue by injecting additional tokens extracted from the landmark entity into the
varying entity before the perturbation. Therefore, such a dataset contains entities close to the
landmark, and the surrogate model trained with these entities will be able to highlight the
distinctive tokens, that mainly contribute to the decision. Without the injection, descriptions of
non-matching entities would have a large number of tokens that would uniformly contribute to
the decision with the same low impact.
2.2. Landmark Explanation explanations
Let 𝑟 be a record in an EM dataset representing a pair of entity descriptions (𝑒𝑥 , 𝑒𝑦 ), each one
composed of a collection of tokens {𝑡𝑖1 , ..., 𝑡𝑖𝑛𝑖 }, where 𝑖 ∈ {𝑥, 𝑦}, and 𝑛𝑖 is the number of
tokens belonging to the description of the entity 𝑖. The application of an EM binary classi-
fication model to 𝑟 returns {0, 1} when 𝑟 is composed of non-matching or matching entity
descriptions, respectively. An explanation is composed of a score for each description token
𝐸𝑖 = {𝑠𝑖1 , ..., 𝑠𝑖𝑛𝑖 }, where 𝑖 ∈ {𝑥, 𝑦}, 𝑠 ∈ R, 𝑠𝑖𝑗 is the score of token 𝑡𝑖𝑗 . 𝑆𝑥 is the explanation
generated by selecting 𝑒𝑦 as the landmark and, vice-versa, 𝑆𝑦 by selecting 𝑒𝑥 as the landmark.
Positive scores push the decision towards the class of matching entities, negative towards
non-matching. The highest the absolute value of the score, the highest the importance of the
token associated with the score. An explanation with augmented features assumes the form of
𝐸𝑖 = {𝑠𝑥1 , ..., 𝑠𝑥𝑛𝑥 , 𝑠𝑦1 , ..., 𝑠𝑦𝑛𝑦 }, where for the explanation 𝐸𝑥 , the scores 𝑠𝑦𝑗 are the ones of
the injected features from the entity description 𝑒𝑦 (and vice-versa for the explanation 𝐸𝑦 ).
�2.3. Landmark Explanation workflow
Figure 1 shows the description of the end-to-end workflow implemented by Landmark Explana-
tion. The yellow boxes are the ones provided by a generic explanation system. The white boxes
are provided by Landmark Explanation.
Landmark generation and entity augmentation. The descriptions of the entities are tok-
enized, and a prefix is added to each token to mark the provenance attribute. We set as landmark
the set of tokens of the first entity, the other set of tokens will be perturbed. In the case of
non-matching predictions, tokens are injected in the varying entity as described in Section 2.1.
The process is repeated exchanging the landmark and the varying entities.
Perturbation generation. A representation of the neighborhood for varying entities is gen-
erated by perturbing its tokens in multiple ways. We used LIME which generates a series of
textual phrases containing many combinations of the tokens of the varying description.
Reconstruction and prediction. We reconstruct the schema of the synthetic textual records
obtained in the last step. We concatenate each of these new records with the original landmark
entity. The produced pairs of entities are finally provided as input to the original EM model in
order to obtain the relative prediction scores.
Explanation via surrogate model. Finally, a surrogate linear model (one for each workflow,
one for the left and right entities, respectively) is trained on the perturbed dataset to learn an
approximation of the behavior of the original model in those localities. The surrogate model
takes in input the bag of words representation of the perturbed tokens and is trained to learn the
relation between the input and the prediction score produced by the model under explanation.
The coefficients learned during training represent the impact of each token in the prediction,
and are used to generate the explanations of the original EM model for each EM record. In our
implementation we adopt LIME to perform this task, but our approach is transparent to the
explanation tool selected.
2.4. Explaining ER Models
Studies applying interpretation techniques in the entity matching area [16, 14], and tools, like
Mojito [15] and Explainer [13], have been proposed. ExplainER provides a unified interface for
applying well-known interpretation techniques (e.g., LIME, Shapley, Anchor, and Skater) in the
EM scenario. Mojito adapts LIME for the explanation of single EM predictions and represents the
work closer to our approach. It extends LIME in two ways: 1) it exploits the subdivision of EM
data into attributes, 2) it introduces a new form of data perturbation, called LIME-COPY2 , which
allows generating match elements starting from non-match elements. Differently Landmark
Explanation, Mojito treats attributes atomically, distributing its impact equally to its constituent
tokens. Furthermore, Landmark Explanation analyzes the diversified impact that the same
token can generate depending on the entity considered as a landmark for the explanation.
2
In Section 3 we refer to this technique as Mojito Copy since it is part of the Mojito tool.
�3. Experimental evaluation
We evaluated the explanations generated by Landmark Explanation according to two main
perspectives: the fidelity in representing the EM Model (in Section 3.1) and the “quality" of the
explanation. For this last evaluation, we introduce a measure for assessing the interest of the
explanations (in Section 3.2) and we propose an example of explanation for non-matching entity
descriptions (in Section 3.3). This shows the importance of the token injection mechanism.
Dataset and Model. We perform an experimental evaluation against the datasets provided by
the Magellan library3 which is considered as a standard benchmark for the evaluation of
EM tasks. The datasets are divided into structured (iTunes-Amazon S-IA, DBLP-ACM S-DA,
DBLP-GoogleScholar S-DG, Walmart-Amazon S-WA), textual (Abt-Buy T-AB) and Dirty (iTunes-
Amazon D-IA, DBLP-ACM D-DA, DBLP-GoogleScholar D-DG, Walmart-Amazon D-WA). The
records in all datasets represent pairs of entities described with the same attributes. A label is
provided to express if the record represents a matching / non-matching pair of entities. A simple
logistic regression model is experimented as matcher, where the features are the similarities of
the paired attributes in the descriptions. We compute the similarity by applying the jaccard
measure on the trigrams of the attribute values. The experiments are performed by sampling
100 records per label (all records in datasets with smaller cardinality) and computing their
explanations. We generate base explanations, by using the tokens from an entity description
and augmented explanations, by generating explanations with the tokens of entity description
with the ones injected from the second entity description.
3.1. Fidelity of the explanations
To evaluate the fidelity of the explanations, i.e., if the weights assigned by Landmark Explanation
to the tokens generate a surrogate model that is consistent with the EM model, we randomly
remove 25% tokens from the record to explain, defining a new item. We then compared the
probability score obtained passing the new item to the EM model with the one of the original
records, where we have subtracted the sum of the coefficients associated with the removed
tokens. If the explanation model correctly represents the EM model these two values should
be close. The experiment is repeated 100 times per class, and the performance measured by
means of two metrics: the mean absolute error (MAE) between the explanation and the EM
Model and the accuracy that measures the percentage of times that the probability score of the
new item changes consistently with to the sum of the impacts of the tokens removed. Table 2
shows the results of the experiment. The column LIME shows the results obtained with LIME
with the same setting. Non-matching settings also include a comparison with the Mojito Copy
technique.
Discussion. The experiments show that the surrogate model built by Landmark Explanation
with the base perturbation provides an accurate representation of the EM model for records
representing matching pairs of entities. At the same time, the model built with the augmented
perturbation is an accurate representation of the EM model for record representing non-matching
pairs of entities. In particular, Table 2a shows that Landmark Explanation, applied to records
3
https://github.com/anhaidgroup/deepmatcher/blob/master/Datasets.md
� Base Augmented LIME Base Augmented LIME Mojito Copy
Acc. MAE Acc. MAE Acc. MAE Acc. MAE Acc. MAE Acc. MAE Acc. MAE
S-IA 0.940 0.226 0.793 0.251 0.847 0.240 0.669 0.248 0.736 0.127 0.624 0.267 0.022 0.569
S-DA 0.887 0.171 0.894 0.164 0.573 0.337 0.975 0.021 0.590 0.287 0.985 0.066 0.005 0.574
S-DG 0.836 0.196 0.823 0.196 0.757 0.200 0.895 0.086 0.660 0.306 0.935 0.107 0.005 0.504
S-WA 0.954 0.071 0.928 0.115 0.659 0.228 0.990 0.028 0.955 0.217 0.890 0.352 0.000 0.746
T-AB 0.908 0.066 0.854 0.146 0.758 0.118 0.860 0.076 0.680 0.047 0.795 0.092 0.045 0.328
D-IA 0.899 0.090 0.975 0.112 0.780 0.156 0.874 0.019 0.291 0.070 0.390 0.129 0.242 0.191
D-DA 0.942 0.030 0.979 0.041 0.940 0.025 0.615 0.071 0.300 0.027 0.690 0.036 0.010 0.173
D-D 0.929 0.107 0.963 0.152 0.891 0.115 0.540 0.305 0.375 0.118 0.640 0.235 0.040 0.437
D-WA 0.916 0.045 0.901 0.090 0.813 0.074 0.500 0.184 0.785 0.078 0.500 0.192 0.005 0.380
(a) Matching label. (b) Non-matching label.
Table 2
Evaluation of the fidelity of the explanations.
labeled as matching entity, performs better than LIME in the datasets when the perturbation is
generated with the base technique (it obtains better accuracy in all datasets and low MAE in 8/9
datasets). The augmented generation technique performs slightly worse: in 8/9 it obtains better
accuracy and in 5/9 lower MAE). Note that this can be motivated also by the increased number
of tokens in the augmented explanations. Nevertheless, the scores, when worst, are very close
to LIME. Table 2b shows the accuracy and the MAE obtained analyzing records referring to
non-matching labels. In this scenario, the augmented entity perturbation obtains the best scores
with an accuracy better than LIME in 3/9 datasets and a lower MAE in 7/9 datasets. Finally,
the copying technique introduced by Mojito to manage records associated with non-matching
labels does not show high performance. The reason is that Mojito generates a perturbation
by duplicating entire attributes. The result of this operation is that the tokens of the replaced
attribute have the same weights, and decrease the performance.
3.2. Quality of the explanations
Since there are many reasons to be dissimilar for two entities, the explanations of non-matching
entity descriptions are typically “slightly polarized" having negative values distributed in a
range close to zero and no value dominating the others. For the user, this means not being
able to grasp a strong motivation for the non-matching decision. To evaluate if we are able to
generate “interesting explanations", we introduced a heuristic according to which an explanation
for non-matching entities is interesting if it contains tokens that, if injected into the second
entity, would make the record classified as matching. These are the elements that make the
explanation interesting for the users. To evaluate if the explanations generated by Landmark
Explanation satisfy this property, we perform the same experiment described in Section 3.1,
but selecting the tokens to remove: negative tokens are removed when the label represents a
non-matching record (all tokens that contribute to the decision). Positive tokens are removed in
case of matching records. In Table 3 we measure the interest, which is the percentage of records
where the removal of the tokens was able to generate a change in the label.
Discussion. Landmark Explanation generates interesting explanations, and the perturbation
generated with the augmented technique effectively increases “the interest" of non-matching
� Base Augmented LIME Base Augmented LIME Mojito Copy
S-IA 0.652 0.404 0.702 S-IA 0.545 0.736 0.393 0.000
S-DA 1.000 0.940 0.965 S-DA 0.000 0.030 0.000 0.005
S-DG 0.660 0.610 0.925 S-DG 0.020 0.545 0.020 0.000
S-WA 1.000 0.785 0.870 S-WA 0.015 0.955 0.000 0.000
T-AB 0.985 0.575 0.995 T-AB 0.305 0.680 0.340 0.045
D-IA 0.561 0.278 0.311 D-IA 0.670 0.291 0.379 0.027
D-DA 0.695 0.715 0.800 D-DA 0.205 0.300 0.125 0.000
D-DG 0.635 0.530 0.735 D-DG 0.200 0.375 0.160 0.030
D-WA 0.915 0.545 0.880 D-WA 0.190 0.785 0.130 0.005
(a) Matching label. (b) Non-matching label.
Table 3
Evaluation of the interest associated to the computed explanations.
record explanations. In particular, Table 3a shows that Landmark Explanation is good but
slightly worse than LIME in terms of interest, when the records are labeled as matching class.
This happens even if the surrogate model is accurate (the MAE score is the lowest for all
experiments with the single-entity configuration). The problem is that in most of the cases,
even removing all tokens, the explanation created by Landmark Explanation belongs to the
same class as before the token removal. Note that if we set a decision threshold to 0.4, our
approach has the best results in all datasets. Table 3b shows that the augmented explanations of
non-matching entities generated by Landmark Explanation outperform LIME and Mojito Copy.
3.3. Showing the explanations
Original Tokens Original Tokens Original Tokens Augmented Tokens Original Tokens Augmented Tokens
l_price, nan r_name, case l_name, case l_name, lcjthcw
l_name, case r_id, 459 l_description, case l_name, jacket
l_description, series r_description, top r_name, lcs-csl r_name, sony
l_name, camera l_name, white
l_name, with r_name, sony
Right Landmark
Right Landmark
l_description, camera l_name, case
Left Landmark
Left Landmark
l_description, jacket r_name, camera
l_description, white r_name, sony r_description, black l_description, case
l_description, cybershot r_description, loading
l_name, jacket l_name, camera
l_description, cybershot r_price, nan
l_description, custom-fitted r_description, leather l_name, stylus l_description, camera
r_description, black r_name, lcs-csl
l_description, lcjthcw r_description, black l_name, white l_description, white
l_name, lcjthcw r_name, lcs-csl l_name, lcjthcw l_name, stylus
0.5 0.0 0.5 0.5 0.0 0.5 0.5 0.0 0.5 0.5 0.0 0.5 0.5 0.0 0.5 0.5 0.0 0.5
Token impact Token impact Token impact Token impact Token impact Token impact
(a) The base technique. (b) The augmented technique.
Figure 2: Visualizing an explanation. Red (green) bars are associated to the right (left) entity description.
Figure 2a shows the explanations computed with the base technique for the entity descriptions
in Table 1 1. We recall that positive impacts push towards the match decision, negative towards
a non-match decision. Landmark Explanation generates two explanations per record and we can
see that no token assumes a particular importance. The resulting explanation is therefore not
interesting (and useful) for the user. Figure 2b shows the explanation obtained by the injection
of the tokens from the landmark. The first explanation (where the right entity is the landmark)
clearly shows that the token case pushes towards the match decision (both the entities refer to
camera cases) and the code lcjthcw towards the non-match decision (it is different from the
code in the second description). The augmented tokens show that the code lcs-csl pushes
�towards a match decision. This means that if that code had been part of the description for the
left entity, it would have pushed the model towards a match decision. Similar considerations
can be done by observing the second explanation obtained setting the left entity as landmark.
4. Conclusion
This paper introduces Landmark Explanation a tool that makes a post-hoc perturbation-based
explainer able to deal with ML and DL models describing EM datasets. The approach has been
experimented coupled with the LIME explainer on a simple EM model based on logistic regres-
sion. The results show that the explanations generated by Landmark Explanation outperform
the ones generated by the competing approaches.
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