<table><caption>Table 5.4 – Accuracy for relation classification, compared to the baseline.</caption><thead><tr><th>Timeout (s)</th><th>10</th><th>20</th><th>30</th><th>60</th><th>120</th><th>300</th><th>600</th><th>1200</th></tr></thead><tbody><tr><th>Ours (EV)</th><td>79.5</td><td>79.7</td><td>79.6</td><td>80.1</td><td>80.4</td><td>80.3</td><td>80.4</td><td>80.4</td></tr><tr><th>Ours (MC)</th><td>82.0</td><td>82.1</td><td>82.7</td><td>82.9</td><td>83.4</td><td><strong>83.6</strong></td><td><strong>83.6</strong></td><td><strong>83.6</strong></td></tr><tr><th>Baseline</th><td></td><td></td><td></td><td></td><td></td><td>80.4</td><td></td><td></td></tr></tbody></table>

## 5.4.2 Relation Classification

We now evaluate our Concepts of Neighbors-based relation classification module individually on the Relation Classification task.

**Experiment Design** These experiments are made on the positive examples of TACRED, i.e., the examples that have an annotation other than *no_relation*. As our method does not have any use of a development corpus, we merge this corpus with the training one. We finally obtain a dataset composed of 18,446 training examples and 3,325 test examples. The quality measure usually used on TACRED is the micro-averaged F-score. However, as there is no negative class on this task, this measure does not make sense, and therefore we use accuracy.

In these experiments, as we work on a subset of TACRED we cannot compare this approach directly to other existing methods. Therefore, we compare our approach to a basic baseline in the RECENT ([LC21], see Section 5.3.1) paradigm. This baseline simply predicts, for given subject and object types, the relation type that appears the most often amongst the training examples with the same subject and object types. Therefore, scoring over this baseline implies that useful knowledge based on the semantics of the sentences has been used to make predictions.

As the algorithm for the computation of Concepts of Neighbors is *anytime*, we have to choose a timeout for our experiments. In order to see how the timeout influences the classification task, several timeouts were tested between 10 and 1200 seconds. In addition, the dependency tree pruning strategy (see Section 5.3.1) was used with $K = 1$. Finally, experiments were run using both the MC and the EV scoring method.

Our approach was implemented in Java<fnref n="4" /> and uses the *CONNOR* library ([ACF22b], see Section 4.3.3) for the computation of Concepts of Neighbors.

<footnote n="4">4. Accessible here: https://gitlab.inria.fr/hayats/conceptualknn-relex</footnote>
<figure><img src="image_27.png" /><figcaption>Figure 5.4 – Average number of computed (pre-)concepts per example and proportion of examples having a fully computed sets of Concepts of Neighbors</figcaption></figure>

**Results** Table 5.4 presents the accuracy for the baseline and for our approach. First, it can be observed that the baseline has an accuracy of 80.4%, which is particularly high, which means that the dataset leaves little space for progress. Second, it shows that our approach with the EV scoring method shows negative result, hardly competing with the baseline for timeouts over 600 seconds. Then, it can be read that for any timeout, the proposed approach with the MC scoring method has a better accuracy than the baseline, surpassing it by 3.2 points for a timeout of over 300s.

In addition, this table clearly shows a saturation phenomenon: there is an important gain when timeout gets from 10s to 120s, a gain that is far smaller from 120s to 1200s. It can be intuited that this comes from the fact that most concepts are computed before 120 seconds, and only a few concepts are added after 120s. This is confirmed by Figure 5.4: the first graph shows that effectively most of the concepts are computed in less than 120s, and the second graph shows that, for a timeout of over 600s, for over 99% of the examples the full set of concepts of neighbors is computed, i.e., the prediction is made from the exact set of concepts of neighbors, and not from an approximation of it.

In the light of the results, in the following, this relation classification method is used with the MC scoring method and a timeout of 300 seconds.
<table><caption>Table 5.5 – F-score for several Relation Extraction methods on TACRED</caption><thead><tr><td>Method</td><td>F1 score</td></tr></thead><tbody><tr><td>LUKE [Yam+20]</td><td><b>72.7</b></td></tr><tr><td>BERT-LSTM-Base [SL19]</td><td>67.8</td></tr><tr><td><em>Ours</em></td><td><b>66.9</b></td></tr><tr><td>C-GCN [ZQM18]</td><td>66.4</td></tr><tr><td>GCN [ZQM18]</td><td>64.0</td></tr></tbody></table>

### 5.4.3 Relation Extraction

Now that we have shown that our Concepts of Neighbors-based method is a valid approach for relation classification and that we have chosen a deep learning relation detection module, both can be assembled to form a full relation extraction method. In this subsection, we present the experimental process to evaluate this method, as well as both quantitative and qualitative results.

**Experiment Design** We evaluate our two-step approach on the full TACRED dataset in order to compare it to existing approaches. To do so, according to the structure presented in Figure 5.1, we process the test examples of TACRED with *luke-base*, and obtain examples classified as positive or negative. Then, each example classified as positive is processed by our Concept of Neighbors module for relation classification. Note that, as the relation detection step is made separately, the relation classification cannot predict the *no_relation* class.

**Quantitative Results** Table 5.5 compares our method with previous Relation Extraction methods. It shows that although our method is not competitive with pre-trained language models such as BERT or LUKE, it outperforms approaches based on graph convolution networks. Indeed, our method beats by 2.9 F-score points the basic graph convolution network (GCN) and by 0.5 points the contextualized graph convolution network (C-GCN). This is interesting because our method and those two methods are conceptually close: both are based on the representation of sentences as a graph, both use the pruned dependency tree of the sentences, and both add to this modeling a semantic layer (a word embedding for GCN and C-GCN, WordNet for our approach). The main benefit of our approach is its ability to provide explanations for the examples classified as positive.
<figure><img src="image_28.png" /><figcaption>Figure 5.5 – Example of rule body</figcaption></figure>

**Qualitative Results** To illustrate the explainability of our approach, let us take for example the sentence “Sollecito has said he was at **his** own apartment in **Perugia**, working at his computer.” *luke-base* predicts that there is a relation between the subject (**his**) and the object (*Perugia*). As the subject is a person and the object a city, there are only three compatible relations: *per:cities_of_residence*, *per:city_of Death* or *per:city_ofBirth*. After computation of the Concepts of Neighbors, we observe that the relation *per:city_of_residence* is predicted, as six rules of confidence 1 predict it, while only one rule each predicts the other two compatible relations. Figure 5.5 shows the body of one of those rules. It can be read as:

- The subject has lemma *he* and is the possessor of an apartment;

- The object is the name of a city in which there is something.

Even if this pattern is too specific to form a general rule, it can be inferred that, knowing there is a relation between the subject and the object, we can be pretty sure that any sentence following this pattern has the relation *per:cities_of_residence* between its subject and its object. To complete this explanation, we can look at the training examples matching this rule. In our case, there is one sentence matching it: “Wilbert Gibson walked from **his** apartment to the grocery store earlier this week – that’s what people do in **New York City** – and thought this must be what it’s like to be a celebrity.” We can see that this sentence effectively expresses the relation *per:cities_of_residence*, but quite implicitly. Therefore, this is interesting to see that this kind of pattern can be captured and exploited by our approach.

In practice, we observe that the rules of maximum confidence have systematically a confidence equal to 1. This is due to the fact that Concepts of Neighbors compute rules specific enough to match a few cases, and therefore to have a low extensional distance. After reviewing the explanations for ten randomly chosen correct predictions, we can ob-
serve that 56% of the 172 graph patterns seem reliable. Most of those reliable explanations are considered as such because of a lemma or a synset appearing in the graph pattern (for example, the word *daughter* to characterize the relation *per:children*). In addition, we observe that the reliability of the explanations depends on the relation type. For example, it can be pointed out that for an example predicting the relation *per:top_member/employee*, most of the explanations are invalid. This is caused by the fact that there is a great variety of words or formulations expressing this relation, and therefore the same one is rarely used several times.

## 5.5 Conclusion

This chapter presents a two-step explainable approach for relation extraction. This approach is based on the division of the relation extraction task in two sub-tasks. The first one, relation detection, is addressed with a fine-tuned transformer-based LLM. The second one, relation classification, relies on the modeling presented in Chapter 3, and is addressed by an explainable, instance-based approach using the Concepts of Neighbors method. Both the relation detection and the relation classification modules are conceived to be integrated in the workflow presented in Chapter 3. This work has been evaluated on TACRED, a standard dataset for relation extraction. It has shown positive results on both the sub-tasks, and even if it cannot compete with state-of-the-art transformers-based approach on relation extraction, the performances are similar to graph convolution deep-learning approaches while presenting an explicit gain in terms of explainability.

Several aspects of the presented work need further research. First, concerning the relation detection module, as presented before the fine-tuning of LUKE has not been possible to reproduce, and an equivalent fine-tuning would probably have shown better performances. In addition, the domain of LLM is evolving rapidly, and new, more efficient models are published regularly. Concerning the relation classification module, it relies on both the graph modeling of texts presented in Chapter 3 and on the Concepts of Neighbors method presented in Chapter 4. Possible enhancement for those aspects are presented in those chapters. Concerning the experimental settings, the TACRED dataset has raised criticisms through the years, due to the low reliability of its annotations, especially for the harder cases, as presented in [AGH20]. In addition, TACRED is composed of examples extracted mainly from newspapers, written in literary English, and with a wide spectrum of themes. Therefore, this is quite far from the considered use-cases (described in Chap-
ter 3), with domain-specific texts composed of short, factual sentences. The creation of a
new dataset (based on the WebNLG dataset [Gar+17], originally made for natural lan-
guage generation) with domain-specific sub-datasets and more factual examples has been
initiated during this thesis, but this has not been published yet.

Finally, the main aspect needing enhancement is the explainable aspect of this method.
As for today, this method is explainable for an expert user, capable of interpreting con-
fidence values and concept intensions. In order to make this method explainable for a
non-expert user, and therefore allowing interaction as presented in Chapter 3, the current
explanation (i.e., the set of confidence values and the set of concepts of neighbors) has to
be reduced and transformed into a concise explanation. Preliminary works are being made
on this aspect, using a graph mining method to extract the significant patterns from the
intensions of the concepts used for the prediction.
CONCLUSION AND PERSPECTIVES

This thesis is part of the current research in both the semantic web and the natu-
ral language processing fields, by attacking the problem of knowledge graph construction
from text. This problem is crucial today, for several reasons. First, the mass of avail-
able text data is bigger than ever and virtually covers the whole human knowledge, but
can be hard to access. Second, many semantic web technologies are now for querying,
exploring, completing or editing knowledge graphs. Finally, diverse approaches exist for
making a knowledge graph usable by an end-user. Therefore, transforming text in knowl-
edge graph could allow for an easier process, completion, and access to diverse forms of
knowledge, from generalist encyclopedic knowledge to domain-specific or context-specific
knowledge. However, due to the ambiguity of language and of the task itself, knowledge
graph construction from text is far from being trivial.

The approach proposed in this work has the specificity to be user-centered: instead
of proposing an end-to-end system trained on pre-existing data, we present a system
that learns by interacting with the user, and co-construct the knowledge graph with
them. To do so, the system is designed according to a user-centered workflow, allowing
for interaction during the whole process. The overall objective is to produce a resulting
knowledge graph This workflow relies on different technologies and methods in terms of
text representation, human-machine interaction, machine learning and inference systems,
and some of them are studied in this thesis.

The first contribution of this thesis is the workflow model itself (Chapter 3). The
workflow relies on an intermediate representation of text as a graph, representing both
its semantic and syntactic aspects. This intermediate representation also embeds a pre-
annotation to the elements that could appear in the resultant knowledge graph – this
pre-annotation being made by a machine learning approach. Two units use this represen-
tation. The first is an interactive channel, based on a human-machine interface as well as
an instance-based, explainable machine learning approach using Concepts of Neighbors,
a technique issued from the Formal Concept Analysis (FCA) framework. This channel
aims to assist the user in the manual construction of the knowledge graph. The second
is an automated channel that, based on the actions of the user through the interactive
unit and through a rule-based inference system, extracts knowledge from the intermediate
representation into the knowledge graph. The second contribution (Chapter 4), mainly
theoretical, is a set of additions to the FCA framework, especially on the Concepts of
Neighbors method, in order to adapt it for the desired task: relation extraction on graphs.
These contributions consist into a new, more general formalization of the notion of con-
cepts of neighbors, an adaptation of it for the n-ary case, and a method for handling RDF
graphs in this framework. The last contribution (Chapter 5) is the conception and evalua-
tion of a relation extraction system, composed of a deep learning relation detection module
and a Concepts of Neighbors-based relation classification module. Both those modules are
conceived to be compatible with the workflow – for the construction of the intermediate
representation for the relation detection module, and for the interactive unit for the rela-
tion classification module. This method presents results comparable to pre-existing deep
learning approach, while presenting advantages in terms of interpretability.

Perspectives

Several aspects of the contributions of this thesis require further research. Those dif-
ferent points are detailed below.

**Explainability of the Concepts of Neighbors** As of today, our relation classification method presented in Chapter 5 is explainable for an expert user: the classification results from a scoring made on the set of concepts of neighbors of a given individual, and an expert can interpret the score and the intensions and extensions of the concepts to comprehend and verify the explanation. However, a non-expert end user cannot understand easily such an explanation, nor validate it. In addition, as presented in Chapter 3, the explanation should take the form of a rule applicable by the inference system on the intermediate modeling. Preliminary works exploring the idea of using a graph mining method (namely GraphMDL+ [BCF21]) on the intensions of the concepts used in the prediction in order to produce such rules have been conducted, and have shown promising results.

**New Relation Extraction Dataset** TACRED [Zha+17], the standard dataset in Relation Extraction used in this thesis, which is a widely used benchmark in the domain, can
be criticized. First, from a general point of view, as shown in [AGH20], the annotations of TACRED, obtained by crowdsourcing, are of poor quality, especially for the hardest individuals. In addition, the data of TACRED itself is noisy: it should contain annotated plain sentences in English, mainly extracted from newspapers, but some of those sentences either are malformed (e.g., by merging the headline of an article and its first sentence) or contains artifacts (such as metadata of newspaper article or special characters replaced by strings – e.g., "-RRB-" replacing "("). Finally, this dataset contains longs, complex sentences written in a literary style, which is quite far from our use cases on factual texts. Therefore, we decided to create a new relation extraction dataset. This dataset is based on WebNLG [Gar+17], a dataset for text generation from RDF triples already adapted to the reverse task (RDF triples generation from text). The sentences are shorter, factual, and domain-centric, and therefore should be more adapted to our needs. The main task for transforming this dataset into a relation extraction dataset is entity alignment on text, which is not trivial and asks for more development work before evaluation and publication.

**Human-Machine Interface** The user interface, core of the interactive channel of the workflow, should have several properties. First, it should be an edition tool for the produced knowledge graph. Second, it should map the text (or its intermediate representation) with the knowledge graph. In addition, it should present and permit validation of the suggestions and explanations produced by the relation extraction module. In other terms, even if technically part of the interactive unit, this interface has to interact with modules and bases in the whole system. Initially, a basic command line interface could be developed for experimenting with interactions scenarios, before developing a graphical interface.

**Automated Unit** This unit is constituted of two elements: a rule base and an inference system. A basic implementation would be using a plain set of rules and a query system for the inference. For example, the rules could be formulated as SPARQL queries and the inference system could be a basic query engine applying those queries on the intermediate representation. Then, the system could be enriched by adding a confidence values on the rules, confidence that could evolve through user interactions.

**Extension of the Pattern Language of Projected Graph Patterns** In the presented Graph-FCA theory, the pattern language of PGP is quite restrictive, as it does not
allow unlabeled hyper-edges or paths of unspecified length. This pattern language could
be extended in order to handle more complex graph patterns, on the model of regular
expressions. This would impact the PGP inclusion definition, as well as the combinatorics
of the graph concepts. In particular, for what concerns the concepts of neighbors, this
could have an important impact on the algorithmic complexity, that should be studied.

**Algorithmic Enhancements for the Computation of Concepts of Neighbors**
In a more theoretical and algorithmic side, several aspects of the algorithmic framework of the Concepts of Neighbors could be developed. The point of these enhancements is to reduce the computation time of Concepts of Neighbors, in order to grant interactivity even while working on important datasets.

First, as no strategy for choosing an incidence in the partitioning algorithm is specified,
the current implementations use a basic *first in, first out* strategy. However, there is no
reason to believe this strategy is optimal, neither in terms of computation time nor in
terms of relevance of the explored concepts. Therefore, diverse strategies, based on the
structure of the data and the already processed incidences, could be explored. In addition,
it has been experimentally observed that the choice of some specific incidences provokes
an explosion of the computation time due to a combinatorial problem. Those problematic
cases could be characterized, and specific strategies to avoid them could be conceived.

The *lazy join* algorithm is also subject to enhancement. A post-insertion downward
propagation algorithm has been developed in order to reduce the size of the match-sets –
and by consequence of the match-trees – and therefore have better performance in terms
of memory and time. However, if this enhancement has shown positive results in the case
of unary concepts of neighbors, further work is needed to adapt it to the n-ary case, due
to the potentially disjoint nature of the intensions. Some strategies have already been
considered, but this needs further research before publication.
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Publications

[ACF21] H. Ambre Ayats, Peggy Cellier, and Sébastien Ferré, « Extracting Relations in Texts with Concepts of Neighbours », in: *International Conference on Formal Concept Analysis*, 2021, pp. 155–171, DOI: https://doi.org/10.1007/978-3-030-77867-5_10.

[ACF22a] H. Ambre Ayats, Peggy Cellier, and Sébastien Ferré, « A Two-Step Approach for Explainable Relation Extraction », en, in: *Advances in Intelligent Data Analysis*, 2022, pp. 14–25, DOI: https://doi.org/10.1007/978-3-031-01333-1_2.

[ACF22b] H. Ambre Ayats, Peggy Cellier, and Sébastien Ferré, « CONNOR: Exploring Similarities in Graphs with Concepts of Neighbors », in: *Existing Tools and Applications for Formal Concept Analysis (ETAFCA)*, 2022, pp. 219–224.

[ACF24] H. Ambre Ayats, Peggy Cellier, and Sébastien Ferré, « Concepts of neighbors and their application to instance-based learning on relational data », in: *International Journal of Approximate Reasoning* 164 (2024), p. 109059, DOI: 10.1016/j.ijar.2023.109059.

[Aya22] H. Ambre Ayats, « Construction de Graphes de Connaissance à partir de textes avec une I.A. centrée-utilisateur », in: *Rencontres Etudiants Chercheurs en Informatique pour le TAL (RECITAL)*, 2022, pp. 33–46.
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**Titre** : Construction de graphes de connaissances à partir de textes avec une intelligence artificielle explicable et centrée-utilisateur·ice

**Mot clés** : Traitement automatique du langage naturel, explicabilité, IA centrée-utilisateur·ice, analyse de concepts formels, web sémantique, graphes de connaissances

**Résumé** : Avec les progrès récents dans le domaine de l'intelligence artificielle, la question du contrôle humain est devenu centrale. Aujourd'hui, cela passe à la fois par des recherches en explicabilité et des systèmes centrés autour de l'interaction avec l'utilisateur·ice. De plus, avec l'expansion du web sémantique et des méthodes de traitement automatique du langage naturel, la tâche de construction de graphes de connaissances à partir de textes est devenu un enjeu important. Cette thèse présente un système centré-utilisateur·ice pour la construction de graphes de connaissances à partir de textes.

Cette thèse présente plusieurs contributions. Tout d'abord, nous introduisons un workflow centré-utilisateur·ice pour la tâche sus-citée, ayant la propriété d'automatiser progressivement les actions de l'utilisateur·ice tout en lui laissant un contrôle fin du résultat. Ensuite, nous présentons nos apports dans le domaine de l'analyse de concepts formels, utilisés afin de concevoir un module d'apprentissage fait néant et explicable pour la tâche de classification de relations. Enfin, nous présentons nos apports dans le domaine de l'extraction de relations, et comment ces apports s'inscrivent dans le workflow présenté précédemment.

**Title:** Knowledge Graph Construction from Text with an Explanable, Human-Centered Artificial Intelligence

**Keywords:** Natural Language Processing, Explainability, User-Centric AI, Formal Concept Analysis, Semantic Web, Knowledge Graphs

**Abstract:** With recent advances in artificial intelligence, the question of human control has become central. Today, this involves both research into explainability and designs centered around interaction with the user. What's more, with the expansion of the semantic web and automatic natural language processing methods, the task of constructing knowledge graphs from texts has become an important issue. This thesis presents a user-centered system for the construction of knowledge graphs from texts. This thesis presents several con-

tributions. First, we introduce a user-centered workflow for the aforementioned task, having the property of progressively automating the user's actions while leaving them a fine-grained control over the outcome. Next, we present our contributions in the field of formal concept analysis, used to design an explainable instance-based learning module for relation classification. Finally, we present our contributions in the field of relation extraction, and how these fit into the presented workflow.