François Torregrossa
email: ftorregrossa@solocal.com

Robin Allesiardo
email: rallesiardo@solocal.com

Vincent Claveau
email: vincent.claveau@irisa.fr

Guillaume Gravier

Unsupervised Tree Extraction in Embedding Spaces for Taxonomy Induction

Keywords: Unstructured/Structured Data, Hyperbolic Embedding, Tree extraction, Taxonomy Induction

Exposing latent structure (graph, tree...) of data is a major challenge to deal with the web of data. Today's embedding techniques incorporate any data source (noisy graphs, item similarities, plain text) into continuous vector spaces that are typically used as input to classifier. In this work, we are dealing with the opposite task: finding structures (taxonomies) from embedded data. We provide an original unsupervised methodology for taxonomy induction by directly searching for graph structures preserving pairwise distances between items. Contrary to the state-of-the-art (SOTA), our approach does not require to train classifiers; it is also more versatile as it can be applied to any embedding (eg. word embedding, similarity embedding like space-time local embedding...). On standard benchmarks and metrics, our approach yields SOTA performance. As another contribution, we propose better evaluation metrics for taxonomy induction, leveraging graph kernel similarities and edit distance, showing that the structures of our predicted taxonomies are significantly closer to the ground-truth than SOTA solutions.

INTRODUCTION

Nowadays, structured knowledge sources, such as knowledge graphs or taxonomies, can be integrated in numerous AI systems. Question answering, information extraction/retrieval are examples of domains where taxonomies are found advantageous, as they enhance the generalization ability of automatic systems [START_REF] Cleuziou | QASSIT: A Pretopological Framework for the Automatic Construction of Lexical Taxonomies from Raw Texts[END_REF][START_REF] Mao | End-to-End Reinforcement Learning for Automatic Taxonomy Induction[END_REF][START_REF] Wang | A Short Survey on Taxonomy Learning from Text Corpora: Issues, Resources and Recent Advances[END_REF]]. Yet, taxonomies are often designed by human experts which implies time and financial costs. Moreover, those handcrafted taxonomies are configured with regard to a topic which may not match every usage. Others even claim that structured knowledge inferred from Gargantuan online sources such as WikiData are not suited for specific domain [START_REF] Wang | A Short Survey on Taxonomy Learning from Text Corpora: Issues, Resources and Recent Advances[END_REF].

An early idea to address these issues is to automatically induce taxonomies from text sources [START_REF] Hearst | Automatic Acquisition of Hyponyms from Large Text Corpora[END_REF]. Indeed, automatic knowledge discovery ultimately leads to a tailored taxonomy closing the gap between general taxonomies and specific applications. Provided taxonomical terms and a sufficiently large text corpus covering those, the process consists in collecting morphological, syntactic, and semantic features to classify or extract is-a relations between these terms [START_REF] Aly | Every Child Should Have Parents: A Taxonomy Refinement Algorithm Based on Hyperbolic Term Embeddings[END_REF][START_REF] Cleuziou | QASSIT: A Pretopological Framework for the Automatic Construction of Lexical Taxonomies from Raw Texts[END_REF][START_REF] Mao | End-to-End Reinforcement Learning for Automatic Taxonomy Induction[END_REF][START_REF] Panchenko | TAXI at SemEval-2016 Task 13: a Taxonomy Induction Method based on Lexico-Syntactic Patterns, Substrings and Focused Crawling[END_REF]. This taxonomy is then employed in realworld applications (eg. e-commerce, biomedecine) [11, ch. 3, for examples], which help at judging its quantitative quality [START_REF] Wang | A Short Survey on Taxonomy Learning from Text Corpora: Issues, Resources and Recent Advances[END_REF].

Generally, collected features are used to produce continuous or handcrafted term embeddings used in classifiers or with heuristics helping at predicting the taxonomy [START_REF] Aly | Every Child Should Have Parents: A Taxonomy Refinement Algorithm Based on Hyperbolic Term Embeddings[END_REF][START_REF] Mao | End-to-End Reinforcement Learning for Automatic Taxonomy Induction[END_REF][START_REF] Panchenko | TAXI at SemEval-2016 Task 13: a Taxonomy Induction Method based on Lexico-Syntactic Patterns, Substrings and Focused Crawling[END_REF]. For instance, noisy is-a relations are projected into a hyperbolic space in order to remove noise and obtain cleaner features [START_REF] Aly | Every Child Should Have Parents: A Taxonomy Refinement Algorithm Based on Hyperbolic Term Embeddings[END_REF]. In our opinion, these additional supervised steps seem disposable, as some intermediate embedding proposed in the literature perform well at hypernym detection [START_REF] Le | Inferring Concept Hierarchies from Text Corpora via Hyperbolic Embeddings[END_REF]. Provided a mapping from embedding to taxonomies, preserving pairwise distances between term vectors, the mapped taxonomy may also preserve the geometrically embedded structural information. Today, there are methods to convert trees or graphs to continuous hyperbolic embeddings with minimal information loss [START_REF] Nickel | Poincaré Embeddings for Learning Hierarchical Representations[END_REF][START_REF] Sarkar | Low Distortion Delaunay Embedding of Trees in Hyperbolic Plane[END_REF]. Here, we propose solutions doing the exact opposite for taxonomy induction: converting continuous embedding spaces to discrete trees. In other words, we propose to address taxonomy induction with a different point of view: instead of using embeddings as filters or as input to classifiers [START_REF] Aly | Every Child Should Have Parents: A Taxonomy Refinement Algorithm Based on Hyperbolic Term Embeddings[END_REF], we intend to directly extract the latent structural information from them. We build a taxonomy that tries to preserve as much as possible the information contained in the distance between embedded terms.

The contributions of this paper can be summarized as follows:

(1) we provide a new unsupervised methodology for the taxonomy induction problem relying on the distortion between metric spaces. This methodology is flexible and is applicable to any kind of data sources (plain text, graph, similarities). [START_REF] Aly | Every Child Should Have Parents: A Taxonomy Refinement Algorithm Based on Hyperbolic Term Embeddings[END_REF] we propose a new feature mixing morphological and syntactic features, used to build term embeddings. (3) we explore three different solutions for our tree extraction problem on various distances-Euclidean and hyperbolic. (4) we propose a new evaluation protocol to quantitatively evaluate how close the induced taxonomies are from the groundtruth. In this respect, we experimentally show that our approach outperforms existing methods.

The paper is organized as follows: Sec. (II) gives an overview of the state-of-the-art (SOTA) and basic notions for taxonomy induction. Sec. (III) describes our whole approach. Experimental results are gathered in Sec. (IV) before conclusion in Sec. (V).

RELATED WORK

A taxonomy T = (𝑉 , 𝐸) is an acyclic directed connected graph denoted by a set of nodes 𝑉 and an edge set 𝐸. Nodes are labeled by terms which are word phrases representing a concept of the taxonomy. For instance, «computer science» may be chosen as a node label to depict the computer science field composed by sciences such as «software engineering», «robotics» or «artificial intelligence».

In the following, we refer to nodes as terms.

The edge set 𝐸 is viewed as a set of is-a relations, i.e. if a term 𝑥 ∈ 𝑉 is-a term 𝑦 ∈ 𝑉 , then there is a directed edge from 𝑥 to 𝑦 in 𝐸. In the following, we denote this relation by (𝑥, is-a, 𝑦) as in [START_REF] Le | Inferring Concept Hierarchies from Text Corpora via Hyperbolic Embeddings[END_REF]. The set of is-a relations is defined by the taxonomy and illustrate that the characteristics or concepts of 𝑥 are contained in those of 𝑦 (eg. «artificial intelligence» is a field of «computer science»).

Overview

Taxonomy induction is a problem where an edge set of is-a relations must be found for a given set of terms. A text corpus may be used as a source to compute features for each term [START_REF] Aly | Every Child Should Have Parents: A Taxonomy Refinement Algorithm Based on Hyperbolic Term Embeddings[END_REF][START_REF] Bordea | Semeval-2016 task 13: Taxonomy Extraction Evaluation (TExEval-2)[END_REF]. Several shared tasks were proposed following this scheme, the most recent one being SemEval-2016 Task 13 [START_REF] Bordea | Semeval-2016 task 13: Taxonomy Extraction Evaluation (TExEval-2)[END_REF]. Among the participating systems, TAXI [START_REF] Panchenko | TAXI at SemEval-2016 Task 13: a Taxonomy Induction Method based on Lexico-Syntactic Patterns, Substrings and Focused Crawling[END_REF] obtained the best results by exploiting lexico-syntactic patterns on a general (Wikipedia) and a domain-specific corpus. Other systems participated: USAAR [START_REF] Tan | USAAR at SemEval-2016 Task 13: Hyponym Endocentricity[END_REF] leverages hyponym endocentricity (see next sub-section); JUNLP [START_REF] Maitra | JUNLP at SemEval-2016 Task 13: A Language Independent Approach for Hypernym Identification[END_REF] relies on BabelNet (https://babelnet.org/) and morphological rules.

In the early approaches, the set of relations is constructed leveraging morphological features of the term (seen as a character strings) and syntactic features (their co-occurrences in the plain text corpus following syntactic patterns, known as Hearst patterns) [START_REF] Hearst | Automatic Acquisition of Hyponyms from Large Text Corpora[END_REF][START_REF] Panchenko | TAXI at SemEval-2016 Task 13: a Taxonomy Induction Method based on Lexico-Syntactic Patterns, Substrings and Focused Crawling[END_REF][START_REF] Tan | USAAR at SemEval-2016 Task 13: Hyponym Endocentricity[END_REF][START_REF] Wang | A Short Survey on Taxonomy Learning from Text Corpora: Issues, Resources and Recent Advances[END_REF]. Extracted relations are then further filtered or pruned using hard-coded rules (USSAR, JUNLP, SubSeq [START_REF] Gupta | Taxonomy Induction Using Hypernym Subsequences[END_REF]) and/or a supervised system (TAXI). Distributional features (such as word embedding) may be used to help with the inference of the taxonomy, but they are not fully exploited [START_REF] Bordea | Semeval-2016 task 13: Taxonomy Extraction Evaluation (TExEval-2)[END_REF][START_REF] Wang | A Short Survey on Taxonomy Learning from Text Corpora: Issues, Resources and Recent Advances[END_REF]. Indeed, their usage is limited to the refinement of taxonomy constructed by TAXI [START_REF] Aly | Every Child Should Have Parents: A Taxonomy Refinement Algorithm Based on Hyperbolic Term Embeddings[END_REF], or as input of supervised systems [START_REF] Mao | End-to-End Reinforcement Learning for Automatic Taxonomy Induction[END_REF]. Recently, a proposition (CTP) was made to fine-tune contextualised embedding for taxonomy induction [START_REF] Chen | Constructing Taxonomies from Pretrained Language Models[END_REF]. It explores the use of syntactic patterns in pre-trained contextualised language models. While it shows an improvement for induction of small taxonomies (10-50 terms), it did not improve induction of medium to large scale taxonomies (100-1000 terms), which is our main interest here.

Other systems avoid pruning as it appears to be an error-prone process. TaxoRL [START_REF] Mao | End-to-End Reinforcement Learning for Automatic Taxonomy Induction[END_REF] is an end-to-end supervised system relying on reinforcement learning to construct a taxonomy from scratch. As it is supervised, it requires real-world taxonomy to be trained on: sub-taxonomies of WordNet [START_REF] Miller | WordNet: A Lexical Database for English[END_REF] were used in that purpose. Graph2Taxo [START_REF] Shang | Taxonomy Construction of Unseen Domains via Graph-based Cross-Domain Knowledge Transfer[END_REF] is another end-to-end approach which predicts the adjacency matrix of the taxonomy, but also introduces direct cross-domain knowledge integration. In other words, it used a priori known taxonomy to create features to help with the prediction of edges for unknown taxonomies.

The major issue with SOTA techniques is the supervision. TAXI, TaxoRL, Graph2Taxo, CTP require external structured data to train their models or heavy heuristics to work. Instead, we propose an unsupervised taxonomy induction system. Our approach is inspired by HyperCones [START_REF] Le | Inferring Concept Hierarchies from Text Corpora via Hyperbolic Embeddings[END_REF], an unsupervised hyperbolic term embedding based on the co-occurrence counts of Hearst patterns. They leverage hyperbolic geometry which is shown more effective than Euclidean geometry at representing and recovering the hierarchical structure of tree structures [START_REF] Ganea | Hyperbolic Entailment Cones for Learning Hierarchical Embeddings[END_REF][START_REF] Nickel | Poincaré Embeddings for Learning Hierarchical Representations[END_REF]. We push these ideas further to produce complete taxonomies from term embeddings instead of the simpler task of hypernym detection proposed by [START_REF] Le | Inferring Concept Hierarchies from Text Corpora via Hyperbolic Embeddings[END_REF]. Indeed, hypernym detection does not have to deal with the directed acyclic and connected nature of taxonomies. We also propose to construct and embed a graph that leverages both morphological and syntactic features, while they mainly focused on the syntactic ones.

In this work, we compare with TAXI [START_REF] Panchenko | TAXI at SemEval-2016 Task 13: a Taxonomy Induction Method based on Lexico-Syntactic Patterns, Substrings and Focused Crawling[END_REF], USAAR [START_REF] Tan | USAAR at SemEval-2016 Task 13: Hyponym Endocentricity[END_REF], JUNLP [START_REF] Maitra | JUNLP at SemEval-2016 Task 13: A Language Independent Approach for Hypernym Identification[END_REF] and TAXI with refinement presented in [START_REF] Aly | Every Child Should Have Parents: A Taxonomy Refinement Algorithm Based on Hyperbolic Term Embeddings[END_REF], an improved version of TAXI. Unfortunately, we will not compare with SubSeq for reproducibility issues. As we do not use cross-domain input or supervision with external taxonomies, other systems (like Graph2Taxo, TaxoRL and CTP) are not considered. To the best of our knowledge, TAXI with refinements is the best taxonomy induction system that is not using cross-domain information for the SemEval-2016 Task 13. We provide, in the following, further details on related studies and important notions helping to understand how we completed this task.

Feature Engineering

Feature extraction from plain text or term strings is an important step in taxonomy induction since classifiers used in TAXI [START_REF] Panchenko | TAXI at SemEval-2016 Task 13: a Taxonomy Induction Method based on Lexico-Syntactic Patterns, Substrings and Focused Crawling[END_REF] or more complex models such as TaxoRL or Graph2Taxo [START_REF] Mao | End-to-End Reinforcement Learning for Automatic Taxonomy Induction[END_REF][START_REF] Shang | Taxonomy Construction of Unseen Domains via Graph-based Cross-Domain Knowledge Transfer[END_REF] rely on them to produce taxonomies. The quality of the resulting taxonomies highly depends on those features.

Morphological features.

They simply compare term strings while being particularly accurate. This is why they are widely exploited [START_REF] Aly | Every Child Should Have Parents: A Taxonomy Refinement Algorithm Based on Hyperbolic Term Embeddings[END_REF][START_REF] Mao | End-to-End Reinforcement Learning for Automatic Taxonomy Induction[END_REF][START_REF] Panchenko | TAXI at SemEval-2016 Task 13: a Taxonomy Induction Method based on Lexico-Syntactic Patterns, Substrings and Focused Crawling[END_REF][START_REF] Tan | USAAR at SemEval-2016 Task 13: Hyponym Endocentricity[END_REF]. Indeed, the appearance of one term in another, known as the hyponym endocentricity [START_REF] Tan | USAAR at SemEval-2016 Task 13: Hyponym Endocentricity[END_REF], often indicates a is-a relation (eg. engineering and electrical engineering). Generally, a set of morphological features are generated by pointing out whether prefix or suffix matches or the length of the longest common substring [START_REF] Bansal | Structured Learning for Taxonomy Induction with Belief Propagation[END_REF]. Although, morphological features are very precise, they lack recall since they cannot detect is-a relations for pairs without common morphological components (eg. algebra and mathematics).

Syntactic features.

They are produced with syntactic patterns, or Hearst patterns [START_REF] Hearst | Automatic Acquisition of Hyponyms from Large Text Corpora[END_REF], which are applied on the corpus to harvest specific co-occurrences between terms of the taxonomy. A set of patterns is pre-defined, such as "X is a Y" or "X such as Y", and one counts the number of times patterns are triggered for a term pair X, Y. The Hearst graph is a formal representation of these frequency counts. In other words, nodes of the graph are taxonomical terms and an edge linking a term X to another term Y is weighted by the amount of times X and Y matched patterns in the corpus. This approach is widely used [START_REF] Aly | Every Child Should Have Parents: A Taxonomy Refinement Algorithm Based on Hyperbolic Term Embeddings[END_REF][START_REF] Le | Inferring Concept Hierarchies from Text Corpora via Hyperbolic Embeddings[END_REF][START_REF] Mao | End-to-End Reinforcement Learning for Automatic Taxonomy Induction[END_REF][START_REF] Panchenko | TAXI at SemEval-2016 Task 13: a Taxonomy Induction Method based on Lexico-Syntactic Patterns, Substrings and Focused Crawling[END_REF][START_REF] Roller | Hearst Patterns Revisited: Automatic Hypernym Detection from Large Text Corpora[END_REF] because it is simple, explainable, scalable. WebIsA [START_REF] Seitner | A Large DataBase of Hypernymy Relations Extracted from the Web[END_REF] is a free online available database gathering such frequency counts crawled on very large web corpora, used in this work and in [START_REF] Aly | Every Child Should Have Parents: A Taxonomy Refinement Algorithm Based on Hyperbolic Term Embeddings[END_REF][START_REF] Panchenko | TAXI at SemEval-2016 Task 13: a Taxonomy Induction Method based on Lexico-Syntactic Patterns, Substrings and Focused Crawling[END_REF] to enrich term features.

Distributional features.

They rely on vectors from word embeddings (eg. word2vec) or distributional approaches to compute inclusion or semantic proximity of terms. They are finally added either before classification or modeling [START_REF] Mao | End-to-End Reinforcement Learning for Automatic Taxonomy Induction[END_REF] or after in order to detect co-hyponyms [START_REF] Aly | Every Child Should Have Parents: A Taxonomy Refinement Algorithm Based on Hyperbolic Term Embeddings[END_REF] and help to predict the taxonomy. Distributional features may also be used to detect is-a relations using the Distributional Inclusion Hypothesis claiming that contexts of a term are included in the contexts of its hypernyms [START_REF] Wang | A Short Survey on Taxonomy Learning from Text Corpora: Issues, Resources and Recent Advances[END_REF][START_REF] Weeds | Characterising Measures of Lexical Distributional Similarity[END_REF].

Hyperbolic Term Embeddings

Recently, [START_REF] Aly | Every Child Should Have Parents: A Taxonomy Refinement Algorithm Based on Hyperbolic Term Embeddings[END_REF][START_REF] Le | Inferring Concept Hierarchies from Text Corpora via Hyperbolic Embeddings[END_REF] proposed to embed the Hearst graph into a hyperbolic space, following the hyperbolic graph embedding method of [START_REF] Nickel | Poincaré Embeddings for Learning Hierarchical Representations[END_REF]. By projecting the graph into a continuous hyperbolic space where the geometry is more suited for taxonomical inference, [START_REF] Aly | Every Child Should Have Parents: A Taxonomy Refinement Algorithm Based on Hyperbolic Term Embeddings[END_REF][START_REF] Le | Inferring Concept Hierarchies from Text Corpora via Hyperbolic Embeddings[END_REF] showed that it solved some of the problem caused by the noise and sparsity of the Hearst graph (patterns are sometimes over-sensitive which might creates cycles or wrong is-a relations, or too restrictive which hides true ones). Those relations are condensed and term embedding are interpreted into new relations, using heuristics or specific tuning on the hyperbolic distance.

In [START_REF] Aly | Every Child Should Have Parents: A Taxonomy Refinement Algorithm Based on Hyperbolic Term Embeddings[END_REF], hyperbolic embeddings are used to refine the taxonomies predicted with traditional methods explained above. Thus, it adds an additional post-processing on the top of an already highly layered solution, increasing error propagation and reducing the ability to benefit from low layer improvements. In our work, we propose to process these embeddings directly to search for the taxonomical graph that preserves embedding term distances. Also, contrary to [START_REF] Le | Inferring Concept Hierarchies from Text Corpora via Hyperbolic Embeddings[END_REF], we aim at building an entire taxonomy on a specific topic while they only focused on hypernymy detection.

Distortion

The distortion indicates how well a mapping from a metric space (e.g. an embedding) to another (e.g. a graph / tree) preserves pairwise distances (see [START_REF] Nayyeri | Viewing the Rings of a Tree: Minimum Distortion Embeddings into Trees[END_REF][START_REF] Sarkar | Low Distortion Delaunay Embedding of Trees in Hyperbolic Plane[END_REF]). It is mathematically defined by:

𝑑 𝑓 = max (𝑥,𝑥 ′ ) ∈𝑋 2 𝑥 ≠𝑥 ′ 𝑑 𝑌 (𝑓 (𝑥), 𝑓 (𝑥 ′ )) 𝑑 𝑋 (𝑥, 𝑥 ′ ) • max (𝑦,𝑦 ′ ) ∈𝑓 (𝑋 ) 2 𝑦≠𝑦 ′ 𝑑 𝑋 (𝑓 -1 (𝑦), 𝑓 -1 (𝑦 ′ )) 𝑑 𝑌 (𝑦, 𝑦 ′ ) (1)
where 𝑓 is a mapping from one metric space (𝑋, 𝑑 𝑋 ) to another (𝑌, 𝑑 𝑌 ); in this work, from a graph metric to an embedding space.

[26] used it to prove that their method is able to embed a tree in a hyperbolic space while preserving the distances, thus the information contained in the tree. In this case, the distance in a tree 𝑑 𝑇 is the sum of the edge weights of the path between nodes. Taxonomy induction can be seen as the opposite problem where one is given an embedding of terms-constructed as described in the previous sub-section-and need to recover a tree from it seen as the taxonomy skeleton: this is exactly what we aim to accomplish. Put differently, we need a method for extracting a tree of terms from term embeddings having the lowest distortion.

An embedding can be considered as a fully connected graph where nodes are embedded elements and edges are weighted by a distance function. If the graph is equipped with the standard graph distance 𝑑 𝐺 -defined by the path with the smallest weight sum between two nodes-then the graph and 𝑑 𝐺 form a finite metric space. Then, our problem is known in the network literature as finding tree spanner with lowest expansion / distortion [START_REF] Ahmed | Graph spanners: A tutorial review[END_REF]. The lowest expansion tree-spanner is named the Minimum Max-stretch Spanning Tree (MMST).

When distances in the tree perfectly preserve distances in the embedding (such a tree does not always exist), then the MMST matches the Minimum Spanning Tree (MST) [START_REF] Ahmed | Graph spanners: A tutorial review[END_REF][START_REF] Nayyeri | Viewing the Rings of a Tree: Minimum Distortion Embeddings into Trees[END_REF]. In general, the distortion is not perfect and we must have a strategy to construct the MMST. This problem is known to be NP-hard [START_REF] Ahmed | Graph spanners: A tutorial review[END_REF]. Due to the complexity of the problem, we investigate a naive solution (identical to [START_REF] Monath | Gradient-based Hierarchical Clustering Using Continuous Representations of Trees in Hyperbolic Space[END_REF]) or approximate solutions produced by some algorithms presented in [START_REF] Dhamdhere | Improved Embeddings of Graph Metrics into Random Trees[END_REF].

METHODOLOGY 3.1 Combination of morphological and syntactic features

3.1.1 Overview. Similarly to [START_REF] Le | Inferring Concept Hierarchies from Text Corpora via Hyperbolic Embeddings[END_REF][START_REF] Panchenko | TAXI at SemEval-2016 Task 13: a Taxonomy Induction Method based on Lexico-Syntactic Patterns, Substrings and Focused Crawling[END_REF][START_REF] Roller | Hearst Patterns Revisited: Automatic Hypernym Detection from Large Text Corpora[END_REF], we use pattern based features (which are turned into a mutual information space) and morphological features. We propose to also turn morphological features into a mutual information space, and to sum pattern-based and morphological mutual information scores. The combination of this morphological and syntactic information leads to a new feature 𝑓 𝑎𝑔𝑔 , contribution [START_REF] Aly | Every Child Should Have Parents: A Taxonomy Refinement Algorithm Based on Hyperbolic Term Embeddings[END_REF]. We detail each element hereafter. In the end, we are able to quantify the strength of is-a relations between terms represented as a weighted directed graph (weights being 𝑓 𝑎𝑔𝑔 ). We expect this graph to be less ambiguous than the Hearst Graph which is usually employed [START_REF] Aly | Every Child Should Have Parents: A Taxonomy Refinement Algorithm Based on Hyperbolic Term Embeddings[END_REF][START_REF] Le | Inferring Concept Hierarchies from Text Corpora via Hyperbolic Embeddings[END_REF].

Corpora.

For comparison purposes, we use the same corpus and counts of is-a patterns provided by [START_REF] Aly | Every Child Should Have Parents: A Taxonomy Refinement Algorithm Based on Hyperbolic Term Embeddings[END_REF] 1 . Following previous work [START_REF] Aly | Every Child Should Have Parents: A Taxonomy Refinement Algorithm Based on Hyperbolic Term Embeddings[END_REF][START_REF] Mao | End-to-End Reinforcement Learning for Automatic Taxonomy Induction[END_REF][START_REF] Panchenko | TAXI at SemEval-2016 Task 13: a Taxonomy Induction Method based on Lexico-Syntactic Patterns, Substrings and Focused Crawling[END_REF], this corpus consists of texts from two different sources: general and specific. The general domain corpus is composed of texts coming from huge online resources such as Wikipedia.

The specific corpus gathers texts being correlated with the topic of the taxonomy, collected by querying a Web search engine with random combinations of taxonomical terms.

3.1.3 Syntactic feature. The corpora are processed leveraging syntactic patterns to extract co-occurrences between terms with tools such as PattaMaika2 . Also, frequencies from WebIsA [START_REF] Seitner | A Large DataBase of Hypernymy Relations Extracted from the Web[END_REF] are collected to enrich our feature collection. Finally, three kinds of cooccurrences are obtained: general, specific and WebIsA ones. They are each independently turned into a Positive Pointwise Mutual Information (PPMI) space, following [START_REF] Roller | Hearst Patterns Revisited: Automatic Hypernym Detection from Large Text Corpora[END_REF].

3.1.4 morphological feature. The morphological feature 𝜎 (𝑡 𝑖 , 𝑡 𝑗 ) between two terms 𝑥 and 𝑦 is an hypernymy substring based score [START_REF] Panchenko | TAXI at SemEval-2016 Task 13: a Taxonomy Induction Method based on Lexico-Syntactic Patterns, Substrings and Focused Crawling[END_REF] formally defined by 𝜎 (𝑥, 𝑦) = length(𝑥) length(𝑦) if 𝑥 inside 𝑦 and 0 otherwise. Before being combined, the hypernymy substring based score is transformed as a PPMI score as above.

3.1.5 Aggregated feature. We introduce a new aggregating feature 𝑓 agg which is given by assembling for each term pair (𝑥, 𝑦) patterns and morphological features as follows:

𝑓 agg (𝑥, 𝑦) = ppmi 𝜎 (𝑥, 𝑦) + 1 3 𝑑= General Specific WebIsA ppmi 𝑑 (𝑥, 𝑦) (2)
This feature weights the strength of the relation (𝑥, is-a, 𝑦) in the three corpora while also taking into account their substring scores.

The SOTA methods commonly employ a classifier to combine morphological and syntactic features, or exclusively use the syntactic ones. We advocate for 𝑓 𝑎𝑔𝑔 which fuses early both features and, thus, integrates all information into the graph prior to the term embedding step.

To justify this new feature, we show its effectiveness at ranking term neighbors, w.r.t. its components, in Table 1. The data used is a part of the Semeval 2016 Task 13 [START_REF] Bordea | Semeval-2016 task 13: Taxonomy Extraction Evaluation (TExEval-2)[END_REF] dataset (see Sec. 4.3). The 𝑓 agg improvement over any other feature is statistically significant (one-sided t-test with p<0.01). One can remark that taking individual component features of 𝑓 agg is less efficient at organizing neighborhood, which advocates for the combination of all features as we propose.

We create a matrix using this feature, which is smoothed by removing some of its singular values as done in [START_REF] Roller | Hearst Patterns Revisited: Automatic Hypernym Detection from Large Text Corpora[END_REF], and values under the mean value. The underlying matrix graph is further pruned such that each term have at most 5 direct neighbors. This value was chosen arbitrarily and further investigation must be carried to observe its impact on the final performance. This graph is then embedded into a Euclidean or a hyperbolic space, following Sec. 3.2.

Term embeddings

For convenience, the custom graph introduced in the previous section is denoted 𝐺 = (𝑉 , 𝑀), 𝑉 being the terms and 𝑀 being the smoothed asymmetric matrix of the weights between each terms. We also note 𝑀 (𝑥, 𝑦) for (𝑥, 𝑦) ∈ 𝑉 2 , the weights for the relation (𝑥, is-a, 𝑦). We now thoroughly explain how it can be embedded into a Euclidean or hyperbolic space [START_REF] Ganea | Hyperbolic Entailment Cones for Learning Hierarchical Embeddings[END_REF][START_REF] Nickel | Poincaré Embeddings for Learning Hierarchical Representations[END_REF]. 𝑛, denoted by Θ = (𝜃 𝑥 ) 𝑥 ∈𝑉 ∈ R 𝑛×|𝑉 | . We perform optimization using three distances having specific characteristics that alter the optimization procedure:

1) The Euclidean distance 𝑑 E . It consists of the well known L2norm, written for two terms (𝑥, 𝑦) ∈ 𝑉 2 , 𝑑 E (𝑥, 𝑦) = ∥𝜃 𝑥 -𝜃 𝑦 ∥ 2 .

2) The Poincaré distance 𝑑 P [START_REF] Nickel | Poincaré Embeddings for Learning Hierarchical Representations[END_REF]. It is defined for terms (𝑥, 𝑦) ∈ 𝑉 2 on a unit sphere D 𝑛 = {𝑎|𝑎 ∈ R 𝑛 , ∥𝑎∥ 2 < 1} as:

𝑑 P (𝑥, 𝑦) = arcosh 1 + 2 ∥𝜃 𝑥 -𝜃 𝑦 ∥ 2 2 (1 -∥𝜃 𝑥 ∥ 2 2 )(1 -∥𝜃 𝑦 ∥ 2 2 ) (3)
The parameters Θ must be initialized and optimized in D 𝑛 . This distance is expected to encode more easily hierarchies compared with the Euclidean distance.

3) The squared Lorentzian pseudo-distance 𝑑 L [START_REF] Law | Lorentzian Distance Learning for Hyperbolic Representations[END_REF]. It is defined on the hyperboloid H 𝑛,𝛽 = {𝑎 = (𝑎 0 , ..., 𝑎 𝑛 ) |𝑎 ∈ R 𝑛+1 , ∥𝑎∥ 2 L = -𝛽}, where 𝛽 ∈ R is a hyperparameter. It is based on the squared Lorentzian inner product ⟨𝑎, 𝑏⟩ L = -𝑎 0 𝑏 0 + 𝑛 𝑖=1 𝑎 𝑖 𝑏 𝑖 , and the squared Lorentzian norm ∥𝑎∥ 2 L = ⟨𝑎, 𝑎⟩ L . As we already have an Riemannian optimizer for D 𝑛 , we propose to initialize the parameter Θ in D 𝑛 and then project them to H 𝑛,𝛽 with the projector ℎ -1 from D 𝑛 to H 𝑛,𝛽 [START_REF] Law | Lorentzian Distance Learning for Hyperbolic Representations[END_REF]:

ℎ -1 (𝜃 𝑥 ) = 2𝜃 𝑥 1 -∥𝜃 𝑥 ∥ 2 2 2 2 + 𝛽, 2𝜃 𝑥,1 1 -∥𝜃 𝑥 ∥ 2 2 , ..., 2𝜃 𝑥,1 1 -∥𝜃 𝑥 ∥ 2 2 (4)
with 𝜃 𝑥 = (𝜃 𝑥,1 , ..., 𝜃 𝑥,𝑛 ). The squared Lorentzian pseudodistance between two terms (𝑥, 𝑦) ∈ 𝑉 2 is: 𝑑 L (𝑥, 𝑦) = -2𝛽 -2⟨ℎ -1 (𝜃 𝑥 ), ℎ -1 (𝜃 𝑦 )⟩ L . The main advantage of 𝑑 L is its ability to strongly enforce generic elements to remain in the center of D 𝑛 and the specific ones at the border of D 𝑛 . In comparison, the Poincaré distance is less able to do so. This attitude is desirable since it may help at recomposing the taxonomy.

Optimization.

We build the term embeddings w.r.t. the chosen distance 𝑑 such that they incorporate information contained in D 𝑁 . The loss function is:

L (Θ) = (𝑥,𝑦) ∈D 𝑁 log 𝑒 -𝑑 (𝑥,𝑦) 𝑦 ′ ∼N (𝑥)
𝑒 -𝑑 (𝑥,𝑦 ′ )

;

N (𝑥) = {𝑦 ′ |(𝑥, 𝑦 ′ ) ∉ D 𝑁 }
(5) where N ( §) is the set negative edges (terms are not linked to 𝑥 in D 𝑁 ). L (Θ) is minimized with the following setting: for each batch, we sample 10 negative edges and compute the Euclidean gradients of L which are then used to realize a Riemannian optimization as presented in [START_REF] Ganea | Hyperbolic Entailment Cones for Learning Hierarchical Embeddings[END_REF] or [START_REF] Nickel | Poincaré Embeddings for Learning Hierarchical Representations[END_REF]. For both optimizations, we relied on hyperbolic_cones and gensim projects.

Taxonomy extraction

This part of our work (contribution 3) is particularly different from other studies about taxonomy induction as we will search for a taxonomy minimizing information loss with the embedding. Other studies generally feed features and/or embedding into a classifier or a more complex model [START_REF] Aly | Every Child Should Have Parents: A Taxonomy Refinement Algorithm Based on Hyperbolic Term Embeddings[END_REF][START_REF] Mao | End-to-End Reinforcement Learning for Automatic Taxonomy Induction[END_REF][START_REF] Panchenko | TAXI at SemEval-2016 Task 13: a Taxonomy Induction Method based on Lexico-Syntactic Patterns, Substrings and Focused Crawling[END_REF].

Minimizing the distortion gives an undirected graph, so the direction of the edges is recovered by rooting those at the centroid of the embedding leading to acyclic connected directed graph interpreted as taxonomies. For instance, if we have undirected edges 𝐸 = {(bicycle, vehicle), (vehicle, car)}, and if the centroid is vehicle, then the taxonomy is (bicycle, is-a, vehicle) and (car, is-a, vehicle)

The main drawback of approximating taxonomies by rooted trees is that terms are constrained to have a single parent. Yet, the counterpart is that we can use results from graph theory to find rooted trees close to our embeddings. Three different versions of tree extractor are used, other solutions-aiming at minimizing the distortion-are possible and could be investigated in future work.

Hereafter, we present the three different tree extractors we propose in this work. The tree extractor is applied on the embedding produced in the previous step (see Sec. 3.2) with D 𝑁 obtained by our aggregated feature 𝑓 agg . At the end of each extraction process, the centroid is considered to be the root of the tree and edges are directed accordingly.

Naive Extraction (NE).

This technique is very similar to [START_REF] Monath | Gradient-based Hierarchical Clustering Using Continuous Representations of Trees in Hyperbolic Space[END_REF] except that we extract an undirected tree which is then rooted at the centroid of the embedding space. The extraction relies on the simple idea that for hyperbolic embeddings, the hyperbolic distance encodes the strength of is-a relations and the direction of those can be determined by the Euclidean norm ∥.∥. That is, for an embedding Θ with distance 𝑑 and a term vector 𝑥, 𝑦 * is considered to be related to 𝑥 and the edge (𝑥, 𝑦 * ) is added to the edge set of the tree if:

𝑦 * = arg min 𝑦 ∈𝑉 𝑑 (𝑦, 𝑥) with ∥𝜃 𝑦 ∥ 2 ≤ ∥𝜃 𝑥 ∥ 2
One must notice that edges defined as such always provide a tree since the definition avoid cycles and multiple components.

MST Extraction (MSTE).

As mentioned above, the MST corresponds to the tree that perfectly preserves pairwise distances of embedded element when it does exist. This is why we propose to use the MST because in some cases it can be a good spanning tree for the embedding.

Low Average Stretch Extraction (LASE)

. This stochastic algorithm was initially proposed by [START_REF] Dhamdhere | Improved Embeddings of Graph Metrics into Random Trees[END_REF]. We slightly adapted it to support weighted graphs. This algorithm recursively decomposes the embedding space using spheres with specific radius to preserve the distances from the metric space to the tree. It requires a parameter controlling the scale of radius which was chosen according to experiment on the WordNet mammal.n.01 sub-taxonomy.

Taxonomy Refinement

We also investigated taxonomy refinement as proposed in [START_REF] Aly | Every Child Should Have Parents: A Taxonomy Refinement Algorithm Based on Hyperbolic Term Embeddings[END_REF] which consists in the addition or deletion of aberrant edges with regard to external features. With the same idea, we only consider edge addition for our extracted taxonomy with pre-trained word embeddings from Spacy for each language [START_REF] Honnibal | spaCy: Industrial-strength Natural Language Processing in Python[END_REF]. Those preserving the taxonomy structure (no cycle) are added to the tree. Only disconnected terms are reconnected with this procedure.

EXPERIMENTS

We present here two series of experiments: the first assesses the extraction performance on embeddings issued from real taxonomies (WordNet [START_REF] Miller | WordNet: A Lexical Database for English[END_REF]), the second evaluates our complete approach for

Evaluation protocol

SOTA benchmarks principally rely on Directed F1-score. Here, we propose a more elaborated evaluation protocol which provides measures on the inner sub-structures of taxonomies (contribution 4), highlighting the similarity between taxonomy geometries. Our implementation relies on the libraries GMatch4py [START_REF] Fize | GMatch4py[END_REF] and aprox-imated_ged [START_REF] Riba | Aproximated Graph Edit Distance[END_REF]. Hereunder, we detail the evaluation scores for comparing a ground-truth taxonomy (with edges 𝐸 * ) and a predicted taxonomy (edges 𝐸 𝑝 ). Transitive F1-score is an extension of the directed score (found in [START_REF] Ganea | Hyperbolic Entailment Cones for Learning Hierarchical Embeddings[END_REF]) which measures the concordance between ancestors by connecting every node to each of its ancestors. Specifically, from an edge set 𝐸, we create a new set 𝑇 (𝐸) = {(𝑦, 𝑥)|𝑦 is an ancestor of 𝑥 in 𝐸}. Then we compute the F1-score as previously except that we replace edge sets with 𝑇 (𝐸 * ) \ 𝐸 * and 𝑇 (𝐸 𝑝 ) \ 𝐸 𝑝 . Weisfeiler Lehman Kernel score (WL Kernel) [START_REF] Shervashidze | Weisfeiler-Lehman Graph Kernels[END_REF] is a kernel based graph comparison score with kernel focusing on subtree-like pattern. It thus quantifies the similarity between subtrees from the two graphs. Shortest Path Kernel score (SP Kernel) is also a kernel based graph comparison score where kernels contain shortest path information between nodes. Hausdorff Edit Distance (HED) [START_REF] Fischer | Approximation of graph edit distance based on Hausdorff matching[END_REF] is an approximate edit distance for graphs like the Levenshtein distance for strings [START_REF] Fischer | Approximation of graph edit distance based on Hausdorff matching[END_REF] by counting the number of nodes addition or deletion and edges addition or deletion needed to turn a graph to another one.

In order to emphasize the relevance of kernel-based metrics compared to Directed F1-score, we compare the behavior of both scores on artificially degraded taxonomies. Let T 𝑛 ℎ be the set of perfect 𝑛-ary trees with height ℎ. We denote by T 𝑛 ℎ,𝑘 the set of trees constructed as follows:

(1) Let 𝑡 ∈ T 𝑛 ℎ . (2) Repeat 𝑘 times (the number of corruptions):

• Randomly select a node 𝑥 from 𝑡. If 𝑦 = parent(𝑥) is higher up in 𝑡, then proceed, else skip. • Remove edges from 𝑥 to any of its children.

• Connect these new orphan nodes to 𝑦. This process voluntarily introduces errors in trees from T 𝑛 ℎ that are very similar to real world mistakes for taxonomy induction (mismatch hypernyms with co-hyponyms). On Fig. 1

Inferring WordNet Taxonomies

Before dealing with real taxonomy induction, we first evaluate our methodology on distinct WordNet [START_REF] Miller | WordNet: A Lexical Database for English[END_REF] sub-taxonomies. Instead of using noisy directed edges, we directly embed its ground-truth edges. We work on sub-taxonomies because LASE does not scale on the whole WordNet taxonomy. Spearman's correlation coefficients between the Direct F1-score and the distortion for the distances 𝑑 E , 𝑑 P , 𝑑 L are respectively: -0.79 (𝑝 < 0.01), -0.89 (𝑝 < 0.01), 0.24 (𝑝 < 0.17). It shows that, for 𝑑 P and 𝑑 E , high Direct F1-score are obtained when distortion is low, confirming our approach which tries to find low distortion tree for embeddings. Concerning 𝑑 L , we do not observe any particular trend maybe due to the fact that it is not a proper distance, thus, the distortion seems less meaningful in this case.

Results for two sub-taxonomies-similar trends are observed for others-are presented in Table 2 (SP Kernel and HED metrics are omitted for computational reasons). For each pair of extractor / distance, we selected the dimension corresponding to the best Directed F1-score on the sub-taxonomy communication.n.02, which acted as a validation set.

For all experiments, embeddings using 𝑑 E are significantly less effective than those using hyperbolic distances 𝑑 P or 𝑑 L . This is expected since hyperbolic spaces are known to better embed hierarchical relations than Euclidean space. MSTE is the best extractor for Euclidean embedding since NE uses hyperbolic heuristics which are not usable for Euclidean distance and LASE is hard to tune for this distance. The distance 𝑑 L is best-performing for any metrics and extractor except for transitive F1-score on plant.n.02. This is due to our rooting policy which sets the root of the tree at the centroid. When the centroid does not match the true root, then the transitive F1-score is highly affected since errors in high levels of the taxonomy have more impact than errors in low levels. This is why it is important to also look at the WL Kernel score as it is less affected by these errors and reflects structural differences.

Given that, it appears that 𝑑 L is the best to preserve the taxonomy structure with the considered extractors and distances.

Concerning tree extractors, there are noticeable differences. The taxonomy structure (directed F1-score and WL Kernel) is best reconstructed by LASE for 𝑑 L , NE for 𝑑 P and MSTE for 𝑑 E . This crucial result highlights that one must adapt the tree extraction to the embedding space to yield the best reconstruction performance achievable. Overall, the best combination for WordNet reconstruction is LASE and 𝑑 L . However, on the transitive F1-score this combination is less effective than others due to two reasons: the tree is ill-rooted and LASE decomposes the embedding space in several spheres which can potentially break high-level relations.

Taxonomy Induction

We highlight the versatility of our approach by applying it to the taxonomy induction task: SemEval-2016 task 13 [START_REF] Bordea | Semeval-2016 task 13: Taxonomy Extraction Evaluation (TExEval-2)[END_REF]. This task is composed of multilingual taxonomies: 4 languages-English (EN), French (FR), Italian (IT), Dutch (NL)-and 3 domains-Science (Sc.), Food (Fo.), Environment (En.)-produce 12 taxonomies, one for each arrangement. For this problem, we compare with TAXI, USAAR, JUNLP (taxonomies for USAAR and JUNLP are found on the official SemEval'16 page) and TAXI with refinements (TAXI (ref.

)) proposed in [START_REF] Aly | Every Child Should Have Parents: A Taxonomy Refinement Algorithm Based on Hyperbolic Term Embeddings[END_REF] (their source code is used to compute taxonomies with their method). Given their poor results for English, missing taxonomies for JUNLP and USAAR are not recalculated.

It is worth noting that the roots of taxonomies needs to be given to all methods except ours. In comparison to others, we are able to provide a candidate for the root-being the centroid-that is shown to be close to the truth: we reported in the table the depth of the centroid to indicate how far the centroid is from the real root. In most cases, we observe that the centroid matches the real root of the taxonomy (centroid depth = 0). Yet, we decided to root the tree at the true root for a fair comparison between models.

The English taxonomy covering environment is used to tune hyper-parameters: the distance, the tree extractor and the dimension. We found that the best combination is 𝑑 P with NE and 100 dimensions. Results are reported in Table 3. When method is followed by (ref.), it indicates whether the system uses refinements posterior to the taxonomy prediction.

We observe our propositions (refined or not) to be slightly less effective than TAXI (ref.) at predicting directed edges of the ground truth taxonomy. Indeed, our method has a slightly lower recall but is more precise than TAXI (ref.) potentially due to the tree approximation: we prevent nodes from having multiple parents which limit the number of predicted edges. Nevertheless, our propositions are more effective on other metrics. This is significantly observed on WL Kernel and SP Kernel for which our propositions conduct to fairly higher scores. HED results are not significant but show an identical trend. For Transitive F1-score, which highlights the ability at providing stereotypical features, our proposition with refinement gives best results, while TAXI (ref.) and our method without refinements are comparable. Overall, our propositions, with or without refinements, predict taxonomies with a graph structure that is closer to the target taxonomy, without greatly degrading the performance of TAXI (ref.) on directed F1-score. It also gives higher performance than other methods. Two main reasons can explain these differences: (1) the new feature is more effective than previously used features, (2) the graph pruning step of TAXI is brutal, as it uses individual edge classification, while we extract a coherent tree structure from our embedding space in a single step.

CONCLUSION

In this work, we proposed a new approach to extract tree directly from an embedding space. This method reduces the need for supervision in taxonomy induction system. It can be applied to any embedding computed with any kind of data source-similarities, relations, raw text-which can extend taxonomy induction to other data sources. We quantitatively show that it produces taxonomy with a better structure. This methodology was applied to WordNet term embeddings to extract the latent hierarchical representations with effective performance. Our code will be made available for reproducibility purposes at https://github.com/pagesjaunes/dembedder/.

In addition, we extended the commonly used evaluation protocolmainly composed by the directed F1-score-which gives quantitative insights on the inner structure of the taxonomy. We hope that this new benchmark will provide a more detailed comparison between forthcoming methods.

In the future, we propose to search for other tree extractor algorithms, as well as graph extractors (removing the tree constraint of our proposition). It may involve neural networks similar to [START_REF] Shang | Taxonomy Construction of Unseen Domains via Graph-based Cross-Domain Knowledge Transfer[END_REF] or may require to jointly optimize the tree/graph structure with the embedding, adapting the proposition of [START_REF] Monath | Gradient-based Hierarchical Clustering Using Continuous Representations of Trees in Hyperbolic Space[END_REF] to any extractor. Ideally, our method can be applied to any embedding space; it would be interesting to investigate which type of taxonomies it can produce with embeddings learned with different data sources than graph, or noisy graph and different data types.

3. 2 . 1

 21 Embedding parameters. Let D 𝑁 = {(𝑥, 𝑦)|𝑀 (𝑥, 𝑦) > 0, (𝑥, 𝑦) ∈ 𝑉 2 } be the set of noisy directed edges estimated by taking the term pairs corresponding to the positive values of the matrix 𝑀. We parameterize |𝑉 | term embedding vectors with dimension

Figure 1 :

 1 Figure 1: Directed F1-score and WL Kernel explained by the number of corruptions 𝑘.

  Directed F1-score is the standard combination of the precision 𝑝 = |𝐸 * ∩𝐸 𝑝 | |𝐸 𝑝 | and the recall 𝑟 = |𝐸 * ∩𝐸 𝑝 | |𝐸 * | , which assesses the quality of predicted edges.

Table 1 :

 1 MAP of neighborhood produced by each feature on the direct edges of the EN environment taxonomy of Semeval 2016 Task 13[START_REF] Bordea | Semeval-2016 task 13: Taxonomy Extraction Evaluation (TExEval-2)[END_REF].

	Features	MAP
	ppmi Specific	0.46
	ppmi General	0.50
	ppmi WebIsA	0.37
	ppmi 𝜎	0.51
	avg(ppmi Specific , ppmi General , ppmi WebIsA ) 0.59
	𝑓 agg	0.71

  is still decent whereas WL Kernel value is fairly lower. With so many corruptions, the inner subtree structures are very damaged w.r.t. the original tree and this is not reflected by Directed F1-score. Therefore, we advocate to use kernel metrics to better evaluate the quality of the predicted taxonomies (but we still report standard measures for comparison purposes in the following experiments).

	, we compare
	trees from T 4 5,𝑘 (𝑘 is varying) with their original version in T 4 5
	and reported Directed F1 and WL Kernel scores. We observe that
	Directed F1-score linearly decreases (Pearson's coefficient ≥ 0.98)
	while WL Kernel plummets more. With 600 corruptions, Directed
	F1-score

Table 2 :

 2 Reconstruction results on two sub-taxonomies of WordNet. Dimensions are tuned on the sub-taxonomy communication.n.02 with Directed F1-score. Best results are bolded.

	Sub-Taxonomy (|𝑉 |, |𝐸|)	Tree Extraction	Distance Dimension	Centroid	Directed Transitive F1-score F1-score	WL Kernel
			𝑑 E	5	basic_cognitive_process.n.01	0.2146	0.1183	0.0477
		NE	𝑑 P	100	cognition.n.01	0.391	0.6129	0.2494
			𝑑 L	100	cognition.n.01	0.5466	0.7615	0.7341
	cognition.n.01 (3999, 4033)	MSTE	𝑑 E 𝑑 P 𝑑 L	100 100 100	cognition.n.01 cognition.n.01 cognition.n.01	0.249 0.373 0.6074	0.2754 0.6048 0.9098	0.0402 0.2436 0.7263
			𝑑 E	5	basic_cognitive_process.n.01	0.0853	0.0596	0.0658
		LASE	𝑑 P	10	concept.n.01	0.3073	0.2037	0.1593
			𝑑 L	50	cognition.n.01	0.7805	0.5961	0.8954
			𝑑 E	5	vascular_plant.n.01	0.1568	0.1397	0.0147
		NE	𝑑 P	100	vascular_plant.n.01	0.5439	0.5908	0.8565
			𝑑 L	100	herb.n.01	0.6243	0.4102	0.9256
	plant.n.02 (4487, 4493)	MSTE	𝑑 E 𝑑 P 𝑑 L	100 100 100	vascular_plant.n.01 vascular_plant.n.01 herb.n.01	0.1795 0.517 0.6341	0.2467 0.5751 0.5387	0.0153 0.852 0.924
			𝑑 E	5	vascular_plant.n.01	0.09	0.231	0.0345
		LASE	𝑑 P	10	vascular_plant.n.01	0.4633	0.4984	0.4603
			𝑑 L	50	herb.n.01	0.9032	0.349	0.9789

https://github.com/uhh-lt/Taxonomy_Refinement_Embeddings

http://ltmaggie.informatik.uni-hamburg.de/jobimtext/components/pattamaika/

Authors' version

Torregrossa et al.


Emna Moujehed

Zied Zarai
email: zied.zarai@isbs.usf.tn

Haifa Khemir

Neila Miled

Mohamed Salah Bchir

Corinne Gablin

François Bessueille

Anne Bonhommé

Didier Leonard

Frédéric Carrière

Ahmed Aloulou
email: ahmed.aloulou@enis.usf.tn

Cleaner degreasing of sheepskins by the Yarrowia lipolytica LIP2 lipase as a chemical-free alternative in leather industry

Keywords: Yarrowia lipolytica LIP2 lipase, Enzymatic degreasing, Leather industry

Introduction

The leather industry is considered as one of the most polluting activities due to the broad range and extent of hazardous chemicals that are released to the environment in wastewater.

Sustainable leather manufacturing is a topic with growing importance. That's why worldwide efforts have been made towards reducing ecological risks through ecofriendly approaches in leather processing [START_REF] Huffer | Sustainable leather manufacturing: A topic with growing importance[END_REF]. Degreasing of high-fat content animal skins is a critical step in leather industry. When processing small skins from goat and sheep, tanneries aim for a residual fat content of no more than 5 %, but they are not always able to achieve this. The problem is that the fat is embedded between the grain surface and the corium (the deep inner layer of the skin), which makes it very hard to remove. That's why dry cleaning with strong organic solvents is a widely used technique to dissolve the fat with all the inherent hazards. However, dry cleaning is expensive, and the results are not optimal due to possible damage to the skins.

Detergents are a milder and safer chemical alternative but not as effective. Owing to the fastgrowing enzyme industry and the diversity of products it provides, another method consists in the use of lipases, fat-degrading enzymes, that can simply be added to the drums. Lipasemediated degreasing has emerged as a cleaner alternative in leather manufacturing [START_REF] Christner | The use of lipases in the beamhouse processes[END_REF][START_REF] Rasmussen | An enzymatic treatment of skin and hide degreasing[END_REF][START_REF] Saran | Enzyme mediated beam house operations of leather industry: a needed step towards greener technology[END_REF][START_REF] Shan | Surfactant-free beamhouse technology of leather manufacturing: Removing constraints for the breakdown of natural fats catalyzed by lipase[END_REF].

Benefits include also increased leather uniformity and chemical uptake since lipases improve filling and dye penetration to produce a much more uniform appearance [START_REF] Rasmussen | An enzymatic treatment of skin and hide degreasing[END_REF].

Lipases (EC 3.1.1.3) are soluble enzymes that act on natural insoluble triacylglycerols (TAG) [START_REF] Aloulou | Exploring the specific features of interfacial enzymology based on lipase studies[END_REF]. The LIP2 lipase from the yeast Yarrowia lipolytica (YLLIP2, NCBI accession number CAB91111) is an attractive enzyme that can be overproduced at industrial scale up to a level of 3 g of lipase/L of culture medium [START_REF] Aloulou | Purification and biochemical characterization of the LIP2 lipase from Yarrowia lipolytica[END_REF]. YLLIP2 is the main secreted lipase by Y. lipolytica by exhibiting more than 97% of the extracellular lipase activity [START_REF] Kamoun | Biochemical characterization of Yarrowia lipolytica LIP8, a secreted lipase with a cleavable C-terminal region[END_REF][START_REF] Pignede | Characterization of an extracellular lipase encoded by LIP2 in Yarrowia lipolytica[END_REF]. It displays a 1,3regioselectivity that allows the hydrolysis of ester bonds at external positions of the glycerol backbone of TAG [START_REF] Aloulou | Purification and biochemical characterization of the LIP2 lipase from Yarrowia lipolytica[END_REF][START_REF] Aloulou | Yarrowia lipolytica Lipase 2 Is Stable and Highly Active in Test Meals and Increases Fat Absorption in an Animal Model of Pancreatic Exocrine Insufficiency[END_REF]. YLLIP2 belongs to the same gene family as Thermomyces lanuginosus lipase (TLL), a lipase produced by Novozymes and sold under the trademarks of Lipolase® and Greasex® in the fields of detergents and leather industry, respectively, along with other biotechnological processes. Although these two lipases show a sequence identity of 30.3%, they have quite distinct biochemical [START_REF] Aloulou | A comparative study on two fungal lipases from Thermomyces lanuginosus and Yarrowia lipolytica shows the combined effects of detergents and pH on lipase adsorption and activity[END_REF] and structural properties [START_REF] Aloulou | Biochemical and structural characterization of non-glycosylated Yarrowia lipolytica LIP2 lipase[END_REF]. YLLIP2 can catalyze the hydrolysis of both long-, medium-and short-chain TAG at high rates. At pH 8.0 and in the absence of detergents, the specific activity of YLLIP2 (11,751 ± 402 U/mg) on olive oil-gum arabic emulsion is much higher than that measured previously with TLL (4,975 ± 230 U/mg) [START_REF] Aloulou | Purification and biochemical characterization of the LIP2 lipase from Yarrowia lipolytica[END_REF][START_REF] Tiss | Effects of gum arabic on lipase interfacial binding and activity[END_REF], and thus greater than the highest rate of hydrolysis of long-chain triglyceride previously recorded with a lipase under these conditions.

The aim of the current study was to assess use of YLLIP2 in sheepskins degreasing. Timecourse and dose-dependency of YLLIP2 in degreasing were measured under conditions mimicking those prevailing in the leather industry. Lipase doses for the industrial scale degreasing were deduced from these experiments. The degreasing efficacy of YLLIP2 was tested with time-of-flight secondary ion mass spectrometry (ToF-SIMS) and compared with that of conventional detergent. Leather quality was followed by attenuated total reflectance-Fourier transform infrared spectroscopy (ATR-FTIR) and scanning electron microscopy (SEM). The effluent quality was monitored to determine the pollution load generated.

Materials and methods

Lipase production

Using W29 wild-type Y. lipolytica strain, YLLIP2 was produced in batch cultures using olive oil, the only carbon source, as inducer. Briefly, 1-L shake flasks were filled with 100 mL of YPO culture medium (1% w/v bacto yeast extract, 2% w/v bacto peptone, 1% w/v olive oil, and 0.1 M sodium phosphate, pH 5.6). Bacto yeast extract and bacto peptone solutions were sterilized by autoclaving. The sodium phosphate buffer, the D-glucose solution, and the olive oil were sterilized by filtration. Cultures were inoculated with a 24-h Y. lipolytica liquid preculture to get an initial optical density of 1 U at 600 nm to start the cell growth directly in the exponential growth phase, as well as to establish reproducible cell culture conditions.

Cultures were then incubated at 29 °C in an orbital shaker set at 160 rpm. Media and techniques used here to grow and handle Y. lipolytica have been described elsewhere [START_REF] Najjar | Quantitative study of lipase secretion, extracellular lipolysis, and lipid storage in the yeast Yarrowia lipolytica grown in the presence of olive oil: analogies with lipolysis in humans[END_REF].

Biomass and lipase activity were monitored as a function of time until the lipase activity level in the culture broth reached a maximum value. After discarding cell pellets, the resulting crude supernatant was concentrated by ammonium sulfate precipitation at 65% salt saturation.

After centrifugation, the lipase precipitate was stored at -20 °C until use.

Lipase activity assay

YLLIP2 activity was assayed potentiometrically by automatic titration of free fatty acids released from a mechanically stirred olive oil/gum Arabic emulsion, using 0.1 M NaOH and a pH-stat device (Metrohm, Switzerland). Assays were performed at a constant pH value of 8,0 in a thermostated (37 °C) vessel containing 10 mL of olive oil/gum arabic emulsion and 20 mL of a 0.5 mM Tris, 150 mM NaCl and 5 mM CaCl 2 solution. The specific activity of YLLIP2 was found to be 11,751 ± 402 U/mg under these conditions [START_REF] Aloulou | Purification and biochemical characterization of the LIP2 lipase from Yarrowia lipolytica[END_REF]. One lipase international unit corresponds to one µmol of free fatty acid released per minute.

Degreasing assays

Sheepskins were kindly provided by SOSA Cuir tannery (Sousse, Tunisia). After conventional deliming and bating stages, sheepskins were randomly divided into pieces (10 x 15 cm, weight ~20 g) and distributed to different groups to assess time-course and doseresponse relationships of lab-scale skin degreasing by YLLIP2 lipase. Enzymatic assays (EZ) were performed in 50 mL shake flasks, under a gentle agitation of 50 rpm, at pH 8 and 30 °C in the presence of 20 mL degreasing buffer containing 1 mM Tris. It is worth noticing that one hundred skin weight percent (100 wt %) of buffer is usually used in degreasing at the industrial scale. As a control experiment, chemical treatment (CT) was carried out with the commercial detergent Synthol (SMIT, The Netherlands) under the same conditions. For process validation, industrial degreasing of whole sheepskins was performed in tannery drums filled with 100 wt % of the degreasing buffer under optimized conditions (incubation time and lipase amount) at 50 rpm shaking frequency and 30 °C. Treated skins then underwent conventional tanning.

Residual fat content

Treated sheepskins were dried at 100 °C to constant weight. Residual fats were then extracted by dichloromethane using the Soxhlet method. After solvent removal by rotary evaporation, the residual fat content percentage was calculated from the extracted fat and the initial dried sample weights.

Time-of-flight secondary ion mass spectrometry (ToF-SIMS)

The leather surface analysis was carried out on a TRIFT III ToF-SIMS instrument (Physical Electronics, USA) operated with a pulsed 22 keV Au + ion gun (2 nA ion current) rastered over a 300 × 300 µm 2 area. The electron gun was operated in pulsed mode at low electron energy for charge compensation. The ion dose was kept below the static conditions limit. Data were analyzed using the WinCadence software. Mass calibration was performed on hydrocarbon secondary ions.

Attenuated total reflectance-Fourier transform infrared spectroscopy (ATR-FTIR)

ATR-FTIR analysis of the leather surface was carried out at room temperature using a NEXUS 670 spectrometer (Thermo-Nicolet, UK) equipped with mercury-cadmium-telluride (MCT) detector. Spectra were recorded in the wave number range of 4000-680 cm -1 with a 4cm -1 resolution and 256 scans per sample for improved signal to noise ratio. The OMNIC software was used for spectra processing.

Scanning electron microscopy (SEM)

SEM images of the leather were obtained using a VEGA 3 scanning electron microscope (TESCAN, Czech Republic) with a 20 kV accelerating voltage. Samples were coated with a 25-nm thin gold layer deposited by sputtering to produce an electrically conductive surface for SEM.

Chemical characterization

Dried leather was pounded using a laboratory grinder. Collected powder samples were then subjected to pH, formaldehyde, and chromium VI analysis according to the International Union of Leather Technologists and Chemists Societies (IULTCS). Wastewater samples were collected at the end of the industrial degreasing process and then filtered to remove gross solids. Effluent physicochemical parameters, such as pH, conductivity, total suspended solids, five-day biochemical oxygen demand (BOD) and chemical oxygen demand (COD), were determined to assess the pollution load generated.

Physical characterization

Crust leather samples were conditioned at room temperature under standard atmospheric conditions with 65±2 % relative humidity for 48 hours before physical tests. Samples were cut using Hydraulic Sample Press Atom SE20 (Italy). Leather thickness was measured thrice using a Digimatic Quick Thickness Gauge (Mitutoyo™, France). Leather physical properties were determined using an Instron 4301 Tensile Strength Tester (France). Data were processed using Series IX software.

Results & Discussions

Optimization of enzymatic degreasing

A layer of lipids, which are of both sebaceous and keratinocyte origin, covers the surface of the animal skin. Lipids produced by the epidermal cells are an insignificant fraction of the total extractable surface lipid on areas rich in sebaceous glands. Due to the holocrine activity of the sebaceous gland, its product of secretion (sebum) is eventually released to the surface of the skin and coats the fur as well. Lipids of epidermal origin fill the spaces between the cells. Sebaceous lipids are primarily nonpolar lipids such as TAG, wax esters and squalene, while epidermal lipids are a mixture of ceramides, free fatty acids, and cholesterol [START_REF] Pappas | Epidermal surface lipids[END_REF]. Raw sheepskin natural fat content is about 30 % of dry weight [START_REF] Christner | The use of lipases in the beamhouse processes[END_REF]. However, following batingdeliming steps, average natural fat content was about 10 % of dry weight. Sheepskins degreasing potency of YLLIP2 was first followed by performing dose-effect and time-course relationships (Fig. 1). The degreasing level was evaluated by the determination of residual fat content in greasy skins. Dose-dependency study, upon 15 min incubation, showed that increased amounts of lipase led to a decrease of the residual fat content, thus reaching less than 5 % residual lipids with 6 mg of YLLIP2 per 1 kg of skin (Fig. 1a). Degreasing kinetics, using 6 mg of lipase/kg of skin, showed a continuous decrease in residual fat content as function of time to reach ~1 % after 1 h incubation (Fig. 1b). In the sake of comparison, the commercial lipase from T. lanuginosus (TLL, Greasex®) could be applied typically in an amount of 100,000 U per kg of skin with a reaction time of 30 min [START_REF] Rasmussen | An enzymatic treatment of skin and hide degreasing[END_REF]. Assuming a specific activity of TLL about 10,000 U/mg, Greasex® could be added in an amount of 10 mg per kg of skin. YLLIP2 seems therefore to be as efficient as Greasex® in terms of minimum effective concentrations and reaction times to achieve sufficient degreasing.

ToF-SIMS makes possible the detailed identification of molecular structure at the top surface of materials (information depth limited to a few monolayers i.e. about 2-5 nm) [START_REF] Benninghoven | Secondary ion mass spectrometry: basic concepts, instrumental aspects, applications and trends[END_REF][START_REF] Vickerman | Surface analysis: the principal techniques[END_REF] and was used here to gain more insight on the effect of the degreasing treatment. More precisely, this technique provides with elemental and molecular mass related signatures thanks to the bombardment of the samples using a primary ion beam and the mass analysis of the sputtered secondary ions. To study the effect of enzymatic degreasing on fat removal, both chemically and enzymatically degreased sheepskins samples were analyzed in comparison with the untreated surface in order to identify characteristic signatures before and after each respective samples, but degreasing led to some decrease of their relative intensities. The lipase YLLIP2 eliminated more specifically MAG (16:1). This suggests that, preferentially, this lipase hydrolyzes monoacylglycerols. According to the lipid classification system determined by the lipid MAPS consortium, these MAGs are part of glycerolipids referred to as GL0101 [START_REF] Passarelli | Lipid imaging with time-of-flight secondary ion mass spectrometry (ToF-SIMS)[END_REF].

Leather physicochemical properties

Performing chemical and physical tests on leather ensures that leather products fit into the consumer market in terms quality and durability and meet legal trading requirements for the international market. Some of the treatments, used by tanneries to give leather a certain look and feel, are detrimental to the quality and durability of the leather but could go unnoticed unless the material is professionally tested. Leather manufacturers must be able to prove their products are safe for customers and the environment. Testing the chemical properties of leather products will ensure compliance, as well as the right to use the Ecolabel.

Chromium VI and formaldehyde are chemical produced during the leather tanning process.

As they are known to cause severe allergic reactions on contact with skin [START_REF] Buters | Chromium (VI) contact dermatitis: getting closer to understanding the underlying mechanisms of toxicity and sensitization![END_REF], leather products must not contain chromium VI and formaldehyde in concentrations over 3 mg/kg and 50 mg/kg, respectively, of the total dry weight of the leather according to IULTCS [23].

Results showed that YLLIP2-mediated degreasing (EZ) led to much lower chromium VI-and formaldehyde-containing leather when compared to the conventional chemical treatment (CT) (Table 1).

Apart from chemical properties, leather's characterization and quality depend on mechanical

properties such as tear strength, tear load and stitch tear resistance. The average physical values of conventional and enzymatic leather processing are given in (Table 1). Compared with conventionally processed leather, the tear strength resistance of enzymatically treated leather increased from 16.5 ±1 N/mm 2 to 21 ±1 N/mm 2 . Similarly, double edge tear and stitch tear resistance values were improved in favor of enzymatic treatment from 62 ±6 N/mm to 66 ±5 N/mm and 60 ±3 N/mm to 65 ±2 N/mm, respectively. It turned up that enzymatic treatment enhanced strengthening leather collagen. In fact, it has been elucidated from the strength characteristics that enzymatic processed leather generates better physical values and makes stronger leather [START_REF] Mozersky | Vigorous proteolysis: reliming in the presence of an alkaline protease and bating (Post-Liming) with an extremophile protease[END_REF][START_REF] Dayanandan | Application of an alkaline protease in leather processing: an ecofriendly approach[END_REF]. Physical properties of both processed leather samples, previously described, meet the Tunisian Standards specifications 10 N/mm 2 , 15 N/mm and 25

N/mm respectively.

Leather morphology and structure

Infrared spectroscopy study was performed to characterize leather samples surface's and to detect any chemical modification of samples after degreasing process. ATR-FTIR spectroscopic study of leather fibers, given in (Fig. 3), showed that both FTIR data of EZ and CT samples were very similar and had the same characteristics' peaks with three important peaks corresponding to Amide I at 1637 cm -1 , Amide II: 1659-1637 cm -1 due to νC=O stretching and at 3330 cm -1 due to νN-H stretching. Another band occured at 2924, 2853 cm -1 that corresponds to νCH 2 stretching. The 1334.59 cm -1 and 1238.60-1041 cm -1 peaks corresponded to C=N of amide (III) and C-O vibrations respectively. Similar findings were reported in previous studies [START_REF] You | Improvement in surface hydrophilicity and resistance to deformation of natural leather through O 2 /H 2 O low-temperature plasma treatment[END_REF][START_REF] Plavan | An eco-benign semi-metal tanning system for cleaner leather production[END_REF]. Other studies were also performed by [START_REF] Falcão | Application of ATR-FTIR spectroscopy to the analysis of tannins in historic leathers: the case study of the upholstery from the 19 th century Portuguese Royal Train[END_REF] on leather samples to analyze tannins through ATR-FTIR spectroscopy and has also determined same leather characteristics' peaks earlier identified by [START_REF] Derrick | Infrared spectroscopy in conservation science[END_REF]. Since both enzymatically processed leather and conventionally processed leather spectra were identical, it was concluded that the enzymatic treatment, as revealed by FTIR, did not endure chemical changes in leather composition.

To visualize the leather's microstructure and the effect of enzymatic vs conventional degreasing, a scanning electron microscopy was ascertained. The grain surface and the cross section were given in (Fig. 4). High magnification (2000×) was used to study samples binding's grain layer. Fiber's orientation and compactness with the banded structure were clearly visualized through and the collagen polypeptide chains of enzymatically (Fig. 4a) and chemically (Fig. 4a) were obviously compacted. Leather's micrographs showed the smooth surface of samples without grease particles, which indicates the efficiency of conventional and enzymatic degreasing. The hair pores were clean without foreign particles as even reported by Saravanabhavan, S. et al., [START_REF] Saravanabhavan | Reversing the conventional leather processing sequence for cleaner leather production[END_REF]. Thus, the helical conformation of the collagen was maintained for both processes (CT and EZ) as studied by Shoulders, M.D. et al., [START_REF] Shoulders | Collagen structure and stability[END_REF]. In fact, while degreasing operation, enzymes penetrated fibers and acted at molecular level.

Currently, enzymes and nanozyme, such as a nanomaterial with enzyme-like characteristics and nanomaterials in general were widely applied in different sectors like medicine and analytical chemistry [START_REF] Wang | Nanozyme: An emerging alternative to natural enzyme for biosensing and immunoassay[END_REF] for their interesting properties to adhere to materials for the claimed effect. According to Liu and his collaborators, the nanoparticles were conjugated to collagen and fill in fibers, which leaded to the improvement of leather's physical properties [START_REF] Liu | Enhancement of chromium uptake in tanning process of goat garment leather using nanocomposite[END_REF]. That explained the better properties of enzymatic treated leather from conventional processed leather in previous sections.

Concerning the cross line of enzymatically (Fig. 4c) and conventionally (Fig. 4d) treated leather, there is no significant change, fibers' orientation remained uniform in both samples.

The leather structure where the layering between flesh, corium and the grain layer is well observed. CT sample presents some defibrillations that may be due the action of solvents and detergents. Therefore, these SEM results, along with FTIR results, reveal that leather structure, the surface morphology is preserved and the molecular level is not altered.

Environmental impact assessment of sheepskins degreasing

The ratio BDO 5 to CDO was determined to assess the biodegradation capability of effluents.

This study aimed to develop an enzymatic degreasing technology and reduce the chemicals pollution from leather processing, such as sodium chloride and chromium, which engendering discharge large amounts of highly polluted saline wastewater [START_REF] Lefebvre | Treatment of organic pollution in industrial saline wastewater: a literature review[END_REF]. Biochemical oxygen demand (BDO 5 ), chemical oxygen demand (CDO) and suspended solid (SS) were measured to study degreasing impact and control pollution and to evaluate the environmental impact of the conventional and enzymatic processes [START_REF] De Aquim | Water reuse: An alternative to minimize the environmental impact on the leather industry[END_REF]. In addition to these three parameters, the conductivity and pH were also studied [START_REF] Liu | Enhancement of chromium uptake in tanning process of goat garment leather using nanocomposite[END_REF]. Enzymatic degreasing process had certain advantages over the detergent-solvent system regarding the waste disposal issues. The results

given in (Table 2) indicated that the CT-CDO concentration was nearly 12000, which is four times higher than EZ-CDO concentration due to the presence of hazardous contaminants from chemicals (Table 2). The enzymatic degreasing was carried out in one-step without detergent while conventional degreasing in local tanneries involved several processes using surfactants and solvents [START_REF] Thanikaivelan | Recent trends in leather making: processes, problems, and pathways[END_REF]. Thus, the CDO decreased by 75% in absence of surfactants. Water consumption of chemical-based process also reduced during the beam process where the skins were treated in aquatic environments. The proportional reduction of CDO and BDO 5 in the enzymatic degreasing effluent from 12000 ±600 and 3000 ±150 to 3520 ±176 and 2560 ±128, respectively, could be because of fat tissues present in effluent of control sheepskins. Fat destruction by detergents-solvent processes led to the raise of parameters above subsequently to pollution problems. Such findings were in line with several earlier studies. As an illustration, enzymatic process in leather manufacturing described by Sivasubramanian [START_REF] Sivasubramanian | Ecofriendly lime and sulfide free enzymatic dehairing of skins and hides using a bacterial alkaline protease[END_REF] showed that skins enzymatic proceeding is a pollution reducing process unlike conventional unhairing operation designed as the most generating waste process. The enzymatic unhairing process previously detailed was efficient to reduce BDO 5 and CDO to 78% and 84% in 6 hours using an alkaline protease from Bacillus subtilis MTCC 6537, which provided cleaner and less toxic waste. Moreover, the suspended solids CT-SS value was higher than EZ-SS because of solid waste, which contained unloaded proteins and fatty tissues. In fact, according to Sathish et al [START_REF] Sathish | Alternative carrier medium for sustainable leather manufacturing-a review and perspective[END_REF], beamhouse degreasing operations generated 100 kg of solid waste per ton of hides discharged in lands that might end into water table [START_REF] Sawalha | Wastewater from leather tanning and processing in Palestine: characterization and management aspects[END_REF]. In addition, compared to chemical treatment, conductivity values of CT and EZ were quite similar around 16 ±0.8 and 14 ±0.7, respectively. Neutral pH preserves the quality of the skins and prevents the denaturing of collagen fibers. The biodegradability ratio CDO/BDO 5 indicated that EZ effluent enzymatic degreasing is easily degradable whereas CT was not biodegradable and requires physico-chemical treatment to be finally discharged. Comparing to other international leather, new projects are focusing on the leather wastes reuse to the utmost possible. By using products with negative economic value, this green production aims to reduce pollution and industrial wastes. In agreement with other studies, the reuse of many wastes has been important in different applications, such as the retail market and poultry feed makers [START_REF] Thanikaivelan | Recent trends in leather making: processes, problems, and pathways[END_REF]. The degreasing solid wastes could be also used in soaps and poultry feed due to their high content in fatty tissues and proteins.

Conclusion

Y lipolytica lipase performs sheepskins degreasing successfully, without the use of any chemical, within 10 mg lipase/kg of skin in 15 min of application. The enzymatic process is faster and powerful than the chemical process in the studied degreasing operation. It should be noted that the chemical properties of enzymatically treated leather (EZ) are better than those of the standard (CT) while no differences in leather SEM images neither in ATR-FTIR spectra were observed between the different degreasing processes applied. ToF-SIMS spectra were compared before and after degreasing (chemically and enzymatically). Results are consistent with a specific selective elimination process for the enzymatic treatment.

Sheepskins leather quality has been ensured in term of physical properties. However, differences were observed in effluent analysis where CDO/BDO 5 ratio and solid wastes in residual baths of conventional process were higher due to enzymatic degreasing. It is challenging to note that such reductions in costs of effluent remediation were generated with

3 +)

 3 sphingolipids, as well as compounds with low mass as cholesterol, fatty acids and vitamins are observed. In the negative ion mode range (Fig.2), the lipid profile of untreated sheepskin surface shows triacylglycerols (TG) and fatty acids (FA) characteristic signatures that were detected at m/z = 255.2, 281.2, 309.2, 337.2 and 353.2.[START_REF] Passarelli | Lipid imaging with time-of-flight secondary ion mass spectrometry (ToF-SIMS)[END_REF][START_REF] Taylor | Fatty acid and lipid reference spectra[END_REF][START_REF] Urbini | ToF-SIMS and principal component analysis of lipids and amino acids from inflamed and dysplastic human colonic mucosa[END_REF]. The results on the degreased samples are consistent with a specific selective elimination process for the enzymatic treatment. Concerning the positive ion mode (Fig.S1), the peak at m/z = 184.08 (related to phosphatidylcholine) is well detected in sheepskins before degreasing treatment. It is well known that cell membranes are consisting of proteins, lipids and phospholipids[START_REF] Gray | Lipid compositions of cells isolated from pig, human, and rat epidermis[END_REF]. It appears that YLLIP2 was efficient to get rid of the phosphatidylcholine signature while the conventional chemical process did not, confirming a specific selective elimination process for the enzymatic treatment. Other positive ion mode signatures were detected at m/z = 311.2, 313.2 and 339.2 (Fig.S2). These peaks are attributed to MAG(16:1) as monoacylglycerols (C 19 H 35 O as well as Monoacylglycerols MAG (16:0) and MAG (18:1) corresponding to C 19 H 37 O 3 + and C 21 H 39 O 3 + respectively. The Monoacylglycerols peaks are detected in the spectra of untreated sheepskins and degreased

Figure 2 .

 2 Figure 1. (a) Dose-response effect of YLLIP2 in sheepskins degreasing; (b) Kinetic of enzymatic degreasing.

Figure 3 .Figure 4 .Figure S2 :

 34S2 Figure 3. ATR-FTIR spectra of leather samples after conventional (CT) and enzymatic (EZ) degreasing.

Table 1 .

 1 Physicochemical characterization of chemically (CT) and enzymatically (EZ) treated sheepskins.

	CT	EZ

Table 2 .

 2 Effluent quality parameters of chemically (CT) and enzymatically (EZ) treated

	sheepskins.
		CDO (mg/L)	BDO 5 (mg/L)	Suspended solids (mg/L)	Conductivity (µs/cm)	pH
	CT 12000± 600	3000 ±150	3.96 ±0.2	16.06 ±0.8	8.49 ±0.42
	EZ	3520± 176	2560 ±128	2.98 ±0.15	13.9 ±0.7	7.72 ±03.8

Acknowledgements

Emna Moujehed's doctoral work is supported by the National Program of Research and Innovation (PNRI) & the Ministry of Higher Education and Scientific Research, Tunisia.

chemicals-free process due to YLLIP2 specificity to hydrolyze natural fat without damaging leather and environment.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could influence the work reported in this paper.

Supplementary Data