ssL

Interlinking through lemmas.
The lexical collection of the LiLa Knowledge Baseof linguistic resources for Latin
Marco Passarotti, Francesco Mambrini, Greta Franzini,
Flavio Massimiliano Cecchini, Eleonora Litta,
Giovanni Moretti, Paolo Ruffolo, Rachele Sprugnoli
Abstract
This paper presents the structure of the LiLa Knowledge Base, i.e. a collection of multifarious linguistic resources for Latin described with the same vocabulary of knowledge
description and interlinked according to the principles of the so-called Linked Data
paradigm. Following its highly lexically based nature, the core of the LiLa Knowledge
Base consists of a large collection of Latin lemmas, serving as the backbone to achieveinteroperability between the resources, by linking all those entries in lexical resources
and tokens in corpora that point to the same lemma. After detailing the architecture
supporting LiLa, the paper particularly focusses on how we approach the challenges raised by harmonizing different strategies of lemmatization that can be found in
linguistic resources for Latin. As an example of the process to connect a linguistic
resource to LiLa, the inclusion in the Knowledge Base of a dependency treebank is
described and evaluated.
Keywords: linguistic resources, linguistic linked open data, lemmatization, interoperability, Latin.

1. Introduction
Linguistic resources are machine-readable collections of language data
and descriptions typically divided into two categories depending on the kindof content they include: (i) textual resources, such as written and spoken corpora, featuring either partial or full texts of various typologies, which may
differ in genre, author or time period, and (ii) lexical resources, for instancelexica, dictionaries and terminological databases, providing information on
lexical items for one or more languages, including definitions, translations
and morphological properties. In most cases, linguistic resources do not only
feature data, namely texts and lists of lexical items, but also metadata, which
enhance the resource with a medley of annotations ranging from descriptive
information (e.g. structural division into books, chapters, etc.) to linguistic
Received:
Accepted:

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January 2020
April 2020

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traits, such as lemmatization, Part-of-Speech (PoS) tags and syntactic function.
Over the past two decades the research area dedicated to building,
improving and evaluating linguistic resources has seen substantial growthand, today, covers a wide span of languages and language varieties. This
progress speaks to the need of larger (meta)data sets to support empirically-based studies and to the fact that most (stochastic) systems, tools or
algorithms for Natural Language Processing (NLP) currently rely on the
linguistic and meta-linguistic evidence stored in corpora or lexica. The
strict relation holding between NLP tools and linguistic resources is twofold. On the one hand, NLP tools exploit the empirical data provided byresources to build trained models, whose accuracy rates heavily depend on
the size (and quality) of the training data. On the other, the developmentof new resources, as well as the extension of existing ones, is supported
by NLP tools, which automatically enrich (textual or lexical) data withlinguistic metadata.
Despite the increase in the quantity and coverage of linguistic resources,
most of these are locked in data silos, which prevent users from honing boththeir individual and joint potential in interoperable ways. While resources
tend to focus on providing annotation at one or more levels of linguistic
analysis – be those lexical, morphological, syntactic, semantic or pragmatic – linking them to one another helps to draw the overall picture and to
maximize their individual contribution. Indeed, linguistic data and metadata today are scattered in distributed resources, thus failing to provide acomprehensive overview of the annotations available in these separate collections. One of the main challenges at the present time is interlinking the
motley amount of linguistic data and metadata stored in the resources developed over the past five decades of Computational Linguistics and empiricallanguage studies (Chiarcos et al., 2012: 1). Overcoming this challenge is no
simple task because: (a) linguistic resources are often designed for particulartasks (e.g. PoS tagging and syntactic analysis); (b) linguistic resources and
NLP tools may use different conceptual models (e.g. different PoS tagsets);(c) linguistic data might be represented using different formalisms (e.g. annotation schemas), which are often incompatible between systems (van Erp,
2012: 58).
We owe this predicament to the fact that, throughout the years, more
attention has been given to making linguistic resources grow in size, complexity and diversity, rather than making them interact. Tentative solutions

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to the problem of resource isolation, such as the CLARIN1, DARIAH2 andMETA-SHARE3 linguistic infrastructures and databases, are but upshotsof the last decade. What these initiatives provide, however, is a single query
access point to multiple meta-collections of resources and tools, rather thanconnections between them. Instead, making linguistic resources interoperable requires that all types of annotation applied to a particular word/text
be integrated into a common representation for indiscriminate access to any
linguistic information provided by a resource or tool (Chiarcos, 2012: 162).
A current approach to interlinking linguistic resources takes up Linked
Data principles, so that «it is possible to follow links between existing resources to find other, related data and exploit network effects» (Chiarcos etal., 2013: iii). According to the Linked Data paradigm, data in the Semantic Web (Berners-Lee et al., 2001) are interlinked through connections thatcan be semantically queried, so as to make the structure of web data better
serve the needs of users. In the area of linguistic resources, the Linguistic
Linked Open Data cloud (LLOD)4 is a collaborative effort pursued by several members of the Open Linguistics Working Group5, with the generalgoal of developing a Linked Open Data (sub-)cloud of linguistic resources(McCrae et al., 2016). Indeed, the application of Linked Data to linguistic
data ultimately connects Linguistics to other domains that have adopted theparadigm, including Geography (Goodwin et al., 2008), Biomedicine (Ashburner et al., 2000) and Government6.
What this fervent area of research still lacks, however, is a fine-grainedlevel of interaction between linguistic resources capable of stretching beyond
descriptive metadata over to individual word occurrences in a text or entries
in a lexicon.
One subfield that has enjoyed particular prosperity over the past decadeis that devoted to ancient languages. Owing to their key role in accessing andunderstanding the so-called Classical tradition, Latin and Ancient Greek
are among the main beneficiaries.
Although Latin was among the first languages to be automaticallyprocessed with computers thanks to the pioneering work on the texts of
1
2
3
4
5
6

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Cf. https://www.clarin.eu/.
Cf. https://www.dariah.eu/.
Cf. http://www.meta-share.org/.
Cf. http://linguistic-lod.org/llod-cloud.
Cf. https://linguistics.okfn.org/index.html.
Cf. https://data.gov.uk/.

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Thomas Aquinas by the Italian Jesuit Roberto Busa in the 1940s, throughout its sixty-year history Computational Linguistics has been mainly focusing on living languages, whose commercial and social impact is larger
than that of their dead counterparts. In 2006, however, the launch of two
independent (but related) projects aimed at building the first syntacticallyannotated corpora (called ‘treebanks’) for Latin brought about a researchrenaissance of linguistic resources and NLP tools for ancient languages(Bamman et al., 2008). This came as no surprise given the vast amount oftexts written in Latin spread all over Europe and covering a time span ofalmost two millennia. These texts bear testament to a common yet diverse
past and have contributed to shaping European cultural heritage. Making
full use of the most advanced techniques for preserving, investigating andsharing this legacy is at the same time a challenge and an obligation for the
research community.
Thanks to international efforts, several textual and lexical resources, aswell as NLP tools, are currently available for Latin. Despite the launch of anumber of projects for automatic extraction of structured knowledge fromancient sources in the last decade (see Section 2), much like other languages,
linguistic resources and tools for Latin often live in isolation, a condition
which prevents them from benefiting a large research community of historians, philologists, archaeologists and literary scholars.
To this end, the LiLa: Linking Latin project (2018-2023)7 was awarded
funding from the European Research Council (ERC) to build a Knowledge
Base of linguistic resources for Latin based on the Linked Data paradigm,
i.e. a collection of multifarious, interlinked data sets described with the same
vocabulary of knowledge description (by using common data categories andontologies). The project’s ultimate goal is to make full use of the linguistic
resources and NLP tools for Latin developed thus far, in order to bridge thegap between raw language data, NLP and knowledge description (Declerck
et al., 2012: 111).
This paper presents the structure of the lexical basis of LiLa, which
serves as the backbone of the Knowledge Base to achieve interoperability
between textual and lexical resources for Latin. Following a summary of
the linguistic resources currently available for Latin (Section 2), we detailthe architecture supporting LiLa, with special focus on how we approachthe challenges raised by harmonizing different strategies of lemmatization
7

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Cf. https://lila-erc.eu.

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181(Section 3). The inclusion in LiLa of a dependency treebank is describedand evaluated in Section 4. Finally, Section 5 discusses a number of open
questions to be addressed by the project in the near future.

2. Linguistic resources for Latin
A wealth of linguistic resources is digitally available for Latin today as
a result of decades’ worth of work spent turning paper-based textual andlexical data into machine-readable formats. This section seeks to provide abrief overview of these efforts to delineate the quantity and diversity of thelinguistic data currently at our disposal.
With regard to textual resources, among the most prominent collections of digital texts are the Perseus Digital Library8, the corpus of Latin
texts developed by the Laboratoire d’Analyse Statistique des Langues Anciennes (L.A.S.L.A.)9, the Bibliotheca Teubneriana Latina by De Gruyter10,
the collection of Classical Latin texts prepared by the Packard Humanities
Institute (PHI)11, the Loeb Classical Library12 , and a set of collections published by Brepols, such as the Library of Latin Texts13, the Archive of Celtic
Latin Literature14 and the Aristoteles Latinus Database15. More recently,
the Digital Latin Library project16 set out to publish and curate criticaleditions of Latin texts of all types, genres and eras. A similar objective ispursued by the Open Greek and Latin project17, whose ultimate goal is torepresent every source text produced in Classical Greek or Latin in Antiquity (through c. 600 AD) with a view to covering also the Post-classical era
until modern times. The project places the total number of Ancient Greek
and Latin words surviving from Antiquity at 150 million, and the numberof Post-classical Latin words available in some 10,000 books in the Internet
Archive at 200 million.
8
9
10
11
12
13
14
15
16
17

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Cf. http://www.perseus.tufts.edu/hopper/.
Cf. http://web.philo.ulg.ac.be/lasla/.
Cf. https://www.degruyter.com/view/db/btl.
Cf. http://latin.packhum.org/.
Cf. https://www.loebclassics.com/.
Cf. https://about.brepolis.net/library-of-latin-texts/.
Cf. http://www.brepols.net/Pages/BrowseBySeries.aspx?TreeSeries=ACLL-O.
Cf. http://www.brepols.net/Pages/BrowseBySeries.aspx?TreeSeries=ALD.
Cf. https://digitallatin.org/.
Cf. http://www.opengreekandlatin.org/.

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By virtue of infrastructural efforts conducted over the past decade,this large amount of textual resources is now accessible via aggregating initiatives, including Corpus Corporum18, a meta-collection containing morethan 150 million words in texts written in Ancient Greek or Latin provided by more than twenty different corpora and collections; Trismegistos19, a
portal of papyrological and epigraphical resources formerly covering Egypt
and the Nile valley (800 BC-800 AD) and now expanding to the AncientWorld in general; and the eAqua project20, conceived to support the searchof co-occurrences and citations in a number of collections of Ancient Greek
and Latin texts, including Perseus and PHI.
Beside these catchall (meta)collections comprising large number oftexts, genres and authors diachronically spread from Antiquity to Neo-Latin, some corpora provide more specific data. The Patrologia Latina database21, for instance, features more than 100 million words from the writingsof the Church Fathers; the Musisque Deoque digital archive22 contains poetic works by some 200 authors; late-antique Latin texts are made available by
the digilibLT Digital Library, which currently boasts 265 works written before the 6th century AD23; the Corpus Grammaticorum Latinorum24 gathers the Grammatici Latini, that is, Latin grammar manuals written between
the 2nd and 7th centuries AD and edited by Heinrich Keil in Leipzig from1855 to 1880. As for Medieval Latin, the Index Thomisticus by father Roberto Busa SJ (Busa, 1974-1980)25 collects the opera omnia of Thomas Aquinas, for a total of over 11 million words, the ALIM corpus26 provides texts of
the Italian Latinity of the Middle Ages, and the Computational Historical
Semantics project27 is a large database of Medieval Latin texts from various
sources.
Among other distinctive digital corpora for Latin, noteworthy examples are the School of Salamanca28, a digital text corpus of 116 works of SalCf. http://www.mlat.uzh.ch/MLS/.
Cf. https://www.trismegistos.org/index.php.
20
Cf. http://www.eaqua.net/.
21
Cf. http://pld.chadwyck.co.uk/.
22
Cf. http://mizar.unive.it/mqdq/public/.
23
Cf. http://digiliblt.lett.unipmn.it/index.php.
24
Cf. https://bibliotheque.univ-paris-diderot.fr/bases-de-donnees/cgl-corpus-grammaticorumlatinorum.
25
Cf. http://www.corpusthomisticum.org/.
26
Cf. http://www.alim.dfll.univr.it/.
27
Cf. https://www.comphistsem.org/home.html.
28
Cf. https://www.salamanca.school/en/works.html.
18
19

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mantine jurists and theologians found in selected printed books publishedbetween the 16th-17th centuries; the CroALa corpus brings together some
450 writings by 181 Croatian Latin authors, for a total of over 5 million
words produced between the 10th and 20th centuries29, the Domus sermonum compilatorium archive30 provides the texts of the sermons of theFranciscan preacher Osvladus de Lasko; the Roman Inscriptions of Britain31
hosts multiple corpora, including the Vindolanda tablets; Epistolae32 is a collection of medieval Latin letters written between the 4th and 13th centuries
to and from women; DanteSearch33 provides both the vernacular and theLatin writings of Dante Alighieri, the Latin portion of the corpus counting
approximately 46,000 words; finally, CLaSSES34 is a collection of more than
1,200 non-literary Latin texts, such as epigraphs and letters, from different
eras (between the 4th century BC and the 6th century AD) and sources(Rome, Central Italy, Britain, Egypt and the Eastern Mediterranean Sea).
A subset of the Latin texts carries linguistic annotation. The most common layer of linguistic annotation available in Latin corpora is lemmatization, which in some cases is also enriched with PoS and morphologicaltagging. For instance, while the data provided by CLaSSES and Roman Inscriptions from Britain are lemmatized, the large collection of texts assembled by L.A.S.L.A., the Index Thomisticus, DanteSearch, as well as roughly
one million tokens of the Computational Historical Semantics corpus are
all fully lemmatized and morphologically tagged.
Syntactic annotation, on the other hand, is still limited to a small
set of texts. Four treebanks are currently available for Latin. These are:(i) the Index Thomisticus Treebank (IT-TB) (Passarotti, 2019), basedon the works of Thomas Aquinas; (ii) the Latin Dependency Treebank(LDT) (Bamman and Crane, 2006) of texts belonging to the Classicalera, now part of the Ancient Greek and Latin Dependency Treebank 2.0
under development at the University of Leipzig (Celano, 2019); (iii) the
PROIEL corpus (Pragmatic Resources in Old Indo-European Languages), which features the syntactic annotation of the oldest extant versionsof the New Testament in Indo-European languages and Latin texts from
29
30
31
32
33
34

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Cf. http://www.ffzg.unizg.hr/klafil/croala/.
Cf. http://sermones.elte.hu/szovegkiadasok/latinul/laskaiosvat/index.php?file=os_index.
Cf. https://romaninscriptionsofbritain.org/.
Cf. https://epistolae.ctl.columbia.edu/.
Cf. http://www.perunaenciclopediadantescadigitale.eu:8080/dantesearch/.
Cf. http://classes-latin-linguistics.fileli.unipi.it/.

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both the Classical and Late eras (Haug and Jøhndal, 2008); and (iv) the
Late Latin Charter Treebank (LLCT), a syntactically annotated corpus oforiginal 8th-9th century charters from Central Italy (Korkiakangas andPassarotti, 2011). While the LDT, the IT-TB and the LLCT have shared
the same syntactic annotation schema since their inception (Bamman etal., 2007), resembling that of the so-called analytical layer of annotationof the Prague Dependency Treebank for Czech (Hajič et al., 1999), thePROIEL treebank follows a slightly different style (Haug, 2010). At present, with the exception of the LLCT, all Latin treebanks are also available
in the Universal Dependencies collection (UD) (Nivre et al., 2016)35. In
terms of size, the IT-TB currently counts some 350,000 annotated words,
LDT counts 55,000, the Latin section of the PROIEL corpus 200,000
and LLCT counts 250,000 annotated words.
With regard to lexical resources, among the many dictionaries andlexica available in digital format today are the Lewis and Short dictionary
accessible through Perseus, the Thesaurus Linguae Latinae of the Bayerische Akademie der Wissenschaften in Munich36, and Johann Ramminger’s
Neulateinische Wortliste37. Brepols provides an extensive list of Latin wordforms, known as Thesaurus Formarum Totius Latinitatis38, with numberof occurrences for each in the Library of Latin Texts, and the comprehensive Database of Latin Dictionaries39, which itself consists of a large number of different types of lexical resources. Another noteworthy initiative is
Logeion40, a cross-dictionary search tool, providing simultaneous lookup of
entries in the many lemmatized works from the Perseus Classical collection
by way of the PhiloLogic system41. Within the Computational Historical
Semantics project there is the Frankfurt Latin Lexicon, a lexical resource
built upon assorted source lexicons and taggers and used for NLP tasks, suchas morphological tagging, lemmatization, and PoS tagging42 .
The availability of Latin treebanks has made it possible to induce subcategorization lexica from the IT-TB (IT-VaLex) (McGillivray and Passarotti, 2009) and the LDT (VaLex) (McGillivray, 2013). Latin Vallex is a valency
35
36
37
38
39
40
41
42

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Cf. https://universaldependencies.org/.
Cf. https://www.degruyter.com/view/db/tll.
Cf. http://www.neulatein.de/.
Cf. http://www.brepols.net/Pages/BrowseBySeries.aspx?TreeSeries=TF.
Cf. https://about.brepolis.net/database-of-latin-dictionaries/.
Cf. https://logeion.uchicago.edu/.
Cf. http://philologic.uchicago.edu/.
Cf. https://www.comphistsem.org/lexicon0.html.

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lexicon built in conjunction with the semantic and pragmatic annotationof the IT-TB and the LDT (Passarotti et al., 2016). Presently, Latin Vallex
includes around 1,350 lexical entries. The LatinWordNet (LWN) (Minozzi,2010) was built in the context of the MultiWordNet project (Pianta et al.,2002), whose aim was to build a number of semantic networks for specific languages aligned with the synsets of the Princeton WordNet (PWN)(Fellbaum, 2012)43. The language-specific synsets were created by translating
PWN data with the help of bilingual dictionaries. The LWN counts 8,973
synsets and 9,124 lemmas, and is currently undergoing substantial revision
with a view to refining and extending its contents (Franzini et al., 2019). The
Word Formation Latin (WFL) lexicon (Litta and Passarotti, 2019) provides
information about derivational morphology by connecting lemmas via word
formation rules44.
LiLa seeks to maximize the use of these (and many other) resources for
Latin by making them interoperable, thus allowing users to run complex
queries across linked and distributed resources, like, for instance, searching
the four Latin treebanks for occurrences of verbs featuring a specific (a) dependency relation, e.g. subject (source: treebanks), (b) prefix (source: WFL),(c) valency frame (source: Latin Vallex), and (d) belonging to a particular
WordNet synset (source: LWN).

3. The LiLa Knowledge BaseIn this section we describe the architecture of the LiLa Knowledge
Base, built to structure the information of the Latin linguistic resources in acentralized hub of interaction.
In order to achieve interoperability between distributed resources,
LiLa makes use of a set of Semantic Web and Linked Data standards andpractices. These include ontologies to describe linguistic annotation (OLiA:
Chiarcos and Sukhareva, 2015), corpus annotation (NLP Interchange Format (NIF): Hellmann et al., 2013; CoNLL-RDF: Chiarcos and Fäth, 2017)and lexical resources (Lemon: Buitelaar et al., 2011; Ontolex: McCrae et al.,2017).
43
Synsets are unordered sets of cognitive synonyms, i.e. words that denote the same concept andare interchangeable in many contexts. In WordNets, synsets are interlinked by means of conceptualsemantic and lexical relations.
44
Cf. http://wfl.marginalia.it/.

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Following Bird and Liberman (2001), the Resource Description Framework (RDF) (Lassila and Swick, 1998) is used to encode graph-based data
structures to represent linguistic annotations in terms of triples: (i) a predicate-property (a relation; in graph terms: a labeled edge) that connects (ii) a
subject (a resource; in graph terms: a labeled node) with (iii) its object (another resource/node, or a literal, e.g. a string). The SPARQL language is used
to query the data recorded in the form of RDF triples (Prud’Hommeauxand Seaborne, 2008).
3.1. Linking through lemmatization
Lemmatization is a layer of annotation and organization of linguistic
data common to different kinds of resources. Dictionaries tend to index
lexical entries using lemmas. Thesauri organize the lexicon by collecting all
related entries, and use lemmas to index them; so, for instance, the nominal
synset n#07202206 of the PWN, glossed as “a female human offspring”, islexicalized in LWN by the lemmas: filia “daughter”, nata “daughter” andpuella “girl”. Lemmas are also used to facilitate lexical search in corpora.
This is particularly helpful for languages, like Latin, with rich inflectional
morphology; a regular Latin verb, for instance, can have up to 130 forms(if we exclude the nominal inflection of the participles or gerundives), withvarying endings and, at times, different stems.
Given the presence and role played by lemmatization in various linguistic resources, and the good accuracy rates achieved by the best performing
lemmatizers for Latin (up to 95.30%, as per Eger et al., 2015)45, LiLa uses
the lemma as the most productive interface between lexical resources, annotated corpora and NLP tools. Consequently, the LiLa Knowledge Base ishighly lexically based, grounding on a simple, but effective assumption thatstrikes a good balance between feasibility and granularity: textual resources
45
Such high rates of automatic lemmatization of Latin should be taken with a grain of salt. Indeed, performances of stochastic NLP tools heavily depend on the training set on which their models
are built, and so decrease when they are applied to out-of-domain texts. This problem is particularly
challenging for Latin owing to its wide diachrony (spanning two millennia), genre diversity (ranging
from literary to philosophical, historical and documentary texts) and diatopy (Europe and beyond).
For the state of the art in automatic lemmatization and PoS tagging for Latin, see the results of the firstedition of EvaLatin, a campaign devoted to the evaluation of NLP tools for Latin (Sprugnoli et al.,2020). The first edition of EvaLatin focused on two shared tasks (i.e. lemmatization and PoS tagging),
each featuring three sub-tasks (i.e. Classical, Cross-Genre, Cross-Time). These sub-tasks were specifically designed to measure the impact of genre variation and diachrony on NLP tool performances.

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are made of (occurrences of) words, lexical resources describe properties ofwords, and NLP tools process words.
Figure 1 presents the main components of the LiLa Knowledge Base,showing the key interlinking role played by the Lemma node. A ‘Lemma’ isan (inflected) ‘Form’ chosen as the citation/canonical form of a lexical item.
Lemmas occur in ‘Lexical Resources’ as citation/canonical forms of lexical
entries. Forms, too, can occur in lexical resources, like in a lexicon containing all of the forms of a language (for instance, Tombeur, 1998). Both
Lemmas and Forms can have ‘Morphological Features’, such as PoS, gender,
mood and tense. The occurrences of Forms in real texts are ‘Tokens’, which
are provided by ‘Textual Resources’. Finally, on NLP tools performances canprocess either Textual Resources (e.g. a tokenizer), Forms, regardless of theircontextual use (e.g. a morphological analyzer), or Tokens (e.g. a PoS tagger).

Figure 1. The main components of LiLa.

The core of the LiLa Knowledge Base consists of a large collection of
Latin lemmas: interoperability is achieved by linking all those entries in lexical resources and tokens in corpora that point to the same lemma. While theprocess of selecting the canonical forms to be used as lemmas tends to follow
a standardized series of language-dependent conventions (e.g. for Latin, thenominative singular form for nouns, or the first person singular of the active
indicative present tense for verbs), building and structuring a repository of
canonical forms that may serve as a hub in LiLa is complicated by the factthat different corpora, lexica and tools adopt different strategies to solve theconceptual and linguistic challenges posed by lemmatization, namely (a) theform of the lemma and (b) lemmatization criteria.
Citation forms for the same lexical item chosen to represent the lemmadiffer in (a) graphical representation (voluptas vs uoluptas “satisfaction”), (b)SSL_2020(1).indb 187

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spelling (sulfur vs sulphur “brimstone”), (c) ending and possibly inflectional
type (diameter vs diametros vs diametrus “diameter”), or (d) in the paradigmatic slot representing the lemma (sequor “to follow”, first person singular of
the passive/deponent present indicative vs sequo, first person singular of theactive present indicative). Furthermore, homographic lemmas, like occīdo(ob+caedo “to strike down”) and occĭdo (ob+cado “to fall down”), can either
be left ambiguous by using the same character string occido for the forms ofboth lemmas, or told apart. For instance, in the Index Thomisticus corpus
occīdo and occĭdo are recorded as occido^caedo and occido^cado, respectively,
while in the LDT (and in the Perseus Digital Library in general) as occido1
and occido2.
As for lemmatization criteria, differences are such that, on occasion, aword form can be reduced to multiple lemmas. This is the case of participles,
which can be considered either as part of the verbal inflectional paradigm
or as independent lemmas deserving of a separate entry in lexical resources. Accordingly, participles can either be lemmatized under the main verb
or under a dedicated participial lemma, which in turn may be used either
systematically or only when the participle has grown into an autonomous
lexical item (e.g. doctus “learned”, morphologically the perfect participle ofdoceo “to teach”). The same holds true for deadjectival adverbs (e.g. aequaliter “evenly” from aequalis “equal”), which are either lemmatized as forms of
their base adjective, as happens in the IT-TB, or treated as independent lemmas, like in the PROIEL treebank. Another issue is raised by polythematicwords for which missing forms are taken from other stems, as is the case of
melior used as the comparative of bonus (see English “good” and “better”).
These are sometimes subsumed under the (positive degree of the) adjective
or given a self-standing lemma.
3.2. The LiLa ontology of Latin canonical forms
Cases like the disambiguation of the ambiguous forms occīdo and occĭdo attest to the variety of lemmatization solutions different resources may
adopt. In this respect, it is important to note that the approach of LiLa is
not to harmonize resources by choosing one lemmatization standard overanother or by imposing prescriptive guidelines to which all lemmatized resources must be converted. Rather, LiLa aims to provide a descriptive setof concepts and properties capable of integrating all solutions adopted bydifferent Latin resources.

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To this end, LiLa implements a formal ontology, expressed in the Web
Ontology Language (OWL; McGuinness and Van Harmelen, 2004), thatdefines the classes, properties and instances involved in the task of lemmatization, as well as the possible interactions between lemmas, lemmatizedcorpora and lexica. Since the ultimate goal of the project is to establish a network of linguistic resources fully interoperable within the LLOD cloud, thisontology reuses as many existing standards as possible. In this way, we ensure that the data amassed by LiLa are immediately compatible with other
Linked (Open) Data resources.
The LiLa ontology starts by defining the class of the Lemma, the pivotal
concept in our domain. In our definition, lemmatization is the task of indexing all inflected forms under one that is conventionally identified as canonical.
As such, the Lemma is safely subsumed under the general class of Form as defined in the Ontolex ontology, a de facto standard in the Linked Data publication of lexical resources. Relying on the concepts of Ontolex, we define theLemma as a Form that is linked to a Lexical Entry via the property ‘canonicalform’. This structural choice allows us to potentially connect all other lexical
resources compiled using the Ontolex (or Lemon) formalism to our collection.
Forms are grammatical realizations of words or of any other class of
Lexical Entries that have at least one written representation. The Ontolex‘written representation’ property can be used to accommodate the different
spellings or peculiar inflections of canonical forms: in the case of the examples discussed above, sulfur and sulphur become two written representationsof the same lemma, and so do the loan words that display either the Greek or
the Latin endings (like diametros and diametrus)46. We, therefore, use thisproperty whenever the variation in the realization of a lemma affects only
the orthography of a form (including the word ending), provided that its
morphological analysis and the inflectional paradigm are not altered.
What Ontolex also permits is the inclusion of a phonetic representation of a form. As vocalic quantity is often used to disambiguate between
homographic words (again, occīdo and occĭdo), we add a special sub-property
for prosodic representation, which carries all the relevant transcriptions of
a form with long and short vowel diacritics. The variation, however, may involve changes in PoS, inflectional paradigm or other morphological features.
But note that if the variation also entails a different type of inflection (such as diameter on
the one hand and diametrus/diametros on the other), we represent the lemmas as two different forms
linked to one another via the property ‘lemma variant’ (see below).
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Some Latin words belong to more than one PoS, as is, for example, thecase of prepositions that can be used as adverbs. Since Lexical Entries in Ontolex cannot have more than one PoS47, the same restriction applies also tocanonical forms. Accordingly, LiLa will provide two lemmas with written
representation ante “before”, one for the preposition and one for the adverb.
Participles and inflectional variation are harder to model and require an
extension of the Ontolex ontology. Some words present two or more alternative inflectional paradigms, which entail different lemmas. Verbs with botha deponent and an active inflection, for example, are often found in Latin
lexica. Although one of the paradigms might be more frequent and more‘regular’ than another from a traditional lexicography or grammar standpoint48, we cannot exclude that corpora in which the ‘irregular’ instances
are met lemmatize these under the less typical canonical form. As a consequence, LiLa records all possible canonical forms as lemmas; so, in our collection, the verbs sequor49 and sequo50, for example, exist as independent lemmas. Since these forms can both be used to lemmatize instances of the samewords, we link them to one another with the symmetric property ‘lemma
variant’, thus making it possible to retrieve from the textual resources connected to LiLa all the tokens that belong to the same lexical item, regardlessof the lemmatization criteria followed in individual corpora.
Participles, again, behave differently. As previously mentioned, participles like docti “learned” can be reduced to a form of either doceo “to learn” or
doctus “learned”. In these cases, that is, whenever a form can be interpreted
as part of the (regular) inflectional paradigm or as a Lemma in itself, weassociate that form to a special sub-class of Lemma called Hypolemma. Hyper- and hypolemmas are linked to one another via the symmetric property‘has hypolemma’/‘is hypolemma’51.
A Lemma is also defined by a series of morphological features. All lemmas are assigned a PoS (which, as we have already seen, must be exclusive foreach form), and can be analyzed by those traits that are typical of nominal(gender, number, case), adjectival (gender, number, case, degree) and verbal
See the definition of Lexical Entry at https://www.w3.org/2016/05/ontolex/#lexical-entries.
In the case of verbs sequor/sequo, the active form sequo is mentioned by grammarians only: see
Gell. 18.9.8 and Prisc. Ars Gramm. 9.28.
49
Cf. https://lila-erc.eu/lodview/data/id/lemma/124461.
50
Cf. https://lila-erc.eu/data/id/lemma/124462.
51
Note that, with respect to its hyperlemma, a hypolemma entails a change in the PoS: faciliter“easily” is an adverb, while facilis “easy” is an adjective; doctus (as an autonomous lemma) is an adjective,
while doceo is a verb.
47

48

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191(tense, mood, person, number, voice) inflection; additionally, lemmas havean inflectional type (i.e. the conjugations and declensions of traditional
grammars). LiLa’s ontology formalizes these linguistic properties together
with the relevant restrictions, so that, for instance, tense cannot be predicated of nouns. The PoS tags adopted in LiLa are based on the universal tagsetof Universal Dependencies (Petrov et al., 2011). However, in order to ensurecompatibility with other tagsets used for Latin, LiLa’s categories for linguistic annotation are aligned with the OLiA ontology. So, for instance, LiLa’s
class ‘Adjective’ is a sub-class of OLiA’s ‘Adjective’, which also subsumes allother tags used to annotate the same grammatical category.
Lemmas can also be analyzed in terms of their derivational morphology. This level integrates the information recorded in the WFL lexicon
into the LiLa collection. Since an Ontolex extension for derivational morphology is currently under development, this module is still not available
for immediate deploying. Ontolex allows lexical resources to describe derivational morphemes as regular lexical entries, provided with written representations. However, for our ontology, we opted for a minimal extension
only. In LiLa, morphemes belong to their own class, and are grouped into
Affixes (distinguishing between prefixes and suffixes) and Bases. We define the Base as the lexical morpheme of a word that is neither a prefix nora suffix. Words that are derived, even in several steps, from the same root(for instance, adduco “to lead to”, adductio “bringing in”, duco “to lead”,
produco “to lead forth” and productivus “productive”) are therefore linked
to the same base.
This conceptual architecture was first put to the test with a comprehensive list of Latin canonical forms based on the one provided by the
Latin morphological analyzer Lemlat (Passarotti et al., 2017), which wasused to populate the LiLa collection52 . Lemlat’s database reconciles threereference dictionaries for Classical Latin (GGG: Georges and Georges,
1913-1918; Glare, 1982; Gradenwitz, 1904)53, the entire Onomasticonfrom Forcellini’s (1940) Lexicon Totius Latinitatis (Budassi and Passarotti,2016) and the Medieval Latin Glossarium Mediae et Infimae Latinitatis by
du Cange et al. (1883-1887), for a total of over 150,000 lemmas (Cecchini
et al., 2018b).
Cf. https://github.com/CIRCSE/LEMLAT3.
The choice of lexicographic sources for Classical Latin in Lemlat is based on the remarks by
Lomanto (1980).
52

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The linguistic properties of these lemmas are expressed as RDF triples
using the LiLa ontology formalism and are stored in a triplestore publicly
accessible via a SPARQL endpoint54. Lemlat’s lemmas have undergone a
twofold process of revision: firstly, we removed overlapping or duplicate
lemmas between the Classical and Medieval forms; secondly, we generated hypolemmas for all the canonical forms of present, future and perfectparticiples, as well as for deadjectival adverbs, and connected them to theirmain hyperlemmas via the symmetric property ‘has hypolemma’/‘is hypolemma’.
The LiLa collection currently includes 130,925 lemmas, 92,947 hypolemmas, 292,657 written representations of (hypo)lemmas, 59,945 ‘has/is
hypolemma’ properties, and 6,120 links between lemma variants55.
3.3. Examples from the lexical collection of LiLa
In this section, we report on examples taken from the Knowledge Baseto show the way in which a lemma and its connected information are stored
in the LiLa lexical collection. More specifically, we detail how lemma variants, morphological features, hypolemmas, information on derivational
morphology and prosodic representations are treated.
We first consider the lemma claudeo/claudo “to limp”. In the Oxford
Latin Dictionary (Glare, 1982), the entry for this lemma includes both thesecond conjugation (claudeo) and third conjugation verbs (claudo), the latteralso featuring the graphical variant cludo (Lucil. 250). The lemma is recorded
as deriving from the first class adjective clausus “closed, inaccessible”.
In the Ausführliches Lateinisch-Deutsches Handwörterbuch (Georges
and Georges, 1913-1918), alongside the citation forms claudeo and claudo we
also find their respective and semantically identical deponent counterparts
claudeor and claudor.
In the du Cange Medieval Latin Glossarium, lexical entries are provided
neither for claudeo/-eor nor claudo/-or.
As previously mentioned, the Lemlat lexical basis integrates the GGG
dictionaries. In Lemlat, the information about claudeo/claudo provided bythese three reference dictionaries is merged into one single entry; here, aCf. https://lila-erc.eu/sparql/. A network-based access point to the collection is available athttps://lila-erc.eu/lodlive/ and a user-friendly query interface is accessible at https://lila-erc.eu/query/.
55
Numbers subject to change as the process of elimination of duplicate lemmas is still ongoing.
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common ID is assigned to all lexical bases used to build the citation forms of
the lexical entry. In the example case, Lemlat contains five different citation
forms for the same lexical entry, all bearing the same ID: claudeo, claudeor, claudo, claudor, and cludo. In LiLa, these citation forms are represented
by four lemmas distinguished by inflectional category. Claudeo and claudo,
as well as their corresponding deponent forms claudeor and claudor, are citation forms for different lemmas, as they follow two different inflectional
categories (active and deponent second conjugation, respectively)56. Cludo,
on the other hand, is merged with claudo, as these share the same inflection.
Just like sequor and sequo, LiLa connects these four lemmas via the ‘lemma
variant’ property, while cludo and claudo are represented as different written
representations, i.e. graphical variants of the same lemma (Figure 2)57.

Figure 2. Four citation forms of the same lexical entry in LiLa.

In doing so, and as previously mentioned, LiLa harmonizes different
lemmatization strategies and annotation styles, thus granting interoperability. In the example of claudeo/claudo, all the tokens of this lexical item occur56
The homographic lemma of the third conjugation claudo “to close” is an independent nodein LiLa, separate from claudeo/claudo and, thus, given a different unique identifier in the Knowledge
Base.
57
In all LiLa Figures henceforth (taken from the Lodlive interface), the small ‘satellite’ nodes circling the larger ones represent links to other nodes in the Knowledge Base, e.g. the PoS of thelemma.

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ring in the lemmatized corpora and lexica available in LiLa can be joined
together by using a set of five connected citations, regardless of whether thecitation form used in a specific textual resource is claudeo, claudeor, claudor,
or claudo/cludo.
The criterion used to distinguish between the different citation forms
and different written representations of the same lexical item is purely morphological and, specifically, inflectional. If two citation forms for the same
item belong to different inflectional categories, they are considered (and thus
represented in LiLa) as two separate lemmas connected via the ‘lemma variant’ property. If not, they are stored in the lexical collection of LiLa as two
written representations of the same lemma. Indeed, each Lemma node in
LiLa is connected to a number of morphological features, among which is
the inflectional category, as indicated by the ‘has inflection type’ property.
Figure 3 shows the different categories to which the possible citation forms
for claudeo/claudo are connected.

Figure 3. Inflectional categories in LiLa.

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Figure 4. Hypolemmas of verbs in LiLa.

As we have already seen, Lemma nodes in LiLa can be connected to thosefor hypolemmas. In the case of lemmas for verbs, these are all connected totheir hypolemmas for present, future and perfect participles. As Figure 4
shows, the node for claudeo (lemma) is connected to those for its participial
citation forms clausurus/clusurus, claudens/cludens and clausus/clusus (hypolemmas) via the relation ‘is hypolemma’, making it possible to join different
lemmatization strategies for participles. The same holds true for the otherthree lemmas connected via ‘lemma variant’. In this way, whether in a lemmatized corpus a form like claudentem is assigned lemma claudeo (or claudo,
cludo, claudor, claudeor) or claudens, in LiLa the form is always connected tothe same lemma, as claudens is the written representation of the hypolemmasof all four lemmas for claudeo/claudo. Once again, LiLa does not perform
any analysis but merely reflects the disambiguation provided by the connected resources. This means that, be it assigned to claudo or claudens, theform claudentem in LiLa is connected to both claudeo/claudo and claudo “toclose”. If the source corpus (or lexicon) includes morphological annotation,

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the connection of the form to the correct lemma can be partly disambiguated on the basis of inflection, seeing as claudeo and claudo belong to two
different categories58. Instead, if the resource to be included in LiLa does notprovide morphological annotation but lemmatization and PoS tagging only,
any form associated with the lemma claudo or claudens would be connected
to both claudeo/claudo and claudo.
Beside inflectional morphological features, LiLa lemmas also carry information on derivational morphology. Two types of information about word
formation are provided. Firstly, all lemmas belonging to a derivational family,
i.e. a set of (derived) lemmas sharing the same lexical base, are connected to anode common to all family members (Base)59. Secondly, lemmas formed with
one or more derivational affixes are connected to the nodes for such affixes(prefixes or suffixes). The information on derivational morphology is takenfrom the WFL resource by flattening the hierarchical relations of derivation
recorded therein. Indeed, while WFL represents derivational families in termsof rooted trees, where one lemma is hierarchically derived from another (orfrom others, in the case of compounds), LiLa does not include such hierarchical relations between lemmas, but represents derivational morphology via flat
connections between lemmas and their base(s) and affix(es) (Litta et al., 2019).
Figure 5 shows the derivational family tree of claudeo in WFL.
In the derivational tree of Figure 5, each node represents a lemma
belonging to the same derivational family. Nodes are connected by hierarchical relations labelled with the respective word formation rule. For
instance, the lemma claudeo/-eor is the result of an adjective-to-verb conversion rule (A-To-V) applied to the adjective claudus “limping”. The verb
claudico “to limp”, in turn, is derived from claudeo/-eor as a deverbal verb
with the suffix -ic.
Like LiLa, WFL too makes use of the Lemlat lexical basis and so inherits the tool’s lemma merges (e.g. claudeo/-eor). In LiLa, however, claudeo andclaudeor are separate lemmas connected via the property ‘lemma variant’.
Furthermore, LiLa uses ‘lemma variant’ also to connect the third conjugation lemmas claudo/cludo and claudor; these are missing from WFL despite
being recorded in Lemlat as variant forms of claudeo/-eor. Figure 6 shows
how the derivational family of claudeo is represented in LiLa.
58
This disambiguation is only partial. In order to disambiguate between claudo “to limp” andclaudo “to close” (both third conjugation verbs) the resource must provide additional information
other than morphology, e.g. a reference to the semantics of the lexical item.
59
Compounds are connected to more than one Base node.

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Figure 5. The derivational family tree of claudeo in WFL.

Figure 6. The derivational family of claudeo in LiLa.

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In Figure 6, each lemma of the derivational family of claudeo is connected to a common Base node via the relation ‘has Base’. As a connector
between lemmas of a family, the Base node is unspecific and is instead
given a numeric label (in this case, 888)60. Those lemmas that include one
or more affixes are connected to the nodes for such affixes via the ‘has prefix’ and ‘has suffix’ properties, respectively. In Figure 6, this is the case ofinclaudico “to limp / to be lame” and inclaudicabilis “not limping”: whileboth lemmas are connected to the prefix node in (entering)- via the relation ‘has prefix’, inclaudicabilis alone is connected to the suffix node -bil
via the ‘has suffix’ relation. Since the lemma variants claudo/cludo andclaudor do not occur in WFL but in LiLa only, they are not explicitly
connected to the Base node 888. These relations, however, are automatically induced in the ontology of LiLa in that all lemmas connected via‘lemma variant’ share, possibly via inheritance, the same base and affixes(where present).
As mentioned in Section 3.2, cases like occīdo vs occĭdo are handled byattaching a ‘prosodic representation’ with vowel length to the lemma. Figure
7 shows the representation in LiLa of the verb occīdo.
The lemma node for the verb occīdo (with Type ‘Lemma’), is connected to (a) its participial hypolemmas (occisurus, occisus and occidens),(b) its PoS (‘Verb’), (c) the prefix ob-, (d) the inflection type ‘third conjugation verb’ and (e) Base 37, which is shared with, for instance, the verb
peroccido, “to kill thoroughly”. Moreover, the node occīdo is connectedto the written representation occido and to the prosodic representation
occīdo.

60
Base nodes lack any kind of explicitly recorded linguistic information, as doing so would
require a clear definition of the linguistic status of Base nodes stretching beyond that of connectors
between lemmas belonging to the same derivational family. Indeed, such definition would open up
a number of issues. One possible solution could be to assign each Base node a written representationconsisting of a string describing the lexical ‘element’ (a root? a stem?) underlying each lemma in thederivational family (e.g. dic- for dico “to say”, or dictio “a saying”). This procedure is complicated by thefact that different bases can be used in the same family, as is the case of, for example, fer-, tul- and lat-,which can all be found as bases in the family to which the verb fero “to bring” belongs. However, thecurrent treatment of Base nodes does not prevent from integrating etymological information in the
LiLa Knowledge Base (Mambrini and Passarotti, 2020).

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Figure 7. Prosodic Representation in LiLa.

4. Including linguistic resources into LiLa
Compiling the collection of lemmas described in previous sections is
not the ultimate objective of LiLa, but a necessary step towards achieving
interoperability between the linguistic resources included in the Knowledge
Base.
In metaphysical terms, the collection of lemmas in LiLa represents a setof noumena (and it is, in itself, a noumenon), and a resource is a provider ofphenomena (and it is, in itself, a phenomenon). The definition of these terms
in Webster’s Online Dictionary reads61:
The noumenon (plural: noumena) classically refers to an object of human inquiry,
understanding or cognition. The term is generally used in contrast with, or in relation to, phenomenon (plural: phenomena), which refers to appearances, or objects
61

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Cf. http://www.websters-dictionary-online.org.

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of the senses. A phenomenon is that which is perceived; A noumenon is the actual
object that emits the phenomenon in question.

In LiLa, lemmas exist regardless of their actual realizations in textual
and/or lexical resources. The first step of the LiLa project was to build this‘lexical noumenon’. The second step is to connect the noumenon to the phenomenon, i.e. to its actual realizations.
So far, the only textual resource to have been connected to LiLa is
the IT-TB in its original annotation schema. This section describes theprocess of connecting the IT-TB to LiLa and details how the (meta)data provided by this treebank are linked to the lemma collection of theKnowledge Base.
The IT-TB exists in LiLa in its version downloadable from the IT-TB
website (December, 2019)62 . This version includes a selection of the concordances of the lemma forma “form” extracted from three works of Thomas
Aquinas and the full text of the first three books of the Summa contra gentiles, for a total of 277,547 tokens (239,496 lexical tokens and 38,051 punctuation marks), corresponding to 3,901 different lemmas63.
To connect the lemmatized lexical tokens of the IT-TB to the LiLacollection of lemmas, we perform a simple string match between the lemmas in the treebank and the written representations of lemmas in the
Knowledge Base. As a result of this strategy, 3,627 out of 3,901 lemmas in
the IT-TB (corresponding to 233,291 lexical tokens) were linked to at least
one lemma in LiLa, while 274 (corresponding to 6,205 lexical tokens)found no match. Out of 3,697 lemmas, 778 were linked ambiguously64 or,in other words, connected to more than one lemma in LiLa; in LiLa, forexample, there exist two lemmas with written representation venio, bothof which are verbs, one first conjugation (“to genuflect”, a rare Medieval
word from the du Cange glossary) and the other fourth conjugation (“tocome”)65.
Cf. https://itreebank.marginalia.it.
Details on the composition of the IT-TB can be found in Passarotti (2019).
64
Unambiguous linking obtained through simple string match may be risky in the case of homographic lemmas missing from the LiLa lexical collection, i.e. when a lemma in the incoming resource
is a homograph of only one written representation of a lemma in LiLa, but belongs to another homographic lemma not present in the collection.
65
The integration in LiLa of lexical resources providing information like, for instance, the dateof first attestation of a lemma, its frequency, or its prevalence in a specific genre, will help to reduce
ambiguity in the linking process.
62
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To disambiguate cases like venio, we use the morphological tagging provided by the IT-TB, which assigns to each word form its PoS and inflectional
category (declension, conjugation)66. For instance, in the sentence (1):(1)

Nam primo habet formam seminis, postea sanguinis, et sic inde quousqueveniat ad ultimum complementum. (Thom. Summa contra gentiles II 89,9)“At first it possesses the form of semen, afterwards of blood, and so on, untilat last it arrives at that wherein it finds its fulfilment.” 67

the word form veniat in the IT-TB is assigned the PoS ‘Verb’ and the fourth
conjugation, thus making it possible to unambiguously link it to the correct
lemma in LiLa. This strategy disambiguated 650 lemmas out of the ambiguous 778 previously linked.
This leaves us with 128 ambiguously linked lemmas, because the lemmatization and morphological tagging of the IT-TB preclude an automatedchoice between the candidate lemmas. This is the case of the lemma campus(a second declension masculine noun), which links to campus “field” andcampus (marinus) for hippocampus “sea-horse”.
Finally, a number of lemmas were still left unlinked. These were foundto fall under one of the following categories:–– the lemma does not exist in the LiLa collection, as is the case of thethird declension feminine noun actualitas “actuality” (as opposed to potentiality). The IT-TB counts 223 of these cases, besides which 4 are new hypolemmas (e.g. the adverb quantum “as much as” recorded as hypolemma of
quantus “how much”) and 24 are lemmas of the type occido^caedo/occido^cado, for which disambiguation was performed manually: IT-TB tokens connected to occido^caedo were linked to the lemma with prosodic representation occīdo, while those connected to occido^cado were linked to occĭdo;–– the lemma of the IT-TB is a new written representation of a lemma
already present in LiLa; this is the case of the written representation annuncio for the first conjugation verb adnuntio “to announce”. Eight cases;–– the lemma of the IT-TB is a new lemma variant of a lemma alreadypresent in LiLa. For example, the singular first declension masculine noun
66
PoS tagging in the IT-TB does not make use of the usual PoS labels, but follows three inflectional classes: nominal inflection (for nouns, adjectives and pronouns, with a separate tag for thenominal forms of the verbal paradigms: gerunds, gerundives, participles and supines), verbal inflection(for verbs) and no inflection (for adpositions, adverbs, conjunctions and interjections). Further details
on the tripartite tagging of the IT-TB can be found in Cecchini et al. (2018a).
67
English translation from https://dhspriory.org/thomas/english/ContraGentiles2.htm#89.

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anthropomorphita is a lemma variant of the corresponding pluralia tantum
anthropomorphitae (a group of heretics who attributed human form to God).
Three cases;–– so-called ‘pseudo-lemmas’, which are used in the IT-TB for non Latin words (non latina vox), numbers (num. arab. and num. rom. for Arabic
and Roman numbers, respectively) and abbreviations (e.g. breviata loci notatio). Eleven cases;–– lemmatization errors in the IT-TB. Six cases, e.g. pbiectum insteadof obiectum “object”.
After classifying lemmas into these categories, we expanded the LiLacollection with the new lemmas, written representations and lemma variants needed to fully connect the IT-TB to LiLa68. This strategy exemplifies
LiLa’s empirical approach, whereby the lexical basis of the Knowledge Basegrows with the number of linguistic resources connected.
The syntax of the IT-TB is annotated in dependency trees. Figure 8
shows the IT-TB dependency tree of sentence (1).

Figure 8. A dependency tree from the Index Thomisticus Treebank.
68

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Pseudo-lemmas and lemmatization errors remain unlinked.

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The tree in Figure 8 features as many nodes as there are tokens in thesentence, including punctuation. Each token is assigned a syntactic function,
known in dependency treebank jargon as ‘dependency relation’ (DepRel)69.
Figure 9 shows the graphical representation of the connections holding between the tokens of the clause quousque veniat ad ultimum complementum(part of sentence 1) and the lemmas in the LiLa collection.

Figure 9. A clause of the Index Thomisticus Treebank in LiLa.

In Figure 9, each token of the example clause in the IT-TB is connected to exactly one lemma in LiLa via the relation ‘has lemma’, and toits previous/next node in the sentence via the symmetric relation ‘previous
node’/‘next node’ 70.
In the LiLa Knowledge Base, two pieces of information can be extracted from the trees of a dependency treebank:(i) tokens are connected to their syntactic function via the property ‘has
DepRel’. The dependency relations shown in Figure 9 are AuxC (forsubordinating conjunctions, here quousque), ExD (for nodes missing
their head node in the dependency tree, i.e. ellipsis, here veniat), AuxP
69
For a detailed description of the annotation rules and the set of dependency relations used in
the IT-TB, see Bamman et al. (2007).
70
Each token is also connected to a number of descriptive metadata taken from the original
linguistic resource. In the case of the IT-TB, each token is linked to descriptive metadata recording its
position in the texts of Thomas Aquinas (e.g. work, book, chapter, etc.) and to the sequence of morphological tags originally attached to it in the IT-TB (e.g. 3-MB1--6--1 for the third person singular of thepresent subjunctive of fourth conjugation verbs, e.g. veniat). The full morphological tagset of the IT-TB
is available at https://itreebank.marginalia.it/doc/Tagset_IT.pdf.

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(for prepositions, here ad), Atr (for Attributes, here ultimum) and Obj(for direct/indirect objects, i.e. arguments, here complementum). In the
IT-TB, the syntactic functions of nodes in coordinated constructions
are indicated by the extension _Co, as evidenced by veniat in Figure 8.
In LiLa, this is represented via the relation ‘has DepRelEx’, which in
Figure 9 connects the token veniat to the node Co;(ii) dependencies between head and dependent nodes are represented
through the symmetric property ‘has parent’/‘has child’. In Figure 9, forinstance, the relation ‘has child’ holding between veniat and ad indicatesthat veniat is the head of ad in the dependency tree of this IT-TB clause71.

5. Conclusion
In this paper, we have presented the overall architecture of the LiLa
Knowledge Base of linguistic resources for Latin. Interweaving the large
amount of linguistic (meta)data developed thus far in an interoperable
whole is key to promoting the use of resources and tools. Today, this is madepossible thanks to Linked Data technologies.
The first objective of the LiLa project was to compile a large collection of
Latin lemmas in Linked Data form. This collection, described here in Section
3, represents the backbone of LiLa, given the central role played by the lemma in making resources interact. The collection was derived from a numberof reference dictionaries and glossaries covering different chronological eras.
However, as demonstrated by the inclusion of the first linguistic resource inthe Knowledge Base (the Index Thomisticus Treebank; Section 4), a complete lexical coverage is far from being achieved (if not impossible), seeing as
future resources are expected to introduce new lexical items and/or new citation forms of lemmas already recorded in LiLa. The greater the numberof resources connected in LiLa, the larger its lemma collection will become.
The important role of the lemma in LiLa implies that only lemmatizedresources can fully exploit the (lexical) connections in the Knowledge Base.
Nowadays, this is a restrictive condition as, despite growing numbers, many
Latin corpora do not carry this layer of linguistic annotation. One core chalWhen the ‘has parent’/‘has child’ property overlaps with the ‘previous node’/‘next node’ one,these are merged into one edge in the visualization, as exemplified by veniat and ad in Figure 9: veniat
both precedes ad in the word order of the clause and it is its parent node in the dependency tree.
71

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lenge for LiLa will be to collect and evaluate the tools and trained models
available for automatic lemmatization and, next, to build a new set to allow
data providers to process their resource(s) for ready inclusion in the Knowledge Base. Indeed, even if lemmatized, texts might nevertheless cause trouble in cases such as ambiguous homographic lemmas (e.g. occīdo vs occĭdo).
LiLa, after all, reflects the degree of annotation granularity provided by the
resources attached to the Knowledge Base.
Another important issue that LiLa must address is how to deal withresources in closed and/or proprietary formats. While most Computational Linguistics resources and tools are freely available, popular collections ofscholarly editions of Latin and Ancient Greek texts, such as the Bibliotheca
Teubneriana Latina by De Gruyter and all Brepols corpora, are locked behind paywalls. In line with the ‘as open as possible, as closed as necessary’approach, proprietary resources will be connected in the Knowledge Basebut access to them will be subject to charges. In doing so, we hope to influence policy change and to establish LiLa as a leading publication venue of
Latin’s linguistic legacy.
Acknowledgments
We are thankful to Daniela Corbetta and Andrea Peverelli for their invaluable
support in building and extending the LiLa collection of lemmas. The LiLa: Linking Latin project has received funding from the European Research Council (ERC)
under the European Union’s Horizon 2020 research and innovation programme Grant Agreement No 769994.

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Marco Passarotti
Facoltà di Scienze Linguistichee Letterature Straniere
Università Cattolica del Sacro Cuore
Largo Gemelli 1
20123 Milano (Italy)
marco.passarotti@unicatt.it

Eleonora Litta
Facoltà di Scienze Linguistichee Letterature Straniere
Università Cattolica del Sacro Cuore
Largo Gemelli 1
20123 Milano (Italy)
eleonoramaria.litta@unicatt.it

Francesco Mambrini
Facoltà di Scienze Linguistichee Letterature Straniere
Università Cattolica del Sacro Cuore
Largo Gemelli 1
20123 Milano (Italy)
francesco.mambrini@unicatt.it

Giovanni Moretti
Facoltà di Scienze Linguistichee Letterature Straniere
Università Cattolica del Sacro Cuore
Largo Gemelli 1
20123 Milano (Italy)
giovanni.moretti@unicatt.it

Greta Franzini
Facoltà di Scienze Linguistichee Letterature Straniere
Università Cattolica del Sacro Cuore
Largo Gemelli 1
20123 Milano (Italy)
greta.franzini@unicatt.it

Paolo Ruffolo
Facoltà di Scienze Linguistichee Letterature Straniere
Università Cattolica del Sacro Cuore
Largo Gemelli 1
20123 Milano (Italy)
paolo.ruffolo@posteo.eu

Flavio Massimiliano Cecchini
Facoltà di Scienze Linguistichee Letterature Straniere
Università Cattolica del Sacro Cuore
Largo Gemelli 1
20123 Milano (Italy)
flavio.cecchini@unicatt.it

Rachele Sprugnoli
Facoltà di Scienze Linguistichee Letterature Straniere
Università Cattolica del Sacro Cuore
Largo Gemelli 1
20123 Milano (Italy)
rachele.sprugnoli@unicatt.it

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