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The Pico Project: Looking ahead 
Dino Buzzetti 1 and Ernesto Priani Saisó 2 

1olim University of Bologna  
2 Universidad Nacional Autonoma de México (UNAM) 

 
1.  The digitisation of the Conclusiones Nongentae Disputandae at the Virtual Humanities 
Lab of Brown University opens up new research prospects for the Pico Project. The edited text 
is an accurate transcription of the editio princeps printed in Rome by Eucharius Silber on 7 
December 1486. Pico’s Conclusiones, or Theses, is a collection of short statements composed 
on the authority of former thinkers and schools, as well as according to his own opinion, on a 
wide range of philosophical and theological topics. Right at the outset of his “Preface,” Pico 
describes his own work with these words:   

The following Nine Hundred Dialectical, Moral, Physical, Mathematical, Metaphysical, 
Theological, Magical, and Cabalistic opinions, including his own and those of the wise 
Chaldeans, Arabs, Hebrews, Greeks, Egyptians, and Latins, will be disputed publicly by 
Giovanni Pico of Mirandola, the Count of Concord. In reciting these opinions, he has not 
imitated the splendor of the Roman language, but the style of speaking of the most celebrated 
Parisian disputers, since this is used by almost all philosophers of our time. The doctrines to 
be debated are proposed separately by nations and their sect leaders, but in common in 
respect to the parts of philosophy—as though in a medley, everything mixed together. 
(Farmer and Pico 1998, 211) 

Pico’s theses deal with an astonishing amount of different subjects and refer to a large number 
of authors belonging to several philosophical and religious traditions. This means that their 
invention rested upon a prior knowledge of a vast and eclectic variety of doctrines and works. 
As Stephen Farmer observes, “no one can claim mastery over more than a small part of the 
traditions covered in Pico’s text” (xiv), and so the problem of tracing back all of his sources 
still remains an open question.  

Pico’s Theses are organised in two main groups: the first 400 “historical theses” (32) 
according to the opinion of past philosophers, and the remaining 500 theses according to his 
own opinion. In turn, the philosophers’ opinions are grouped by nations or sects—the Latins, 
the Arabs, the Greek Peripatetics and Platonists, the Pythagorean, Chaldean, Hermetic, and 
Cabalist wisemen—and for each nation by their sect leaders, such as Albert the Great, Thomas 
Aquinas, Averroes and so on. So apparently we should know to whom the ideas conveyed by a 
given thesis is to be ascribed. But again Farmer warns us that “it is important to recognize that 
not all or even most of the nine hundred theses can be traced unambiguously to single 
sources”, since many of them “express opinions assigned to authorities by common consent,” 
and “others turn those opinions intentionally on their heads, apparently as challenges to rival 
philosophers.” Moreover, “Pico drew some of his theses from epitomes, anthologies or 
florilegia, or even wholly from oral sources” and indeed others can be found that “combine 
materials from his sources in a highly idiosyncratic fashion, making it impossible again to 
point to one passage or another as his immediate source,” since “he often compressed ideas 
spread out over dozens of pages […] into the exaggerated correlative forms characteristic of 
his own thought (192–93).   

However, Farmer also admits that “in some cases, it is possible to follow Pico as he moves 
page by page through certain texts, drawing theses from various scholastic commentaries on 
Peter Lombard’s Sentences or from favorite Greek sources” (192). Accordingly we decided, 
for a start, to concentrate on medieval authors, considering that digitised corpora of their 
works are available for most of them. But also in these cases, we still have to face the difficulty 



	 	Buzzetti & Priani / 2 

of locating, for the majority of the theses, possibly exact references to the works of the author 
they are ascribed to.  
 
2.  The research activity of the ARTFL Encyclopédie	Project	at	the	University	of	Chicago1	
offers	an instructive case of the application of digital-assisted approaches to text analysis in 
the successive stages of development of computational tools applied to textual studies. 
Scholars working at the project have all along been fully aware of the kind of opportunities 
offered to researchers by the diverse technologies available from time to time. The project 
avails itself of PhiloLogic, a tool developed specifically to work on the Treasury of the French 
Language, which provides full-text search, retrieval and analysis functionalities and has been 
continually upgraded to allow the collaborative development of higher-level, interoperable 
tools for Humanities Computing applications.2  

ARTFL scholars discern three successive main stages of development of computational 
tools that enabled three distinct hermeneutical approaches, focussing respectively on “the 
interaction of digital methods with the text, the context, and the intertext” (Roe 2014, 89).3 
The first approach, practised already far before the digital turn, consists in the traditional 
provision of concordances, usually presented in digital form as Key Words In Context 
(KWICs). This method “is the oldest, simplest, and in many ways the most powerful tool of 
navigation and exploration of the texts” and it gives the opportunity “to compare different 
uses of a word in the context of the other words that surround it” (Morissey et al. 2016, 603). 
Accordingly, an interactive concordance programme affords “instant searching for words or 
combinations of words through large sets of texts” (604), so that “it gives an idea of their use 
and the lexical field in which they occur most frequently” (Roe 2014, 92). But together with 
those advantages, this approach shows also its limitations: “the specificity of the keyword and 
of its particular context drops gradually out of sight” (Roe 2014, 94), when dealing with 
increasingly larger corpora. Therefore, new analytical tools for search and retrieval are needed. 

In the succeeding phase the attention was directed to implementing more efficient search 
and retrieval tools to explore word contexts in large digitised text collections.  The study of 
context became the chief concern and distant reading was the “response to the growth of the 
digital contexts” (Roe 2014, 95). The resulting approach to text became overwhelmingly 
quantitative and induced analytical practices that were severely undermining textual integrity 
by “reducing the texts or parts of the texts to one or several ‘bags of words’ ”(Morissey et al. 
2016, 606). On the other hand, however, a capacity arose “to consider not only the raw 
frequency of words, but also their lexical field” (Roe 2014, 97). This led up to developing data 
mining tools, mostly based on machine learning. The resulting algorithms were then more 
“concerned about the frequency of words in relation to each other” and the “frequency 
distribution of words” (Morissey et al. 2016, 607). “Automatic classifiers” (Roe 2014, 99) were 
implemented to classify documents and to “establish ontologies that allow to navigate through 
massive textual collections,” by building “a representation of the discursive content of each of 
these classes” (Morissey et al. 2016, 607). Moreover, by representing the frequencies of words 
as vectors in an n-dimensional space, algorithms were developed, that “seek to identify similar 
content by measuring lexical distance between items in a vector space” (611–12). So, 
“lexical similarity measures” (Roe 2014, 101) can be defined, since “the proximity of two 
vectors indicates a similarity in terms of the vocabularies and the frequency distribution of 
these vocabularies” (Morissey et al. 2016, 612).   
																																																								
1 ARTFL Encyclopédie Project. Accessed 4 March 2017. https://encyclopedie.uchicago.edu/.  
2 PhiloLogic. Accessed 5 March 2017. https://sites.google.com/site/philologic3/home.  
3 All translations of texts in French are ours.  



	 	Buzzetti & Priani / 3 

Thus, since concordances hardly help in the exploration of large corpora, new methods 
have been introduced to inspect word contexts and vocabularies through the classification of 
texts, the development of ontologies, and the identification of lexical similarities. To support 
these new methods, new text technologies have been developed to “help the mobility between 
the macroanalysis or distant reading of massive collections and the microanalysis or close 
reading of the digital texts,” in brief, to afford the interplay “between the text and the context, 
through the intertext” (Roe 2014, 103-104).  

Finally, as regards the investigation of intertextual relations, the “heuristic value” 
(Morissey et al. 2016, 609) of such new methods is indisputable, but the “limitations” of these 
tools are equally obvious, since they tend to “remove the order of deployment of the words, so 
essential to the construction of sense” (613). Accordingly, in a later phase, sequence alignment 
techniques were used to identify similar passages. Glenn Roe quotes Roland Barthes —“every 
text is an intertext [...] every text is a new web of past quotations” (Barthes 1973, 1013) — to 
stress the hermeneutical significance of a thorough investigation of text reuse. Intertextuality, 
then, becomes the “theoretical foundation” of further digital implementations (Roe 2014, 104).  
 
3.  Finding the sources of Pico’s theses in an author’s corpus is a matter of context and 
intertextuality. So how can we proceed in our quest? The method of concordance could help, 
but it would necessarily require an exceedingly long human intervention and a painstaking 
close reading to identify the relevant contexts of a given key term. The method of alignment 
would bring optimal results, but very seldom a thesis is likely to turn out as an exact quotation, 
so that looking for text reuse by sequence-similarity searching would hardly produce good 
overall returns. Text mining methods would then seem the most productive. This approach 
comprises a wide range of algorithms and machine learning techniques and testing them by 
comparing their results seemed to be the preferable attitude, even though applying vector 
space models would look as the most promising procedure.  

The vector space approach comprises, in turn, a great variety of algorithms, ranging from 
cosine similarity, count-based, predictive, random-vector, and so-called word-embedding 
models. Among them, the recently developed “popular software package” (Recchia 2016)  
word2vec (Mikolov et al. 2016) is widely employed for its “ease of use and state-of-the-art 
performance” (Recchia 2016). From a methodological point of view, it may be interesting to 
observe that  word2vec  implements a prediction-based model, which relies on an 
“intuition,” namely that “words that share many contexts will be similar to each other.” This 
intuitive step may indeed look quite “hand-wavy” (Goldberg and Levy 2014, 5) and it has been 
demonstrated (Levy and Goldberg 2014) that “despite the apparent gulf between the inner 
workings of prediction-based and count-based models” what  word2vec is doing,  
“mathematically speaking, is not much different than the sort of thing that some count-based 
models have been doing for awhile” (Recchia 2016). But in the face of these critical 
assessments, the same authors have nevertheless acknowledged that this “word embedding 
method” still “remains superior” in some respects (Levy and Goldberg 2014, 2177) to 
traditional natural language processing methods, such as distributional semantics solutions. 
The decisive intuitive step seems then to endow the  word2vec  approach of an indisputable 
heuristic force that makes it look preferable to other context analysis techniques.  
 
4.  Another approach worth exploring is that of The Concept Lab, a research project at the 
University of Cambridge led by Peter de Bolla. 4 This project aims at “tracking both the history 

																																																								
4 The Concept Lab. Accessed 15 March 2017. https://ccdkconceptlab.wordpress.com/.  



	 	Buzzetti & Priani / 4 

of conceptual forms and their architectures by using data derived from digital archives” (de 
Bolla 2013, 2) and “is committed to the view that concepts are not equivalent to the meanings 
of the words which express them” (de Bolla 2015). Theories that treat concepts “as essentially 
in one-to-one correspondence to word senses” are therefore rejected (Recchia et al. 2017, 2) 
and the focus is set on conceptual structures and their internal and external manifestations, 
namely on their “form and function” as well as on “the conceptual networks within which 
concepts circulate” (de Bolla 2015). 

The “distinction between word senses and concepts” is here deemed to be an important 
one, because “word senses change over time,” but “a change in the frequency or lexical 
associations of a particular word does not necessarily entail a change in the concept” 
expressed in in the text (Recchia et al. 2017, 3). This circumstance is particularly relevant in 
relation to Pico’s theses, since his vocabulary, depending as it does on the need to demonstrate 
the genuine concordance of all philosophical positions, is not necessarily the same as that of 
the authors they are ascribed to. As we have seen by quoting his “Preface,” Pico is well aware 
that the expressive language he uses is a matter of choice. To cope with this problem, the 
Cambridge research project refers to the Concept Through Time (CTT) model that stems 
from the collaboration between computational linguists and cultural historians.  

The CTT model is based on the assumption that “the vocabulary used in the debate on a 
concept may change over time” and intends to “focus on the stabilization and destabilization 
of relational vectors between words, i.e., the emergence and disappearance of words within a 
subset, as well as the shifting position of words in relation to one another” (Wevers et al. 2015). 
But does a change in the lexical associations of a given word “merit the addition” of a new 
concept, or “is this simply novel language for describing an old idea?” (Recchia et al. 2017, 3). 
At the present time, work towards finding a fitting algorithm to solve this problem is still in 
progress. The developers of CTT point out that it “a successful system for monitoring 
vocabulary shifts over time should strike a balance between an adaptive strategy that responds 
to changes in vocabulary, and a more conservative approach that keeps the vocabulary stable” 
(Kenter et al. 2015, 1191). In a similar vein, the researchers of the Cambridge project maintain 
that a model should be “flexible” enough (since “words whose meanings shift away from the 
conceptual core should drop out”) and at the same time suitably “stable” (since “conceptual 
networks” should not “drift” significantly “away from the original conceptual core”) (Recchia 
et al. 2017, 6–7).  

These being the options at hands, the decision was taken to test different methods, even 
though the vector space model—and  word2vec  in particular—would seem the most 
promising approach.  

  
5.  A first probing was conducted at the Universidad Nacional Autonoma de México (UNAM), 
in collaboration with the Grupo de Ingeniería Linguistica of the Instituto de Ingeniería, to 
locate the “sixteen conclusions” according to Albert the Great (Farmer and Pico 1998, 212–
17) in his online corpus.5 As an example, let us consider Pico’s first thesis credited to Albert:  

Species intelligibiles non sunt necessariae, et eas ponere non est bonis peripateticis 
consentaneum. (Farmer and Pico 1998, 211) 

Non-lexematic and categorematic words such as non, sunt, et, eas, est were excluded, in order 
to keep only those words that could help in identifying specific contexts and passages in 
Albert’s works. The remaining words, species,	intelligibiles,	necessariae,	ponere,	bonis,	

																																																								
5 Alberti Magni e-corpus. Accessed 17 March 2017. http://albertusmagnus.uwaterloo.ca.  



	 	Buzzetti & Priani / 5 

peripateticis,	and	consentaneum were used as a ‘bag of words’ to search the Alberti Magni e-
corpus. The search retrieves a collection of selected paragraphs of Albert’s works that can be 
used to make a comparison with our thesis to identify relevant contexts. We chose the Cosine 
Similarity method to compare Pico’s theses with passages retrieved from Albert’s e-corpus, 
because it is the most straightforward procedure to detect text similarities. Moreover, it gives 
us the opportunity to compare the results we obtained, with those arrived at applying different 
methods. The results of our experiment are accessible online at the following address: 
http://www.corpus.unam.mx/tesis_latin/.  

So, for instance, the 15 more relevant results concerning the third thesis on Albert the 
Great can be seen through the visual interface of the system, as shown here in Figure 1:    
 

 
— Fig. 1 — 

This particular example is absolutely cogent to expose the difficulties depending on the use, as 
a term of comparison, of short sentences such as Pico’s theses are, because the results we get 
are not decisive: in this case, the fact that 14 over the 15 highest results share the same score 
shows that the chosen bag of words is bound to yield a far too large number of similarity 
results. Consequently, for our purposes, the performance of the simple cosine similarity 
method turns out too low. To obtain significant results other and more efficient 
computational models have to be employed.    
 
6.  A further step in our testing may then consist in applying topic modelling and vector-space 
word-embedding techniques, such as the MALLET, a Latent Dirichlet Allocation, or LDA, 
based toolkit (McCallum 2002, Graham and Milligan 2012), and the  word2vec  packages.6 
This approach may not be decisive for taking into account vocabulary drifting and for locating 
possible sources of Pico’s theses by retrieving exact passages referred to, but it would provide 

																																																								
6 Moody (2016) has recently proposed a mixing of the two models called lda2vec. Similar models that aim at 
combining LDA and  word2vec  have been presented by Das et al. (2015) and Batmanghelich et al. (2016). 



	 	Buzzetti & Priani / 6 

by all means reliable results in identifying recurring contexts and motifs, both in Pico’s works 
and in his sources, that could be quite serviceable for hermeneutic purposes. The heuristic 
power of this method is of substantial support to any text analysis operation, such as 
producing linked open data, ontologies and annotation languages.  

In more detail, a topic modelling analysis would allow a segmentation of the corpus 
under scrutiny into several sections relating to specific topics. A classification of the topics is 
then possible and can be structured hierarchically in a rigorous way as a formal ontology, that 
can provide a powerful means of navigation through the entire corpus. Topic modelling can 
then be employed as a classification tool, with results that, in the case of the French 
Encyclopédie, have been found “consistent with […] previous machine classification 
experiments” (Roe et al. 2014, 337) conducted on the same text (Horton et al. 2009). But the 
topic modelling results enable us to work also within the sections of the texts that deal with a 
specific topic. Distant and close reading can then meet and in defiance of the common 
opinion they can effectively show their complementarity.    

According to the topic modelling approach, a topic is seen as bag of co-occurring words 
and we can deal with them in many useful ways. The full heuristic force of the topic modelling 
outcomes deserves to be thoroughly exploited. To give an example, we may come back again 
to the comparison of topic modelling and conventional techniques in computational 
linguistics. The latter can be conceived of as utterly quantitative, whereas the former is better 
thought of as an adaptive framework, relying on anticipatory assumptions. So, for instance, in 
purely computational linguistics terms, a Pointwise Mutual Information (PMI) measure—
taken as a co-occurrence-based method to determine word relatedness—is seen as a function 
of two probabilistic events evaluating semantic similarity. This is apparently what  
word2vec  also does, since it has been shown that its word-embedding prediction-based 
method “is implicitly factorizing a shifted PMI matrix—the well-known word-context PMI 
matrix from the word-similarity literature, shifted by a constant offset” (Levy and Goldberg 
2014, 2177). But this actually means that the same data can be seen on the one hand as the 
representation of a pure matter of fact, and on the other as endowed of the anticipating power 
to disclose a full range of possible still unnoticed word relations. And it is precisely in 
exposing them that a close-reading approach comes of use.  

Co-occurrence detection can thus be exploited to define a topic, or to produce RDF 
statements and populate a triple store repository, to build and increase graph databases in 
order to store and manage linked data, or again to introduce possible and new ontology 
classes. These activities can be carried out either manually, or alternatively in a semi-
automatic or fully automated way. Thus a controlled annotation language, obtained out of 
topic modelling and co-occurrence results, would possibly enable an intermediary course of 
action between manual and automated procedures, consisting for instance in a formalised 
annotation practice.  

 
7.  A promising attempt to process annotation data comes from machine learning and 
artificial intelligence (AI), through the application of probabilistic soft logic (PSL).7 
Probabilistic soft logic is a recent development in the “field of statistical relational learning 
(SRL)” (Getoor and Taskar 2007), a successful approach to treating “noisy, or uncertain, 
multi-relational data” produced by manual or automated information-extraction procedures. 
In our case, topic modelling results could be mapped onto RDF linked data graphs and 
processed through PSL programmes, such as have been profitably applied to “mining” 

																																																								
7 Probabilistic soft logic. Accessed 18 March 2017. http://psl.linqs.org/.  



	 	Buzzetti & Priani / 7 

annotation graphs. Their outcome produces consistent “graph summaries” quite helpful to 
“handle uncertainty” present within this sort of “sources of evidence about concepts.” For 
graph summaries can actually “group entities and relations based on similarity” and create “a 
graph at a higher level of abstraction” (Memory et al. 2012, 75), well suited to “filter out noise” 
and help with “the identification of structure and meaning” in the ingested data (Liu et al. 
2017, A:1–2). 

This example can be generalised, since the output of manual annotation practices and 
automated information-extraction systems is usually “spurious” and “often hampered by 
noise.” On that account, the “statistical relational learning approach” (SRL) is specifically 
aimed at removing undesirable “noise.” From an SRL point of view, to solve that problem, is 
to solve “the problem of knowledge graph identification,” a process that “infers a knowledge 
graph” (Pujara et al. 2015, 65–66) from “the noisy output of an information-extraction system” 
(Pujara et al. 2013, 556). SRL techniques “incorporate statistical information and logical 
dependencies” (Pujara et al. 2015, 65–66) and formalise collective probabilistic reasoning in 
relational domains by building knowledge graphs, or “probabilistic graphical models” (Bach et 
al. 2015, 2) of  “relational data, i.e., data composed of entities and relationships connecting 
them” (47). The end result is a more consistent representation of the “logical dependencies” 
(17) between a set of entities (concepts, in our case), their attributes, and their relations, that 
can be handled by formal ontologies enabling further reasoning to derive conclusions not 
explicitly expressed in the resulting knowledge-representation data structures.  

Probabilistic soft logic (PSL) is also directly employed to develop a “programming 
framework for designing custom topic models,” called “latent topic networks” (LTNs) that 
can be employed to explore “influence between scientific articles” (Foulds et al. 2015, 778), or 
for that matter literary and philosophical texts in general. A crucial step in the development of 
LTNs and other PSL applications, consists in the use of potential functions, or semantic 
constraints, to handle knowledge graph identification, a process that “incorporates semantics, 
in the form of an ontology,” and employs “ontological constraints as weighted rules” (Pujara 
et al. 2015, 65–66). As a matter of fact, the “semantic dependencies” between nodes in a 
knowledge graph (66) are expressed “in the form of a potential function or constraint, along 
with any necessary parameters, such as the weight of the potential” (Bach et al. 2015, 13). This 
is done because, “if a modeler does not know how the domain behaves, the potentials should 
capture how it might behave, so that a learning algorithm can find weights that lead to 
accurate predictions” (4). Learning algorithms are designed to enable inferences that comply 
with the assigned potential semantic constraints, so as to identify abstract probabilistic 
dependencies. Semantic constraints are then treated as “learned prediction functions to assign 
a confidence score” to uncertain dependencies between concepts: virtually, they are used “as 
‘hints’ to find” the “correct” dependencies, in order to establish reliable knowledge graphs 
(Pujara et al. 2015, 66). 

The whole graph identification process “requires combining two disparate elements: the 
statistics output by an information extraction and ontological constraints derived from the 
semantics of the knowledge graph” (68). Accordingly, it qualifies as a new development of the 
“mechanism known as Bayesian updating,” a process in which “information is used to adjust 
the initial prediction (=prior belief) to the reality of the environment, resulting in a new 
adapted belief about the world (posterior belief)” (De Ridder et al. 2013, 1). In our case, ‘the 
reality of the environment’ consists of textual data, but again—as we have seen in regard to the 
Concept Through Time (CTT) model—by examining lexical dependencies are we dealing 
with concepts and ontology classes, or are we dealing with words and phrases in our texts? 
The point is crucial and deserves, for our purposes, a little more attention.  



	 	Buzzetti & Priani / 8 

As is well known, Bayesian procedures admit of quite divergent interpretations, ranging 
from a strictly objectivist to a purely subjectivist view. Are we trying to find what is actually 
there, or are we just measuring the risk of our bet? Our surmise is that the two opposite points 
of view may even be compatible, depending on one’s intentions and on where one’s interests 
lie. Is the face you spot in a cloud actually there, or is it just a fiction of your imagination? The 
latter may indeed be the case, but you would not see anything if the cloud had not been there. 
If the cloud is made of words, the game is open. Out of metaphor, data mining applications 
using artificial adaptive systems (Tastle 2013), such as PSL models are, move from a principle 
derived from an analogy with natural language: “Exactly as through writing a natural language 
can create cultural objects... in the same way the Artificial Sciences can create through the 
computer automatic models,” whose dynamics develops somewhat autonomously and 
independently from the observed data: “models... generate rules dynamically” in a way 
“similar to the Kantian transcendental rules” (Buscema 2011, 17-20). Models can then be 
construed either as cultural objects, in certain respects independent from what they represent, 
or as objective representations of what is being described. There is both an active, so to speak, 
and a passive aspect in their construction.  

The same duality is present in the practice of textual interpretation. According to the 
“conversational cycle” (Fig. 2) proposed by Frederick Parker-Rhodes in his Inferential 
Semantics (1978, 16), the “thought that the speaker had intended to convey... will be processed 
in the listener mind, and possibly result in the elaboration of a new thought” (17).  

 

 
— Fig. 2 — 

 

This means that the comprehension process, or for that matter the interpretation of a text, 
produces a model of the text not totally dependent from the actual process of its expression. It 
is this kind of partial autonomy from the effective state of affairs that endows the model of its 
significant heuristic force. The epistemological rationale behind our last observations consists 
in admitting “the possibility... of presenting the difference of system and environment within 
the system” (Luhmann 1990, 12). We think it proper to the textual condition that both the 
model and the textual data be represented, and interpreted, within one and the same system. 

Back to the application of PSL to text analysis and natural language processing, we can 
now show that both close and distant reading can play similar roles in respect of quantitative 
as opposed to qualitative, or factual as opposed to conceptual approaches, and that they can 
both be regarded as complementary practices. In PSL applications, input data are always 
assumed as strictly quantitative and purely observed ones, but they may be provided either by 
text mining and distant reading results, or by the outcomes of annotation and close reading 
practices. Accordingly, both close reading and distant reading provide, in this case, factual 
information. On the other hand, a conceptual and interpretational step may be essential in 



	 	Buzzetti & Priani / 9 

producing the outcome of specific distant- or close-reading practices. This is patent in a close-
reading approach, where a reflective aspect is always present, but it turns out to be so also in 
distant-reading data-processing approaches, as we have seen in the case of word2vec. 
Likewise, in the application of PSL to text analysis and natural language processing, an 
anticipatory step can be singled out in the choice of semantic constraints that identify links of 
potential dependence between words and phrases in a text—or, in more technical terms, 
between the logical atoms and predicates that constitute the nodes and edges of a probabilistic 
knowledge graph.  

 
8.  To sum up, what we have proposed so far is a tentative survey of possible models for a 
digital approach to the study of Pico’s works, but whatever the methods we choose, the focus 
of computational analysis, be it cast on the hermeneutics of the text, of its context, or its 
intertextual connections, must preserve the distinctive character of that kind of critical 
reflection, as much conceptual as factual, which is proper of the humanities frame of mind. 
 
 

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