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title	FAIR Knowledge Graph Construction from Text, an Approach Applied to Fictional Novels
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title	FAIR Knowledge Graph Construction from Text, an Approach Applied to Fictional Novels
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FAIR Knowledge Graph construction from text, an approach applied to fictional novels[edit]

FAIR Knowledge Graph construction from text, an
approach applied to fictional novels
Diego Rincon-Yanez1 , Sabrina Senatore1
1
University of Salerno Via Giovanni Paolo II, 132 - 84084 Fisciano SA, Italy

Abstract
A Knowledge Graph (KG) is a form of structured human knowledge depicting relations between entities,
destined to reflect cognition and human-level intelligence. Large and openly available knowledge graphs
(KGs) like DBpedia, YAGO, WikiData are universal cross-domain knowledge bases and are also accessible
within the Linked Open Data (LOD) cloud, according to the FAIR principles that make data findable,
accessible, interoperable and reusable. This work aims at proposing a methodological approach to
construct domain-oriented knowledge graphs by parsing natural language content to extract simple
triple-based sentences that summarize the analyzed text. The triples coded in RDF are in the form of
subject, predicate, and object. The goal is to generate a KG that, through the main identified concepts,
can be navigable and linked to the existing KGs to be automatically found and usable on the Web LOD
cloud.

Keywords
Knowledge Graph, Natural Language Processing, Semantic Web Technologies, Knowledge Graph Em-
beddings, FAIR

1. Introduction
Due to the complex nature of real-world data, defining a data knowledge model within the
specification of a domain is not a trivial task. Semantic Web Technologies (SWT) promise formal
paradigms to structure data in a machine-processable way by connecting resources defining
semantic relations among them. The result is a rich information representation model whose
information is well defined, uniquely identifiable, and accessible as a knowledge base. However,
the knowledge representation (KR) task is becoming more challenging every day due to the
increasing need to improve, extend, and re-adapt that knowledge to the real-world evolution.
Knowledge Graphs (KGs) provide a way of represent knowledge at different levels of abstrac-
tion and granularity; generally it is described [1] as composed of a knowledge base (KB) and a
reasoning system with multiple data sources. The KB is a directed network model whose edge
represent semantic properties; traditionally those network models can be classified depending
on the relations nature into a (1) homogeneous graph [2] 𝐺 = (𝑉, 𝐸), whose edges (E) rep-
resents the same type of relationship between the vertices (V), and (2) heterogeneous graph
𝐺 = (𝑉, 𝐸, 𝑅)[3, 2], whose edges (E) describe a set of relations (R), i.e., natural connections

Text2KG 2022: International Workshop on Knowledge Graph Generation from Text, Co-located with the ESWC 2022,
May 05-30-2022, Crete,Hersonissos, Greece
$ drinconyanez@unisa.it (D. Rincon-Yanez); ssenatore@unisa.it (S. Senatore)
� 0000-0002-8982-1678 (D. Rincon-Yanez); 0000-0002-7127-4290 (S. Senatore)
© 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
Workshop
Proceedings
http://ceur-ws.org
ISSN 1613-0073
CEUR Workshop Proceedings (CEUR-WS.org)
�between the nodes.
Thanks to the W3C Resource Description Framework (RDF) standard1 , knowledge can be ex-
pressed as a network of atomic assertions, also called facts, described as triples composed of
Subject (S), Predicate (P) and Object (O), 𝑇 = (𝑆, 𝑃, 𝑂). In the light of the traditional definition
of heterogeneous graph 𝐺 = (𝑉, 𝐸, 𝑅), a KB can be described by mapping 𝑉 ≡ (𝑆 ∪ 𝑂) and
𝑅 ≡ 𝑃 as the semantic relation between nodes, leaving the description of vertices as ∀ ∈ 𝐸 :
𝑒 = (𝑉𝑥 , 𝑅𝑧 , 𝑉𝑦 ). On the other hand, the KG Reasoning System (KRS) can be interpreted as the
combination of one or more computing or reasoning (r) techniques to read or write the the state-
ments in the KB, and it can be expressed as 𝐾𝐺 = (𝐾𝑅𝑆, 𝐾𝐵) or 𝐾𝐺 = (𝐾𝑅𝑆, (𝑉, 𝐸, 𝑅))
where 𝐾𝑅𝑆 = {𝑟1 , 𝑟2 . . . 𝑟𝑛 } and 𝑛 ≥ 1 e.g.: 𝑅𝑖 : KGE(𝐾𝐵)𝑒𝑅𝑗 = (𝑒𝑠 , 𝑒𝑝 , 𝑒𝑜 ). In a nutshell,
a Knowledge Graph (KG) is composed of a Knowledge Base (KB) and a Knowledge Reasoning
System (KRS) 𝐾𝐺 = ({𝑟1 , 𝑟2 . . . 𝑟𝑛 }, (𝑉, 𝐸, 𝑅)).
The FAIR [4]; Findability, Accessibility, Interoperability and Reuse principles refer to the
guidelines for good data management practice: resources must be more accessible, understood,
exchanged and reused by machines; these principles in the context of SWT and particularly in
the field of Knowledge Graph, encourage communities such as Open Linked Data Community
(OpenLOD) and Semantic Web to join efforts into the Fair Knowledge Graph topology [4]
building. On the other hand, the Open Knowledge Graphs (OpenKGs) are semantically available
on the Web [2, 5], they can be focused on universal domains, e.g., DBpedia [6], WikiData [7], or
domain-specific knowledge such as YAGO [8] or WordNet2 ; most of them are integrated and
can be found through the OpenLOD Cloud3 , and typically, they can be queried by SPARQL
interfaces. Nowadays, Linked Open Data and FAIR principles have played an essential role in
the KG spreading. These proposals catapulted the Knowledge Base Construction (KBC) process
by providing enormous leverage helping to cross-reference and improve the new generated
KBs accuracy, increasing the number of available queryable sources on the Web.
This work proposes a methodological approach implemented as a library to build domain-
specific knowledge graphs compliant with FAIR principles from an automated perspective
without using knowledge-based semantic models. The method leverages traditional Natural
Language Processing (NLP) techniques to generate simple triples as a starting point and then
enhance them using external OpenKG, e.g., DBpedia, to annotate, normalize, and consolidate
an RDF-based KG. As a case study, novels in literature were explored to validate the proposed
approach by exploiting the features from the narrative construction and relationships between
characters, places and events in the story.
The remainder of this work is structured as follows: Section 2 provides an overview of relevant
approaches, knowledge base construction foundations, and KG construction approaches. Section
3 presents the proposed methodological approach at a high abstraction level. Then, Section 4
describes details of the developed tool and the case study results. Experimental evidence and
evaluation is reported in Section 5. Finally, the conclusions and the future work are highlighted
in Section 6.

1
W3C RDF Official Website - https://www.w3.org/RDF/
2
WordNet Official site - https://wordnet.princeton.edu
3
Open LOD Cloud - https://lod-cloud.net/
�Table 1
KBC process classification; Knowledge Source defines the characteristics of where the knowledge is
located, e.g., Human, source design and Schema Fixed lexicon of entities and relations where the data
will be stored, adapted from [13]
Knowledge Source Method Schema
Curated Manual Yes
Collaborative Manual Yes
Structured Automated Yes
Semistructured Automated Yes
Unstructured Automated No

2. Related Work
The Knowledge Base (KB) lifecycle [9] can be described by three main phases: the Knowledge
Creation, in charge of discovering initial knowledge; in this phase, initial statements are gener-
ated systematically to feed the KB. Then, the Knowledge Curation phase annotates and validates
the generated triples. Finally, the Knowledge Deployment phase is accomplished once the KB is
mature and can be used along with a reasoning system (KRS) to perform inferring operations.

2.1. Knowledge Base Construction
A knowledge base construction (KBC) can be achieved by performing the first two stages of the
KB life cycle, populating a KB [10] and then annotate the knowledge with semantic information
[11]. The annotation process can be approached from an automated or a human-supervised
perspective. More specifically, the human-centered methods are categorized as (1) curated,
meaning a closed group of experts or (2) collaborative, by an open group of volunteers; these
two methods must be sustained by one or more knowledge schemes (e.g., ontology-based
population). Meanwhile, automatized approaches can uphold the previous schema-based[12],
but also a schema-free approach [13], this categorization is detailed in Table 1.
The KB is often a Commonsense Knowledge (CSK) since it refers to generic concepts from
daily life; classifying and getting more specific information can enhance the CSK by adding new
statements annotated semantically. In [14] a three-step architecture is proposed: (1) Retrieval,
(2) Extraction, (3) Consolidation are the steps aimed at fact construction to extract multiple
key-value statements pairs from the consolidated assertions. Also, Open KGs are used to match
and connect terms from existing ontologies. Since Open KGs, like DBpedia, are composed
of large amounts of triples, retrieving data by SPARQL queries or other retrieval systems is
time and resource-consuming. Working with DBpedia On-Demand [15] allows accessing a
lightweight version of the KB, by using the semi-structured fields of DBpedia articles called
info-boxes (structured knowledge from the ontology).
Ontological reasoning, on the other hand, needs synergistic integration: in [16], for example,
lexical-syntactic patterns are extracted from sentences in the text, exploiting an SVM (Support
Vector Machines) algorithm and then finding matches with DBpedia; the resulting matches
are then integrated by a Statistical Type Inference (STI) algorithm to create a homogeneous
�RDF-based KB. Furthermore, Open Information Extraction (Open IE) provides atomic units
of information, with individual references (URI) to simplify conceivable queries on external
databases, such as DBpedia [17].
The KB construction has evolved along with the knowledge representation requirement for
data ingestion as well as human consumption of data [18]. This evolution has increased the
complexity in terms of data dimensionality, number of sources, and data type diversity [19]. For
these additional challenges [20], semantic tools [21] can enhance the collected data by providing
cross-context, allowing inference from native data relationships or external data sources, such
as status reports or supply chain data.
Once an initial KB has been deployed, the Knowledge Completion can be accomplished,
leveraging on the open-world assumption (OWA) also by using masking functions [22] to
connect unseen entities to the KG. Specifically, a masking model uses text features with the
structure 𝑇 = (𝑆, 𝑃, ?) to learn embeddings of entities and relations via high-dimensional
semantic representations based on the known Knowledge Graph Embedding (GKE) models such
as TransE neural network [23]. This approach can also be used for link prediction by exploiting
existing GKE models such as DistMult, ComplEX, to perform inference using the masked form
𝑇 = (𝑆, ?, 𝑂).

2.2. Knowledge Graph Embeddings (KGE)
Knowledge Graph Embeddings (KGE), also called Knowledge Graph Representation Learning,
has become a powerful tool for link prediction, triple classification, over an existing KG. These
approaches encode entities and relations of a knowledge graph into a low-dimensional embed-
ding vector space; these networks are based on Deep Learning techniques arriving at a score by
minimizing a loss function ℒ.
KGE provides a comprehensive framework to perform operations [24] over a KB, usually
provided in the KB deployment phase. TransE [23], being one of the most studied KGE models,
𝑓 𝑇 𝑟𝑎𝑛𝑠𝐸 = −‖𝑒𝑠 + 𝑟𝑝 − 𝑒𝑜 ‖𝑛 , computes the similarity between the 𝑒𝑠𝑢𝑏𝑗𝑒𝑐𝑡 translated by the
embedding of the predicate 𝑒𝑝 and the object 𝑒𝑜 . TransE model is often exploited for applications
such as a knowledge validation model and achieves state-of-the-art prediction performance.
TorusE is a TransE-based embedding method that, combined with the use of Lie Groups
knowledge base (KGLG) [25], is used to validate an inferred KB; it has been tested using stan-
dard de-facto datasets to evaluate the multidimensional distance between spaces of computed
embeddings. Other KGE methods such as DistMult [26] 𝑓 𝐷𝑖𝑠𝑡𝑀 𝑢𝑙𝑡 = {𝑟𝑝 , 𝑒𝑠 , 𝑒𝑜 } use a tri-
linear dot product to capture and extract semantic relations, while the method ComplEx [27]
𝑓 𝐶𝑜𝑚𝑝𝐸𝑋 = 𝑅𝑒({𝑟𝑝 , 𝑒𝑠 , 𝑒𝑜 }) uses a Hermitian dot product.

3. The proposed approach
The proposed approach provides an incremental unsupervised methodology, compliant with
the FAIR principles, to build a knowledge graph on a specific domain, without exploiting
any ontology schema, but achieving the discovery, construction, and integration of triples
𝑇 = (𝑆, 𝑃, 𝑂), extracted from an unstructured text, with zero initial knowledge. The approach
�Figure 1: Proposed Methodology from a high level view. Pipeline steps, positioned related with the
Knowledge Graph definition.

is described by the pipeline of Figure 1 shows the interaction of the main components of the
proposed framework.
From a high-level perspective, the input is represented by the natural language text sources
and feeds the Triple Construction component, in charge of extracting triples from the analysis of
sentences in the text. The text parsing and the triples extraction are tailored to exploit the specific
prosaic text features, starting from the narrative properties and the implicit relations created by
the recurring entities described in the text. The facts extracted from textual data sources are plain
triples < 𝑠𝑢𝑏𝑗𝑒𝑐𝑡, 𝑝𝑟𝑒𝑑𝑖𝑐𝑎𝑡𝑒, 𝑜𝑏𝑗𝑒𝑐𝑡 > that, are incorporated as formal statements in the KB.
The triple elements are annotated semantically, by querying external semantic knowledge bases
such as DBpedia or Wikidata. This task is accomplished by the Triple Enrichment component,
generating a RDF-compliant KB. Afterward, this triple set is input to Embedding Training
component responsible for the KB completion. It accomplishes the reasoning on the KB,
exploiting a KGE model, to infer and validate the collected KB.
Next subsections provides additional details about the introduced components of the frame-
work pipeline of Figure 1.

3.1. Triple Construction
NLP-based parsing tasks are applied to extract plain triple-based sentences. The Triple Construc-
tion component initially carries out the Basic POS annotation on the input raw text, consisting of
traditional preliminary text analysis, namely, the tokenization, stop-word removal and -tagging
routines to identify and annotate the basic part of speech entities.
Once the Basic POS annotation is accomplished, parallel activities, namely Information Ex-
traction and Chunking Rules and Tagging Pattern Execution are carried out, as shown in Figure
2.

3.1.1. Information Extraction:
This block is composed of three basic tasks necessary to get an further processing of the
tagged text yield by the Basic POS Annotation. The text is indeed given to the Named Entity
Recognizer (NER) which identifies named entities according to some specific entity types: person,
organization, location, event. Then a Dependency Parsing task allow locating named entities into
subject or object and finally, the Relation Extraction task is in charge of the triple composition,
�Figure 2: Basic plain Triple Construction: from raw text to the POS annotation, chunking rules to
grammar dependency rules. The dashed lines represent a process that is not executed, applies for the
Tagging Pattern Execution step.

Figure 3: Directed Acyclic Graph (DAG) describing the Tagging Pattern task

starting by the detected entities. Let us remark that the named entities has a crucial role in the
triple identification and generation.

3.1.2. Tagging Pattern:
This task accomplish ad-hoc text processing, considering the nature of the text from a linguistic
perspective. Linguistic patterns were defined specifically for processing the prosaic narrative
and for this reason, for example, some stop words are not discarded. Those patterns can be
implemented to discriminate additional triple-based statement to feed the KB; in particular,
a finite-state machine is proposed, implementing three evaluation steps, one for each triple
component 𝑇 = (𝑆, 𝑃, 𝑂). Figure 3 sketches a few of the implemented state transitions used in
the development of this methodology.

3.1.3. Chunking Rules:
This task achieve a parallel shallow syntactic analysis where certain sub-sequences of words are
identified as forming special phrases. In particular, a regular-expression has been defined over
the sequence of word tags to select and extract basic triple for enriching the KB, such as the
following tag string 𝑇 𝑃 : {< 𝐷𝑇 >? < 𝐽𝐽* >< 𝑁 𝑁 >? < 𝑉 𝐵* >< 𝐷𝑇 >? < 𝐽𝐽* ><
𝑁 𝑁 >}. This regular expression pattern says that a triple pattern TP chunk should be formed
by the subject chunker which finds an optional determiner (DT) followed by any number of
adjectives (JJ) and then a noun (NN); then the predicate-chunker composed of the verb (in its
tenses) an finally the object-chunker that is similar to the subject chunker, except that it could
�Figure 4: Chunking Rule Evaluation Example.

be a pronoun besides the noun. Subject and object chunkers are typical noun phrases. Some
rule example application is shown in Figure 4.
As final result of the Triple Construction component, all the generated data are integrated
into a set of plain triples, 𝑇𝑃 𝑙𝑎𝑖𝑛 ≡ 𝑇𝐼𝐸 ∪ 𝑇𝑃 𝑎𝑡𝑡𝑒𝑟𝑛 ∪ 𝑇𝐶ℎ𝑢𝑛𝑘𝑖𝑛𝑔 , where the 𝑇𝐼𝐸 represents
resulting triples of Information Extraction task, the 𝑇𝑃 𝑎𝑡𝑡𝑒𝑟𝑛 , the result of Tagging Pattern and
𝑇𝐶ℎ𝑢𝑛𝑘𝑖𝑛𝑔 is the Chunking Rules result, once redundant and duplicated are discarded. Finally,
the resulting triple set 𝑇𝑃 𝑙𝑎𝑖𝑛 is stored into a plain format file.

3.2. Triple Enrichment
Once the construction of the triple has been completed and stored, the Triple Enrichment
component (Figure 1) is in charge of annotating semantically the triple elements. First, each
element of triples is a candidate to be enriched semantically, by assigning a URI to it; for this
purpose, SPARQL queries whose arguments are the individual subject and object of the triples
are submitted to OpenKGs such as DBpedia or Wikidata, to retrieve the relative URIs, when
they are available. Similarly, the predicate is also looked up in the semantically enhanced lexical
database WordNet, once the verb has been transformed into its infinitive form.

Algorithm 1 Triple enrichment Algorithm
Input: Plain Triple Data set
Output: RDF Triple Data set
1: for all [𝑆, 𝑃, 𝑂] : 𝐴𝑙𝑙𝑇 𝑟𝑖𝑝𝑙𝑒𝑠 do
2: for (S and O) as Entity do
3: [URI, Label] = QueryOpenKG(Entity)
4: triples.replace( Entity, URI )
5: triples.append( (URI, rdfs:label, label) )
6: end for
7: O = QueryWordNet( tokenization(O) )
8: end for
9: return RDF Triples

The general process of the Triple Enrichment component is described in Algorithm 1. It
�takes the triple collection as input and for each triple subject and object, search for the cor-
responding URI in a OpenKG; and then, once retrieved, replaced the URI in the triple, along
with the associated rdfs:label. Let us notice that not only the external OpenKG URI is ex-
tracted from the query, but also the rdfs:label for each entity added to the KB. A similar
query is carried out on WordNet, for the predicate: a simple disambiguation task is accom-
plished considering the other triple elements, by similarity between terms in the WordNet
definition and triple words. Otherwise, the first sense is retrieved for default. Future devel-
opment are taken into account to select the right sense after a more accurate disambiguation
task. The output of the Triple Enrichment component is a semtic version of the collected
triples: 𝑇𝑅𝐷𝐹 −𝐾𝐵 ≡ 𝑇𝑅𝐷𝐹 −𝐼𝐸 ∪ 𝑇𝑅𝐷𝐹 −𝑃 𝑎𝑡𝑡𝑒𝑟𝑛 ∪ 𝑇𝑅𝐷𝐹 −𝐶ℎ𝑢𝑛𝑘𝑖𝑛𝑔 , where 𝑇𝑅𝐷𝐹 −𝐼𝐸 ,
𝑇𝑅𝐷𝐹 −𝑃 𝑎𝑡𝑡𝑒𝑟𝑛 , 𝑇𝑅𝐷𝐹 −𝐶ℎ𝑢𝑛𝑘𝑖𝑛𝑔 are the RDF-annotated triples coming from the corresponding
tasks of the Triple Construction component.

3.3. KG Embeddings Training
Last component of the described pipeline is the Embedding Training that is responsible to train
the KGE model on the processed triples. The KGE model provides a mechanism to perform
operations with the discovered KB, such as fact checking, question answering, link prediction
and entity linking.
According to the close-world knowledge assumption (CWA) [22] of the knowledge graph,
in general no new statements (triples) can be added to an existing KG, except that predicting
relationships between existing, well-connected entities. In the light of this consideration, it is
essential to integrate some reasoning mechanism (KRS) that allows the KG to perform operation
within the statements inside the KB, to reach the so called closed-world Knowledge Graph
Completion. At the same time, once all the possible statements are saturated, additional new
statement could be added by considering external information sources, in order to enrich the
KB.

4. Implementation detail: A case study
The approach has been implemented and validated, considering a case study relative to the
narrative domain, specifically books and article synopses describing short stories, fairy tales,
adventure stories. This section describes the methodology at a technical level to identify the
main characters, capture the relationships among them, places and events where they are
involved by exploiting linguistic features and qualities of the narrative prose.
An enlarged view of the proposed pipeline of Figure 1 is shown in Figure 5 where the whole
high-level framework is described. A synergy between different methodologies and technologies
has been achieved: NLP activities are integrated with the Machine Learning models, designed
according to the principles of Information Retrieval and FAIR, through the Semantic Web
technologies (RDF, Turtle, OWL and SPARQL).
The implementation is based on Python language and cover the core system shown in Figure
5. Input textual resources are downloaded from Kaggle narrative books, available in plain text
format; at the same time, the synoptic version of the book was retrieved from Wikipedia. Our
case study is applied to the book The Lord of the Rings. The text was extracted using a simple
�Figure 5: Methodology implementation detailed overview.

web crawler that retrieve the content of the URL page; the data is cleaned using a standard
HTML/XML parser (Data Extraction).
As shown in Figure 5, the Triple Construction component achieves the traditional POS-tagging
activities (Figure 2). The information extraction is performed by the Stanford Open-IE server
tool combined with Spacy library (en_core_web pre-trained models in English) for the extraction
of triples.
The Tagging Pattern and Chunking Rules tasks from Triple Construction are ad-hoc designed;
these tasks also were implemented by exploiting WordNet. To give an example of pattern
extraction described in Figure 3 the text "But Frodo is still wounded in body and spirit" is
analyzed according to the regular expression 𝑇 = (𝑁 𝑁 *, 𝑉 𝐵*, 𝑁 𝑁 *), where the wildcard
character is a placeholder for possible variations of that POS-tags. The generated triple is
𝑇 = (𝐹 𝑟𝑜𝑑𝑜, 𝑤𝑜𝑢𝑛𝑑𝑒𝑑, 𝑠𝑝𝑖𝑟𝑖𝑡).
The triple extraction is also aimed at generating the widest set of triples from the sentence
analysis. Along with the definition of regular expression patterns for discovering simple phrase
chunks, the co-reference is also achieved: primary sentence with the same subject in secondary
sentences or simply sentences with pronoun are re-arranged to get triples with noun instead of
pronoun. The rule (𝑃 𝑅𝑃, 𝑉 𝐵, 𝑁 𝑁 ) in Figure 4 allows us to obtain the pronoun PRP that will
be replaced by the right a noun ( in the previous sentence NN 𝑆 = ℎ𝑜𝑏𝑏𝑖𝑡𝑠), to get the triple
𝑇2 = (ℎ𝑜𝑏𝑏𝑖𝑡𝑠, 𝑒𝑛𝑐𝑜𝑢𝑛𝑡𝑒𝑟, 𝑅𝑎𝑛𝑔𝑒𝑟).
Once the Triple Construction process is concluded, the generated triples are rearranged,
discarding duplicates (coming from the parallel tasks). The remaining triples are ordered
according to the same subject and then predicate and then stored in a plain CSV file for further
use.
The Triple Enrichment component uses the CSV-generated file to carry out Algorithm 1. An
initial Named Entity Recognition task allows discriminating important entities among subject
and object, in the triples in order to discard irrelevant triples (i.e., triples with no named entity)
�Algorithm 2 SPARQL Query example to URI and Label extraction

SELECT ∗
WHERE { {
{{ d b r : { word } r d f : t y p e dbo : F i c t i o n a l C h a r a c t e r .
d b r : { word } dbp : name ? l a b e l .
} } UNION { {
{ { d b r : { word } dbo : w i k i P a g e R e d i r e c t s ? URI } }
UNION
{ { d b r : { word } dbo : w i k i P a g e D i s a m b i g u a t e s ? URI } } .
? URI r d f : t y p e dbo : F i c t i o n a l C h a r a c t e r .
? URI r d f s : l a b e l ? l a b e l
}}
FILTER ( lang ( ? l a b e l ) = ' en ' )
}}

and more important, to use that entities as parameter for the query, according to Algorithm
1. The individual entities are looked at DBpedia by submitting SPARQL queries, to retrieve
the semantic annotation (URI and label) corresponding to that entity. The goal is to make the
subject or object on each triple accessible resources, according to the Linked Data principles.
The query shown in Algorithm 2 was submitted to the public SPARQL end-point 4 by
implementing the SPARQLWrapper to retrieve the DBpedia URI of the selected entities and the
rdfs:label. Let us notice that when a URI of an entity is not available, i.e., no result is returned
by the query, a customized URI is generated. In a similar way, the predicate is processed using
WordNet after reducing it to the infinitive form, e.g., the several tenses kills, killed, killing
become the infinite form kill. After these activities, a semantic knowledge base (called RDF KB)
should be available.
The resulting RDF KB is passed to the KGE models (Embeddings Training in Figure 5 to
get the embeddings on the RDF triple-based KB. Embedding models such as ComplEX, TransE,
and DistMult were trained to compare and evaluate the results across this implementation. The
training was achieved and evaluated by using the evaluation proposed by the KG Embeddings
of the framework Ampligraph [28].

4.0.1. Triple Storage:
An important role was assumed by the storage component, that allows to store all the interme-
diate version of the triples generated. In a nutshell, triples generated in the Triple Construction
component (Section 3.1) are stored in CSV format (to reuse them later); then, after the the Triple
Enrichment (Section 3.2), the resulting triples are stored semantically, namely in RDF/Turtle
languages. RDF-based triples can be e imported into the NEO4J5 by the neosemantics plugin
and then the resulting knowledge graph can be graphically displayed into the GUI This last
4
DBpedia SPARQL end-point - https://dbpedia.org/sparql
5
https://neo4j.com/v2/
�storage task is crucial for reusing the KB for later validation, charts generation, query execution,
and further operations.

4.0.2. Source Code:
A demo for the implementation of this project is available on GitHub. The usage instructions
are consigned on the repository6 and the produced results (KBs) should be published under the
CC-BY-SA license.

4.1. Knowledge Graph Completion Evaluation
The KB validation is performed by implementing a cross-graph representation learning ap-
proach [29] by embedding triples based on the semantic meaning of the KB; the representation
is done by estimating a confident score in each statement based on a degree of correctness.
This estimation is calculated by exploiting the KGE models and the corresponding evaluation
metrics over the discovered KB.
First, the validation is accomplished by considering the output of each tasks of the Triple
Construction component and setting a pair-wise comparison among 𝑇𝑅𝐸𝑆−𝐼𝐸 , 𝑇𝑅𝐸𝑆−𝑃 𝑎𝑡𝑡𝑒𝑟𝑛 ,
𝑇𝑅𝐸𝑆−𝐶ℎ𝑢𝑛𝑘𝑖𝑛𝑔 through the trained KGE models, with the purpose of measure the correspon-
dence between the discovered knowledge. Then, to assess the generated knowledge base, new
triples are added to the KGE models once trained the trustworthiness (i.e., how they are true) of
these triples. These new triples are called "synthetic", because they are manually generated,
are used to perform link predictions queries of the form 𝑇 = (?, 𝑃, ?). The synthetic triples
are divided into two groups, positive (𝑆𝑇𝑃 𝑜𝑠 , existing) and negatives (𝑆𝑇𝑁 𝑒𝑔 , not existing, or
unlikely to be true). Known learning-to-rank metrics to assess the performance of KGE models
are as follows.
|𝑄|
1 ∑︁ 1 |𝑞 ∈ 𝑄 : 𝑞 < 𝑛|
MRR = (1) HITS@N = (2)
|𝑄| 𝑞 |𝑄|
𝑖=1
The Mean Reciprocal Rank (MRR) is a statistical measure for assessing the quality of a list
of possible answers to a sample of queries, sorted by probability of correctness. MRR assume
value equal to 1 if a relevant answer was retrieved at rank 1; the intention is to measure the
correctness of each of the proposed scenarios using 𝑀 𝑅𝑅 to validate source knowledge against
the validation dataset. The 𝐻𝐼𝑇 𝑆@𝑁 measures the percent of triples that were correctly
ranked in the top 𝑁 . Besides, the combination with 𝐻𝐼𝑇 𝑆, with two different coefficients
𝑁 = 5 and 𝑁 = 3 to measure the ranking of the entities part of the statement (S, O) in the
validation dataset at the time of link prediction.

5. Experiment Design and Results
5.1. Triple Discovery
For the validation of the KBC process, let us focus on the KB generated by the Triple Construction
component described in Section 3.1. After the basic POS tagging tasks, the three sub-tasks,
6
Github Repository - https://github.com/d1egoprog/Text2KG
�Information Extraction, Chunking Rules and Tagging Pattern Execution generates statements for
the graph construction, the statements are grouped in 𝑇𝐼𝐸 , 𝑇𝑃 𝑎𝑡𝑡𝑒𝑟𝑛 , 𝑇𝐶ℎ𝑢𝑛𝑘𝑖𝑛𝑔 , respectively.
The triples generated by using the Information Extraction (𝑇𝐼𝐸 ) are approximately 75% of
the discovered triples. The remaining 25% can be split between the triples (about 10% of the
total KB generated) coming from the Chunking Rules (𝑇𝐶ℎ𝑢𝑛𝑘𝑖𝑛𝑔 ) and those (around 15%) from
Tagging Pattern components (𝑇𝑃 𝑎𝑡𝑡𝑒𝑟𝑛 ). Overlapping statements represent 1% of the triples
discovered.

5.2. KG Completion evaluation approach
Table 2 shows the results of the evaluation process, by pair-wise comparison of the statements
generated by the components and tasks, as described in Section 4.1. The first two columns of
the table show, in fact, the set of triples (statements) generated by two data sources, compared
with each other. In other words, the first column represent the reference triple set that is used
to validate the triple set from the second column. Then, there are the presented metrics and the
relative values obtained for each KGE model used, for the three KGE models analyzed.
Let us notice that in general there is an important dependence between the ranked triple list
compared with the expected ordering of the reference triple set for most of pairs reported in the
table. The worst MRR values are returned for the ranked triples 𝑇𝑅𝐸𝑆−𝑃 𝑎𝑡𝑡𝑒𝑟𝑛 returned by all
the three models, with respect to 𝑇𝑅𝐸𝑆−𝐶ℎ𝑢𝑛𝑘 and vice-versa. This is due to the small amount
of triples generated by the corresponding tasks, compared to the equally small reference dataset.
The highest scores of the 𝑀 𝑅𝑅 are instead returned for the number of triples generated by using
the Information Extraction (𝑇𝐼𝐸 ) and even for the Chunking Rules task, namely 𝑇𝑅𝐸𝑆−𝐶ℎ𝑢𝑛𝑘 .
The percentage of the top three and five ranked triples are also shown in the table, confirming
that using triples from the Information Extraction task (𝑇𝐼𝐸 ) for both reference and validation
often produces good triple rankings. To consider the validation of the external knowledge, the
last rows of the table show the result of the comparison with the synthetic triples 𝑆𝑇𝑃 𝑜𝑠 and
𝑆𝑇𝑁 𝑒𝑔 .
The results suggest that the validation of the synthetic triples produces high values (about
0.95) of 𝑀 𝑅𝑅 for the positive 𝑆𝑇𝑃 𝑜𝑠 triples and 0 for the negative 𝑆𝑇𝑃 𝑜𝑠 triples. The synthetic
statements appear to accurately reflect the knowledge discovered by the methodology and the
correspondence implementation, and the positive statements are potential candidates to feed
into the existing knowledge base.

6. Conclusions and Future Work
This paper proposes unsupervised knowledge construction from domain-specific knowledge
given a natural language text. In particular, a case study is designed on a narrative text, exploiting
features unique to prosaic text. The text was parsed and basic triples, representing the core
part (subject, predicate and object) of the main sentences are extracted. By Semantic Web
technologies, the triple are semantically annotated to be linked and navigable into the Linked
Data cloud. At that point, Knowledge graph embedding models have been exploited to extend
the potential of the collected triples by verifying and validating the so generated knowledge.
Performance is assessed by cross-validation, scoring functions and accuracy on unseen triples.
�Table 2
KG Completition approach results, exploiting the three KGE models TransE, DistMult, ComplEX. Src is
the reference data; Val. are the data to validate with Src. All the KB are the final 𝑇𝑅𝐸𝑆 for each process
TransE DistMult ComplEx
Src. Val. MRR H@3 H@5 MRR H@3 H@5 MRR H@3 H@5
𝑇𝐼𝐸 𝑇𝐶𝐻 0.69 0.53 0.85 0.63 0.58 0.71 0.65 0.59 0.71
𝑇𝐼𝐸 𝑇𝑃 𝑇 0.55 0.40 0.74 0.48 0.41 0.56 0.55 0.47 0.65
𝑇𝐶𝐻 𝑇𝐼𝐸 0.53 0.42 0.67 0.52 0.48 0.57 0.57 0.54 0.59
𝑇𝐶𝐻 𝑇𝑃 𝑇 0.33 0.28 0.42 0.30 0.28 0.31 0.33 0.31 0.36
𝑇𝑃 𝑇 𝑇𝐼𝐸 0.54 0.40 0.70 0.53 0.47 0.58 0.55 0.49 0.61
𝑇𝑃 𝑇 𝑇𝐶𝐻 0.34 0.28 0.42 0.33 0.31 0.33 0.29 0.28 0.31
TransE DistMult ComplEx
Src. Val. MRR H@1 H@3 MRR H@1 H@3 MRR H@1 H@3
𝑇𝐾𝐵 𝑆𝑇𝑃 𝑜𝑠 0.95 0.92 0.99 0.92 0.85 0.99 0.99 0.98 0.99
𝑇𝐾𝐵 𝑆𝑇𝑁 𝑒𝑔 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01

The work provides a general methodological approach to automatically build a knowledge
base, on a preferred domain, given in textual resources. It allows the creation of different types
of KBs. It can be leveraged to build ad-hoc knowledge bases, tailored to a specific domain.
Question-answering systems can act as validation systems when verification of the veracity
and reliability (degree of truth) of a given statement is required, for example, for the discovery
of fake news or just for fact-checking.
As future work, the generation and enrichment of triples by querying existing Open Knowl-
edge bases will be improved, along with the more challenging goal of enhancing the degree of
confidence in the KB by implementing an automatic validation method.

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FAIR Knowledge Graph construction from text, an approach applied to fictional novels[edit]

Keywords
Knowledge Graph, Natural Language Processing, Semantic Web Technologies, Knowledge Graph Em-
beddings, FAIR

Figure 3: Directed Acyclic Graph (DAG) describing the Tagging Pattern task

starting by the detected entities. Let us remark that the named entities has a crucial role in the
triple identification and generation.