=Paper=
{{Paper
|id=Vol-3632/ISWC2023_paper_394
|storemode=property
|title=Text2AMR2FRED, a Tool for Transforming Text into RDF/OWL Knowledge Graphs via Abstract Meaning Representation
|pdfUrl=https://ceur-ws.org/Vol-3632/ISWC2023_paper_394.pdf
|volume=Vol-3632
|authors=Aldo Gangemi,Arianna Graciotti,Antonello Meloni,Andrea Nuzzolese,Valentina Presutti,Diego Reforgiato Recupero,Alessandro Russo,Rocco Tripodi
|dblpUrl=https://dblp.org/rec/conf/semweb/GangemiGMNPR0T23
}}
==Text2AMR2FRED, a Tool for Transforming Text into RDF/OWL Knowledge Graphs via Abstract Meaning Representation==
https://ceur-ws.org/Vol-3632/ISWC2023_paper_394.pdf
Text2AMR2FRED, a Tool for Transforming Text into
RDF/OWL Knowledge Graphs via Abstract Meaning
Representation⋆
Aldo Gangemi1 , Arianna Graciotti2,∗ , Antonello Meloni3 , Andrea Nuzzolese4 ,
Valentina Presutti2 , Diego Reforgiato Recupero3 , Alessandro Russo4 and
Rocco Tripodi2
1
Department of Philosophy and Communication Studies, University of Bologna, 40126 Bologna, Italy
2
Department of Modern Languages, Literatures, and Cultures, University of Bologna, 40126 Bologna, Italy
3
Department of Mathematics and Computer Science, University of Cagliari. Via Ospedale 72, 09124 Cagliari, Italy
4
Institute of Cognitive Sciences and Technologies, CNR, via San Martino della Battaglia 44, 00185, Rome, Italy
Abstract
This paper presents Text2AMR2FRED, a text-to-Knowledge Graph (KG) pipeline that transforms mul-
tilingual natural language text into logically sound KGs. It enables at-scale information retrieval and
knowledge extraction. This pipeline overcomes the lax logic and interoperability challenges faced by
existing semantic parsers and machine readers. Adhering to Semantic Web standards, it transforms text
content into structured knowledge and enriches it by exploiting external knowledge.
Keywords
Knowledge Graphs, Abstract Meaning Representation, Natural Language Processing, Semantic Frames
1. Introduction
Transforming natural language text into logically sound Knowledge Graphs (KGs) supports
at-scale information retrieval from collections of texts.
Natural Language Processing (NLP) and Semantic Web (SW) communities dedicated signif-
icant effort to text-to-KG pipelines. The NLP community exploited the progress of Machine
Learning (ML) and Neural Networks (NN) to improve semantic parsing. Graph-based semantic
parsing has gained attention due to the potential of general-purpose representations, such as
Abstract Meaning Representation (AMR) [1]. Text-to-AMR transduction based on neural ma-
chine translation and sequence-to-sequence (seq2seq) models achieved promising results both in
scenarios limited to English and AMR parsing (SPRING, [2], Transition-based AMR parsing [17])
and in multilingual [3] and multi-formalisms scenarios (SGL, [14]). However, neural semantic
ISWC 2023 Posters and Demos: 22nd International Semantic Web Conference, November 6–10, 2023, Athens, Greece
∗
Corresponding author.
Envelope-Open aldo.gangemi@unibo.it (A. Gangemi); arianna.graciotti@unibo.it (A. Graciotti); antonello.meloni@unica.it
(A. Meloni); andrea.nuzzolese@istc.cnr.it (A. Nuzzolese); valentina.presutti@unibo.it (V. Presutti);
diego.reforgiato@unica.it (D. R. Recupero); alessandro.russo@istc.cnr.it (A. Russo); rocco.tripodi@unibo.it
(R. Tripodi)
© 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
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parsers struggle with making the extracted knowledge interoperable and exploitable due to its
formalisms’ balkanisation [11] and lax logic.
The SW provides means to formally represent the extracted knowledge according to interop-
erable ontologies, therefore favouring knowledge augmentation with heterogeneous Knowledge
Bases (KBs) and alignment with other ontologies. The SW machine reader FRED [6] encodes
the extracted information using Semantic Web (SW) standards. The resulting KGs enable the
exploration and retrieval of facts extracted from heterogeneous text corpora through struc-
tured queries, as well as their augmentation through alignment to other KGs. This alignment
supports the disclosure of explicit knowledge that would otherwise remain hidden in texts.
However, FRED relies on cumbersome NLP pipelines, hard to maintain and unsuitable to scale
in multilingual scenarios.
To overcome such limitations, this paper presents Text2AMR2FRED1 , a revised architecture
of FRED’s text-to-KG pipeline. It exploits pre-trained end-to-end text-to-AMR parsers, which
mitigate error propagation typical of component-based pipelines. In fact, thanks to AMR’s gen-
eralization of lexical and syntactic variations, it allows a more abstract and robust representation
of text, without employing ad hoc data augmentation strategies, such as lexical substitution
[9, 8]. Furthermore, the SotA AMR parser’s multilingual capabilities enhance the scalability
of our application, expanding its reach beyond the original English-only input restriction of
FRED’s NLP pipeline.
2. Text2AMR2FRED at work
Figure 1: Graph resulting from the text2AMR parsing powered by SPRING [2] of the sentence Apple
unveils revolutionary watch obtained via Text2AMR2FRED WebApp1 .
Text2AMR2FRED implements a pipeline to produce KGs automatically from unstructured text.
These KGs are event-centric, as they rely on PropBank Frames 2 [13]. The process for generating
a KG from an input text relies on two modules: (1) the text-to-AMR parsing module, which takes
natural language sentences as input and transforms them into AMR graphs. Sentences in English
1
https://arco.istc.cnr.it/txt-amr-fred/
2
PropBank Frames are the core lexicon of the PropBank paradigm and consist of predicate-argument structures
named “rolesets”. A complete list of PropBank frames can be found at http://propbank.github.io/v3.4.0/frames/
Figure 2: Graph resulting from the AMR2FRED translation powered by AMR2FRED of the sentence
Apple unveils revolutionary watch obtained via Text2AMR2FRED WebApp1 .
are parsed by SPRING3 [2]. Sentences in other languages are parsed by USeA4 [12], which
takes input in 100 languages. (2) the AMR-to-FRED translation, which extends the AMR2FRED5
tool [10] to transform AMR graphs into OWL-compliant RDF KGs, following FRED’s theoretical
model [6]. The integration of the two modules is eased and streamlined by the APIs provided
by both tools. Specifically, the AMR graph produced by the text-to-AMR parsers from the input
text can be directly used as input for the AMR2FRED tool to get a corresponding KG. This makes
it possible for tools such as the Machine Reading suite6 to query both components through
the Text-to-AMR-to-FRED APIs7 and generate RDF named graphs from input text sentences or
paragraphs in batches. Text2AMR2FRED is also released to the public via a user-friendly web
app1 .
The AMR-to-FRED translation facilitates KGs enrichment, which can be achieved by em-
ploying Framester [5]. Thanks to Framester, additional relevant knowledge missing in the text
(e.g., common sense knowledge) can be recovered from other KBs such as WordNet8 , DBPedia9 ,
DOLCE-Zero10 . For example, the output KGs are enriched through Word Sense Disambiguation
(WSD) based on the RDF version of WordNet, included in Framester. The WSD process is applied
to AMR elements (usually nouns and adjectives) that miss links to lexical resources. Figure
1 shows the AMR graph corresponding to the sentence “Apple unveils revolutionary watch”.
The reader may notice that the text-to-AMR parser associates predicates in AMR graphs with
PropBank word senses and Named Entities with their corresponding entities in Wikipedia. The
node z3 / watch instead is missing a link to lexical resources. Therefore, we disambiguate it
against Framester. The WSD process consists of submitting the original sentence to EWISER11 , a
WSD system well-suited for multilingual scenarios due to its SotA performance in both all-words
English WSD and multilingual WSD tasks. As Figure 2 shows, we associate the result of WSD
(WordNet’s synsets) with the AMR nodes missing links to any external source and whose label
3
http://nlp.uniroma1.it/spring/
4
https://github.com/SapienzaNLP/usea
5
https://github.com/infovillasimius/amr2Fred/tree/master
6
https://github.com/anuzzolese/machine-reading
7
http://framester.istc.cnr.it/txt-amr-fred/api/docs
8
https://wordnet.princeton.edu
9
https://www.dbpedia.org
10
http://www.ontologydesignpatterns.org/ont/d0.owl
11
https://github.com/SapienzaNLP/ewiser
corresponds to the lemma of the input sentence. This association is implemented through the
owl:equivalentClass property between the identified node and the selected WordNet’s synset
in Framester. For the example above, we use EWISER and keep the information for the lemma
”watch”. For the same entities (those not linked with external information sources), we further
exploit Framester to generate alignments to two top-level ontologies: WordNet ”supersenses”
(through the rdfs:subclassOf property) and a subset of DOLCE+DnS Ultra Lite (DUL) classes.
3. Conclusions and Future Work
Text2AMR2FRED is a tool that mitigates the issues of existing NLP semantic parsers and machine
readers, adhering to Semantic Web standards to ensure interoperable knowledge extraction.
It enhances the informativeness of KGs by aligning them with domain-specific ontologies,
enabling interrogation through structured queries. This approach uncovers implicit knowledge
from text, enabling the output of KGs with external KBs.
Future work will focus on creating resources for the evaluation of the tool. The AMR parsers
employed in our tool can be leveraged to perform AMR-to-text tasks and compare the original
textual excerpts to the automatically generated ones via a back-translation [16] approach. This
allows the calculation of similarity metrics, such as BLEURT [15] or others, between the original
and generated texts. Under the hypothesis that generated sentences with (relatively) high
similarity scores correspond to high-quality AMR graphs, automatic filters can be designed
and applied to prevent lower-quality AMR graphs’ transformation into RDF/OWL KGs. Our
evaluation method will be completed by the analysis of Motifs, basic logical patterns employed
in SW, defined in [7], in the output KGs. The Motifs-based validation will permit a cross-tools
knowledge extraction tasks comparative evaluation, following the method outlined in [4].
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