=Paper=
{{Paper
|id=Vol-2980/paper418
|storemode=property
|title=Boosting Information
Extraction through Semantic Technologies: The KIDs use case at
CONSOB
|pdfUrl=https://ceur-ws.org/Vol-2980/paper418.pdf
|volume=Vol-2980
|authors=Federico Scafoglieri, Domenico Lembo, Alessandra Limosani, Francesca Medda, Maurizio Lenzerini
|dblpUrl=https://dblp.org/rec/conf/semweb/ScafoglieriLLML21
}}
==Boosting Information
Extraction through Semantic Technologies: The KIDs use case at
CONSOB==
https://ceur-ws.org/Vol-2980/paper418.pdf
Boosting Information Extraction through Semantic
Technologies: The KIDs use case at CONSOB
Federico Maria Scafoglieri1 , Domenico Lembo1 , Alessandra Limosani2 ,
Francesca Medda2 , and Maurizio Lenzerini1
1
Sapienza Università di Roma
{lembo,lenzerini,scafoglieri}@diag.uniroma1.it
2
Commissione Nazionale per le Società e la Borsa
{a.limosani,f.medda}@consob.it
In this paper we report on the initial results of a project concerning the integration of
Semantic Technologies with Information Extraction (IE) techniques, jointly carried out
by Sapienza University of Rome and C ONSOB (Commissione Nazionale per la Società
e la Borsa), the Italian public authority responsible for regulating the securities market.
The use case. In the EU, the creators of financial products (a.k.a. financial manufac-
turers) are obliged by law3 to make information related to so-called PRIIPs (Packaged
Retail Investment and Insurance-based Investments Products) publicly available. The
NCAs (National Competent Authorities) have supervisory duties on such products, so
that they can be safely placed on the respective national markets. The legislation re-
quires information about PRIIPs to be communicated to NCAs through documents
called KIDs (Key Information Documents). In the practice, this means that features
to be checked are cast into text reports, typically formatted as pdf files, and extracting
structured data from them (to bootstrap control activities), is actually in charge to the
authority (In Italy, C ONSOB). Due to the massive amount of documents to be analyzed
(e.g., ∼700.000 KIDs received by C ONSOB in 2019, more than 1 million in 2020), this
process cannot be carried out manually, but still it is only partially automated to date.
Objectives. Our main aim is thus to develop a solution to streamline the extraction
process and reduce as much as possible (ideally eliminate) the need of manual inter-
vention, still guaranteeing very high accuracy. At the same time, such solution should
return a data structure providing a due account of the semantics of the business domain
and suited for rich and highly informative post-extraction analysis.
Solution. Given the previously highlighted requirements, the proposed solution aims
at constructing a Knowledge Graph (KG), whose intensional component (expressed in
OWL) is designed with the help of domain experts, and whose extensional level is auto-
matically created from KIDs through a rule-based IE mechanism. The choice of struc-
turing the extracted data as a KG not only facilitates the integration with other corporate
and external data, enabling rich analysis and management at an abstract, conceptual
level, but also allows for properly formalizing the conceptual distinction between PRI-
IPs and KIDs describing them, and the continuous updates which KIDs are subjected to.
Copyright c 2021 for this paper by its authors. Use permitted under Creative Commons Li-
cense Attribution 4.0 International (CC BY 4.0).
3
PRIIPs Regulation n. 1286/2014
Moreover, the choice to adopt a rule-based approach for IE, instead of a statistical one,
lies not only in the great effort required for generating an annotated dataset for training
learning algorithms, but also in the lack of transparency, accountability, and human in-
terpretability of Machine Learning solutions, which makes excessively difficult to fully
understand the results of the extraction, ill-fitting the financial context of this use case.
In our first implementation efforts, we focused on a portion of the information to be
extracted, consisting of 12 PRIIPs characteristics (e.g., name of the product, issue date,
etc.). We realized two alternative implementations, briefly described below.
First realization. We initially adopted CoreNLP [3], the popular Stanford library for
NLP, which is well-documented, fully-supported, and easy-to-use, and provides a rule
engine module, called TokenRegex, useful to generate annotations on text via a regex-
like rule language. After applying the rules, the generated annotations follow a flow
of transformations, realized through components specifically written to translate anno-
tations into facts of the KG. Although the results of our experiments are particularly
convincing in terms of precision and recall, both averaging around 99% over a dataset
of more than 14.000 KIDs, we encountered two main issues: (i) low performance in
terms of execution time; (ii) complex process to define rules, caused by the low modu-
larity of TokenRegex and the need of complementing rules with ad-hoc (java) code.
Second realization. We have therefore realized a second implementation through
M ASTRO S YSTEM -T [2], a KG-aided IE tool, which allowed us to solve the above
issues, still achieving the same accuracy results. As for issue (i), the extraction speed
has increased considerably, reducing the time needed to materialize the KG by 46.15%,
mainly thanks to the use in M ASTRO S YSTEM -T of the highly performing IE tool
System-T [1]. Issue (ii) has been instead greatly mitigated, by virtue of both the full
declarativeness of the language used for the extractors in System-T, and the way in
which M ASTRO S YSTEM -T casts them into extraction assertions mapping KIDs to KG
predicates. This second implementation allowed us also to follow a new approach to ac-
cess KIDs data. Indeed, M ASTRO S YSTEM -T may be used as a Virtual KG engine [4],
to perform the extraction at query time, which allows to always get fresh data.
Conclusion. Since all European NCAs need to address the same oversight tasks on PRI-
IPs as C ONSOB, the impact of our research may go fairly beyond the single experience
we described, considered also that, to the best of our knowledge, very few authorities
have to date developed solutions supporting automatic IE from KIDs. In particular, the
adoption of our approach by other authorities is enabled by the fact that both KIDs
content and structure must obey to the same common regulation.
References
1. L. Chiticariu, M. Danilevsky, Y. Li, F. Reiss, and H. Zhu. SystemT: Declarative text under-
standing for enterprise. In Proc. of NAACL-HLT (Industry Papers), pages 76–83, 2018.
2. D. Lembo, Y. Li, L. Popa, K. Qian, and F. Scafoglieri. Ontology mediated information extrac-
tion with MASTRO SYSTEM-T. In Proc. of ISWC (Demos Track), pages 256–261, 2020.
3. C. D. Manning, M. Surdeanu, J. Bauer, J. R. Finkel, S. Bethard, and D. McClosky. The
Stanford CoreNLP natural language processing toolkit. In Proc. of ACL, pages 55–60, 2014.
4. G. Xiao, D. Calvanese, R. Kontchakov, D. Lembo, A. Poggi, R. Rosati, and M. Zakharyaschev.
Ontology-based data access: A survey. In Proc. of IJCAI, pages 5511–5519, 2018.