A multi-layer framework for semantic modeling

Publication Date17 Dec 2019
AuthorSergio Evangelista Silva,Luciana Paula Reis,June Marques Fernandes,Alana Deusilan Sester Pereira
subjectMatterLibrary & information science,Records management & preservation,Document management,Classification & cataloguing,Information behaviour & retrieval,Collection building & management,Scholarly communications/publishing,Information & knowledge management,Information management & governance,Information management,Information & communications technology,Internet
A multi-layer framework for
semantic modeling
Sergio Evangelista Silva
Engenharia de Produção,
Universidade Federal de Ouro Preto Campus Joao Monlevade,
João Monlevade, Brazil, and
Luciana Paula Reis, June Marques Fernandes and
Alana Deusilan Sester Pereira
Engenharia de Produção,
Universidade Federal de Ouro Preto, João Monlevade, Brazil
Purpose The purpose of this paper is to introduce a multi-level framework for semantic modeling (MFSM)
based on four signification levels: objects, classes of entities, instances and domains. In addition, four
fundamental propositions of the signification process underpin these levels, namely, classification,
decomposition, instantiation and contextualization.
Design/methodology/approach The deductive approach guided the design of this modeling framework.
The authors empirically validated the MFSM in two ways. First, the authors identified the signification
processes used in articles that deal with semantic modeling. The authors then applied the MFSM to model the
semantic context of the literature about lean manufacturing, a field of management science.
Findings The MFSM presents a highly consistent approach about the signification process, integrates the
semantic modeling literature in a new and comprehensive view; and permits the modeling of any semantic
context, thus facilitating the development of knowledge organization systems based on semantic search.
Research limitations/implications The use of MFSM is manual and, thus, requires a considerable effort
of the team that decides to model a semantic context. In this paper, the modeling was generated by specialists,
and in the future should be applicated to lay users.
Practical implications The MFSM opens up avenue s to a new form of classifi cation of documents,
as well as for the develop ment of tools based on the se mantic search, and to inv estigate how users do
their searches.
Social implications The MFSM can be used to model archives semantically in public or private settings.
In future, it can be incorporated to search engines for more efficient searches of users.
Originality/value The MFSM provides a new and comprehensive approach about the elementary levels
and activities in the process of signification. In addition, this new framework presents a new form to model
semantically any context classifying its objects.
Keywords Semantics, Semiotics, Information management, Knowledge management,
Conceptual framework, Document management, Knowledge organization systems
Paper type Research paper
1. Introduction
Knowledge management has been a crucial issue in the efficiency and efficacy of different
types of organizations (Flett, 2012). The knowledge management process has been of central
interest to knowledge-organization systems (KOS), which permit the description, indexation,
ordering and posterior retrieval of documents in different contexts (Hider, 2006, 2017;
Hjørland, 2003, 2008). The basic purpose of these systems is to afford the user higher
efficiency in their search for documents (Bošnjak and Podgorelec, 2016; Hjørland, 2019;
Jindal et al., 2014; Li et al., 2017).
Journal of Documentation
Vol. 76 No. 2, 2020
pp. 502-530
© Emerald PublishingLimited
DOI 10.1108/JD-03-2019-0042
Received 4 March 2019
Revised 26 September 2019
Accepted 6 October 2019
The current issue and full text archive of this journal is available on Emerald Insight at:
The authors would like to thank Fundação de Amparo a Pesquisa do Estado de Minas Gerais
(FAPEMIG) to support this project (Grant Number APQ-01618-14) and Universidade Federal de Ouro
Preto (TO: 072018111).
Due to the great progress in computational algorithms over recent decades, search tools
are designed based more from the computer-science perspective than from the information-
science perspective (Hjørland, 2016). Currently, the main search engines (e.g. internet search
engines, scientific databases and so on) enable searching based on the orthographical search
paradigm through word match (Castano et al., 2018). These engines also use instruments
such as bibliometric, statistical and probabilistic methods to rank and retrieve information
(Hjørland, 2008, 2016; Schneider and Borlund, 2004).
Despite the importance of computer science in the development of tools for information
retrieval, information science has over time also provided reference models that have
become efficient means for retrieving information. Some examples are CIDOC-CRM a
reference model applied to model information in cultural heritage settings (Farrokhnia, 2013;
ICM, 2019); FBRB/RDA a model dedicated to modeling library resources (Hider, 2009,
2017; Hider and Liu, 2013); faceted analysis an approach tailored to characterize and
contextualize objects[1] (Hjørland, 2013); hierarchical models that enable contextualization
of objects and establishing relations between them in a domain[2] (Bikakis et al., 2017;
Kamdar et al., 2018; Zhang et al., 2016; Beydoun et al., 2011); conceptual maps, ontologies and
thesauri that are schema for high-level abstraction and characterization of domains
(Hjørland, 2008, 2016; Schneider and Borlund, 2004; Shiri et al., 2002).
Comparing the current computational tools with certain reference models of the
information science field, we may conclude that the former encompasses a low level of
abstraction, a level focused on searching for documents based on their basic referential
terms without contextualization of their respective domains. On the other hand, reference
models, such as hierarchical models, ontologies and conceptual maps, permit the modeling
of elements of a domain that we regard as a high-level perspective (Annane et al., 2018;
Arnold and Rahm, 2014; Beghtol, 1986; Bikakis et al., 2017), given that they present
general language terms, which provide the overall semantic relationship between the
elements of a domain.
Despite the basic denomination of documents of a domain being based on their names
and categorical terms, the identification of the subjects of a given document is a useful
strategy for its posterior retrieval (Hjørland, 2001). Accordingly, an alternative path for the
development of information-retrieval tools is the development of semantic models and
search engines (Erfani et al., 2016), designed to be capable of locating documents based on
their relative position in a given domain (Bastos et al., 2018; Jin and Claramunt, 2018;
Tallerås et al., 2018).
Independently of the search paradigm, the user should play a central role in the search
process (Burke, 2018; Eerola and Vakkari, 2008; Gorrel et al., 2009; Jindal et al., 2014;
Shiri et al., 2002). Conversely, further research about semantic models and search tools can
provide considerable benefits to users in their daily searches. Despite the development of
semantic models, several gaps persist in the literature, including an absence of models that
identify the levels of the semantic modeling, thus linking objects with the several domains;
and an absence of a general view of the processes of signification, which hampers the
classification and further development of the current semantic models.
Based on these research gaps, we outline the following research questions:
RQ1. What are the levels of semantic modeling capable of linking objects (physical and
abstract) with the domains to which they are related?
RQ2. What are the processes for the construction of meaning through these levels?
This research, thus, provides us with the opportunity to introduce a multi-level framework
for semantic modeling (MFSM) based on four modeling levels: objects, classes of entities,
instances and domains. Moreover, four fundamental propositions of the signification
process support these levels, based on the processes of classification, decomposition,
framework for

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