Conference papers accepted
24/06/21/10:10 Filed in: Conference papers
D. López-Rodríguez, P. Cordero, M. Enciso, Á. Mora. How to provide light to COVID data by means of FCA. RealDataFCA workshop (jointly with ICFCA'21), 2021.
ABSTRACT COVID data are usually presented in a non-structured format and mainly focused on healthy issues (incidence, mortality, etc). At the same time, Governments have designed a set of measures to deal with the Pandemic. In addition, several institutions have studied the economical effects of the situation in each country. In this work, we combine these three data sources and illustrate how Formal Concept Analysis can become a useful tool to discover relationships among these three views of the situation: health, politics and economy. Our aim is to provide an implication-driven approach to discover knowledge behind the data.
P. Cordero, M. Enciso, D. López-Rodríguez, Á. Mora. Topic Modelling in Social Networks with Formal Concept Analysis. Computational Mathematical Methods in Science and Engineering, Rota, 2021.
ABSTRACT In the age of social networks, the amount of the written material published every day exceeds our processing capacity. Topic models can help to organise and to understand extensive collections of unstructured text documents. In machine learning and natural language processing, a topic model is a statistical model for discovering the abstract ``topics'' in a collection of documents, uncovering hidden semantic structures and clusters of similar words.
To approach topic modelling in social networks, we use Formal Concept Analysis, a mathematical tool firmly based on lattice theory and logic. Our approach uses the knowledge contained in the concept lattice to extract the topics. Thus, this approach to topic modelling is not statistical. For example, we do not need to assume a prior distribution of terms. Instead, the actual data structure is used to infer the semantic relationships between attributes.
An experiment with a dataset with tweets about some hashtags is conducted with our approach to show how Formal Concept Analysis can be used in Social Network Analysis. In addition, a comparison with classical techniques is being addressed.
N. Madrid, M. Ojeda-Aciego. Residuated structures via the f-index of inclusion. Computational and Mathematical Methods in Science and Engineering, Rota, 2021.
ABSTRACT The origin of the $f$-index of inclusion can be dated back to the incorporation of negation connectives in multi-adjoint logic programs and, hence, to study inconsistency and, its sibling, coherence. As a result, it was clear that (in-)consistency should not be considered as a crisp notion when applied in (general) fuzzy logic theories, and different approaches were proposed for this goal. We introduced the notion weak-contradiction as a generalization of the notion of coherence in the general framework of fuzzy set theory. Soon after introducing measures for weak-contradiction, we started to imagine some kind of function-based approach to measuring the inclusion between fuzzy sets, and presented the first ideas about the $f$-index of inclusion. We have recently recovered the idea of relating the two research lines emerged in parallel, namely the weak-contradiction and the $f$-index of inclusion, with satisfactory results.
In this work we show that the $f$-index of inclusion is very related to fuzzy implications and, in fact, three different residuated structures can be obtained.
ABSTRACT COVID data are usually presented in a non-structured format and mainly focused on healthy issues (incidence, mortality, etc). At the same time, Governments have designed a set of measures to deal with the Pandemic. In addition, several institutions have studied the economical effects of the situation in each country. In this work, we combine these three data sources and illustrate how Formal Concept Analysis can become a useful tool to discover relationships among these three views of the situation: health, politics and economy. Our aim is to provide an implication-driven approach to discover knowledge behind the data.
P. Cordero, M. Enciso, D. López-Rodríguez, Á. Mora. Topic Modelling in Social Networks with Formal Concept Analysis. Computational Mathematical Methods in Science and Engineering, Rota, 2021.
ABSTRACT In the age of social networks, the amount of the written material published every day exceeds our processing capacity. Topic models can help to organise and to understand extensive collections of unstructured text documents. In machine learning and natural language processing, a topic model is a statistical model for discovering the abstract ``topics'' in a collection of documents, uncovering hidden semantic structures and clusters of similar words.
To approach topic modelling in social networks, we use Formal Concept Analysis, a mathematical tool firmly based on lattice theory and logic. Our approach uses the knowledge contained in the concept lattice to extract the topics. Thus, this approach to topic modelling is not statistical. For example, we do not need to assume a prior distribution of terms. Instead, the actual data structure is used to infer the semantic relationships between attributes.
An experiment with a dataset with tweets about some hashtags is conducted with our approach to show how Formal Concept Analysis can be used in Social Network Analysis. In addition, a comparison with classical techniques is being addressed.
N. Madrid, M. Ojeda-Aciego. Residuated structures via the f-index of inclusion. Computational and Mathematical Methods in Science and Engineering, Rota, 2021.
ABSTRACT The origin of the $f$-index of inclusion can be dated back to the incorporation of negation connectives in multi-adjoint logic programs and, hence, to study inconsistency and, its sibling, coherence. As a result, it was clear that (in-)consistency should not be considered as a crisp notion when applied in (general) fuzzy logic theories, and different approaches were proposed for this goal. We introduced the notion weak-contradiction as a generalization of the notion of coherence in the general framework of fuzzy set theory. Soon after introducing measures for weak-contradiction, we started to imagine some kind of function-based approach to measuring the inclusion between fuzzy sets, and presented the first ideas about the $f$-index of inclusion. We have recently recovered the idea of relating the two research lines emerged in parallel, namely the weak-contradiction and the $f$-index of inclusion, with satisfactory results.
In this work we show that the $f$-index of inclusion is very related to fuzzy implications and, in fact, three different residuated structures can be obtained.
Conference paper accepted
18/06/21/13:30 Filed in: Conference paper
N. Madrid, M. Ojeda-Aciego. Measuring Consistency of Fuzzy Logic Theories. Proc. of the Spanish Conference on Fuzzy Logic and Technology, Málaga, 2021.
ABSTRACT Fuzzy logic has shown to be a suitable framework to handle contradictions in which, unsurprisingly, the notion of inconsistency can be defined in different ways. This paper starts with a short survey of different ways to define the notion of inconsistency in fuzzy logic systems. As a result, we provide a first notion of inconsistency by means of the absence of models. Subsequently, we define two measures of consistency that belong purely to the fuzzy paradigm; in the sense that both measures coincide with the crisp notion of consistency when the set of truth values is $\{0,1\}$. Accordingly, we can state that the two provided measures of consistence are notions of consistence based on degrees, bringing back the spirit of fuzzy logic into the notion of consistency.
ABSTRACT Fuzzy logic has shown to be a suitable framework to handle contradictions in which, unsurprisingly, the notion of inconsistency can be defined in different ways. This paper starts with a short survey of different ways to define the notion of inconsistency in fuzzy logic systems. As a result, we provide a first notion of inconsistency by means of the absence of models. Subsequently, we define two measures of consistency that belong purely to the fuzzy paradigm; in the sense that both measures coincide with the crisp notion of consistency when the set of truth values is $\{0,1\}$. Accordingly, we can state that the two provided measures of consistence are notions of consistence based on degrees, bringing back the spirit of fuzzy logic into the notion of consistency.