FCA-based knowledge representation and local generalized linear models to address relevance and diversity in diverse social images

Benavent, Xaro; Castellanos, Ángel; Ves, Esther de; Garcı́a-Serrano, Ana; Cigarrán, Juan

doi:10.1016/j.future.2019.05.029

Cited by 7 publications

(3 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The process ends when a stop criterion is reached, often the number k of clusters. At the end of the algorithm, a clustering dendrogram is created, which is a binary tree hierarchy [40,67,72,73]. A possible hierarchical clustering considering the spatial arrangement between samples 1-6 of Figure 6a is illustrated in Figure 6b.…”

Section: Hierarchical Algorithmsmentioning

confidence: 99%

Identifying Fake News on Social Networks Based on Natural Language Processing: Trends and Challenges

Oliveira

Pisa²,

Lopez

et al. 2021

Information

View full text Add to dashboard Cite

The epidemic spread of fake news is a side effect of the expansion of social networks to circulate news, in contrast to traditional mass media such as newspapers, magazines, radio, and television. Human inefficiency to distinguish between true and false facts exposes fake news as a threat to logical truth, democracy, journalism, and credibility in government institutions. In this paper, we survey methods for preprocessing data in natural language, vectorization, dimensionality reduction, machine learning, and quality assessment of information retrieval. We also contextualize the identification of fake news, and we discuss research initiatives and opportunities.

show abstract

Section: Hierarchical Algorithmsmentioning

confidence: 99%

Identifying Fake News on Social Networks Based on Natural Language Processing: Trends and Challenges

Oliveira

Pisa²,

Lopez

et al. 2021

Information

View full text Add to dashboard Cite

show abstract

“…For this reason, scholars have carried out a lot of research work and made remarkable achievements, such as TransE [15], [16], TransH [17], TransR [18], STransH [19], TransA [20] and so on. In these models, TransE is considered as the most potential model because of its simple operation, high computing efficiency [21], effective handling of multiple relational data problems, and wide application. But, we note that TransE does not do well in dealing with one-to-many, many-to-one, many-to-many relationships.…”

Section: Introductionmentioning

confidence: 99%

TransH-RA: A Learning Model of Knowledge Representation by Hyperplane Projection and Relational Attributes

et al. 2023

View full text Add to dashboard Cite

The TransE model plays a key role in dealing with data sparsity and promotes the development of knowledge graphs completion. However, TransE has some difficulties in dealing with oneto-many, many-to-one, many-to-many and transmission relationships. In order to solve this problem, this paper proposes a knowledge representation learning model based on hyperplane projection and relational attributes, namely TransH-RA. First of all, we introduce the idea of hyperplane projection based on the TransE model, this idea is inspired by TransH, which makes different entities have different roles in a specific relationship, thus reducing the constraints of TransE translation rules, and map the head entity h and tail entity t to the plane of special relation r; Secondly, considering that it is not easy to identify different similar entities, the neighborhood information of entities is added to learn the neighborhood of entities around different entities; Then, in order to further strengthen the ability to deal with complex relationships, attribute features of relationships are added and attribute knowledge is embedded; Eventually, during the training of the model, the probability method is chosen to replace the head and tail entities. Link prediction experiments are conducted on the public datasets FB15K and WN18, and the triple classification experiments on the datasets WN11, FB13 and FB15K are carried out to analyze and verify the effectiveness of the proposed method. The evaluation results show that our method achieves the better results on MeanRank, Hits@10 and ACC indicators compared with TransE and TransH.

show abstract

“…In general, a formal concept consists of extension and intension, and is the formal and mathematical description of concepts in philosophy. Since Wille [1] proposed formal concept analysis (FCA) for constructing lattice theory in 1982, FCA has been widely used in data analysis [2–4], information retrieval [5], data mining [6], medical diagnosis [7], machine learning [8] and many other fields [9, 10]. The semi‐concept proposed by Luksch and Wille [11] in 1991 is a branch of FCA, and the binary relation expressed by it is unidirectional, which makes any set of objects and attributes are semi‐concepts.…”

Section: Introductionmentioning

confidence: 99%

Rule acquisition of three‐way semi‐concept lattices in formal decision context

Zhao

Wan

Miao

et al. 2023

CAAI Trans on Intel Tech

View full text Add to dashboard Cite

Three‐way concept analysis is an important tool for information processing, and rule acquisition is one of the research hotspots of three‐way concept analysis. However, compared with three‐way concept lattices, three‐way semi‐concept lattices have three‐way operators with weaker constraints, which can generate more concepts. In this article, the problem of rule acquisition for three‐way semi‐concept lattices is discussed in general. The authors construct the finer relation of three‐way semi‐concept lattices, and propose a method of rule acquisition for three‐way semi‐concept lattices. The authors also discuss the set of decision rules and the relationships of decision rules among object‐induced three‐way semi‐concept lattices, object‐induced three‐way concept lattices, classical concept lattices and semi‐concept lattices. Finally, examples are provided to illustrate the validity of our conclusions.

show abstract

FCA-based knowledge representation and local generalized linear models to address relevance and diversity in diverse social images

Cited by 7 publications

References 17 publications

Identifying Fake News on Social Networks Based on Natural Language Processing: Trends and Challenges

Identifying Fake News on Social Networks Based on Natural Language Processing: Trends and Challenges

TransH-RA: A Learning Model of Knowledge Representation by Hyperplane Projection and Relational Attributes

Rule acquisition of three‐way semi‐concept lattices in formal decision context

Contact Info

Product

Resources

About