Skyline refinement exploiting fuzzy formal concept analysis

Purpose-The study of the skyline queries has received considerable attention from several database researchers since the end of 2000’s.Skyline queries are an appropriate tool that can help users to make intelligent decisions in the presence of multidimensional data when different,and often contradictory criteria are to be taken into account.Based on the concept of Pareto dominance,the skyline process extracts the most interesting(not dominated in the sense of Pareto)objects from a set of data.Skyline computation methods often lead to a set with a large size which is less informative for the end users and not easy to be exploited.The purpose of this paper is to tackle this problem,known as the large size skyline problem,and propose a solution to deal with it by applying an appropriate refining process.Design/methodology/approach-The problem of the skyline refinement is formalized in the fuzzy formal concept analysis setting.Then,an ideal fuzzy formal concept is computed in the sense of some particular defined criteria.By leveraging the elements of this ideal concept,one can reduce the size of the computed Skyline.Findings-An appropriate and rational solution is discussed for the problem of interest.Then,a tool,named SkyRef,is developed.Rich experiments are done using this tool on both synthetic and real datasets.Research limitations/implications-The authors have conducted experiments on synthetic and some real datasets to show the effectiveness of the proposed approaches.However,thorough experiments on large-scale real datasets are highly desirable to show the behavior of the tool with respect to the performance and time execution criteria.Practical implications-The tool developed SkyRef can have many domains applications that require decision-making,personalized recommendation and where the size of skyline has to be reduced.In particular,SkyRef can be used in several real-world applications such as economic,security,medicine and services.Social implications-This work can be expected in all domains that require decision-making like hotel finder,restaurant recommender,recruitment of candidates,etc.Originality/value-This study mixes two research fields artificial intelligence(i.e.formal concept analysis)and databases(i.e.skyline queries).The key elements of the solution proposed for the skyline refinement problem are borrowed from the fuzzy formal concept analysis which makes it clearer and rational,semantically speaking.On the other hand,this study opens the door for using the formal concept analysis and its extensions in solving other issues related to skyline queries,such as relaxation.

Trustworthy Explainable Recommendation Framework for Relevancy

Explainable recommendation systems deal with the problem of‘Why’.Besides providing the user with the recommendation,it is also explained why such an object is being recommended.It helps to improve trustworthiness,effectiveness,efficiency,persuasiveness,and user satisfaction towards the system.To recommend the relevant information with an explanation to the user is required.Existing systems provide the top-k recommendation options to the user based on ratings and reviews about the required object but unable to explain the matched-attribute-based recommendation to the user.A framework is proposed to fetch the most specific information that matches the user requirements based on Formal Concept Analysis(FCA).The ranking quality of the recommendation list for the proposed system is evaluated quantitatively with Normalized Discounted Cumulative Gain(NDCG)@k,which is better than the existing systems.Explanation is provided qualitatively by considering trustworthiness criterion i.e.,among the seven explainability evaluation criteria,and its metric satisfies the results of proposed method.This framework can be enhanced to accommodate for more effectiveness and trustworthiness.

Subposition Assembly-Based Construction of Non-Frequent Concept Semi-Lattice

An efficient way to improve the efficiency of the applications based on formal concept analysis （FCA） is to construct the needed part of concept lattice used by applications. Inspired by this idea, an approach that constructs lower concept semi-lattice called non-frequent concept semi-lattice in this paper is introduced, and the method is based on subposition assembly. Primarily, we illustrate the theoretical framework of subposition assembly for non-frequent concept semi-lattice. Second, an algorithm called Nocose based on this framework is proposed. Experiments show both theoretical correctness and practicability of the algorithm Nocose.

Topic-Feature Lattices Construction and Visualization for Dynamic Topic Number

The topic recognition for dynamic topic number can realize the dynamic update of super parameters,and obtain the probability distribution of dynamic topics in time dimension,which helps to clear the understanding and tracking of convection text data.However,the current topic recognition model tends to be based on a fixed number of topics K and lacks multi-granularity analysis of subject knowledge.Therefore,it is impossible to deeply perceive the dynamic change of the topic in the time series.By introducing a novel approach on the basis of Infinite Latent Dirichlet allocation model,a topic feature lattice under the dynamic topic number is constructed.In the model,documents,topics and vocabularies are jointly modeled to generate two probability distribution matrices:Documentstopics and topic-feature words.Afterwards,the association intensity is computed between the topic and its feature vocabulary to establish the topic formal context matrix.Finally,the topic feature is induced according to the formal concept analysis(FCA)theory.The topic feature lattice under dynamic topic number(TFL DTN)model is validated on the real dataset by comparing with the mainstream methods.Experiments show that this model is more in line with actual needs,and achieves better results in semi-automatic modeling of topic visualization analysis.

Cooperative Answering of Fuzzy Queries

The majority of existing information systems deals with crisp data through crisp database systems. Traditional Database Management Systems （DBMS） have not taken into account imprecision so one can say there is some sort of lack of flexibility. The reason is that queries retrieve only elements which precisely match to the given Boolean query. That is, an element belongs to the result if the query is true for this element; otherwise, no answers are returned to the user. The aim of this paper is to present a cooperative approach to handling empty answers of fuzzy conjunctive queries by referring to the Formal Concept Analysis （FCA） theory and fuzzy logic. We present an architecture which combines FCA and databases. The processing of fuzzy queries allows detecting the minimal reasons of empty answers. We also use concept lattice in order to provide the user with the nearest answers in the case of a query failure.