Skyline (λ,k)-Cliques Identification From Fuzzy Attributed Social Networks

This is the author accepted manuscript. The final version is available from IEEE via the DOI in this recordIdentifying the optimal groups of users that are closely connected and satisfy some ranking criteria from an attributed social network attracts significant attention from both academia and industry. Skyline query processing, a multicriteria decision-making optimized technique, is recently embedded into cohesive subgraphs mining in graphs/social networks. However, the existing studies cannot capture the fuzzy property of connections between users in social networks. To fill this gap, in this article, we formulate a novel model of the skyline (λ,k)-cliques over a fuzzy attributed social network and develop a formal concept analysis (FCA)-based skyline (λ,k)-cliques identification algorithm. Specifically, λ can be regarded as a quality control parameter for measuring the stability of the cohesive groups. Extensive experimental results conducted on three real-world datasets demonstrate the effectiveness of the skyline (λ,k)-clique model in a fuzzy attributed social network. Furthermore, an illustrative example is executed for revealing the usefulness of our model. It is expected that our proposed skyline (λ,k)-clique model can be widely used in various graph-based computational social systems, such as optimal team formation in crowdsourcing, and group recommendation in social networks.European Union Horizon 2020Fundamental Research Funds for the Central Universitie

Optimization of Dominance Testing in Skyline Queries Using Decision Trees

This is the author accepted manuscript. The final version is available from IEEE via the DOI in this record.Skyline queries identify skyline points, the minimal set of data points that dominate all other data points in a large dataset. The main challenge with skyline queries is executing the skyline query in the shortest possible time. To address and solve skyline query performance issues, we propose a decision tree-based method known as the decision tree-based comparator (DC). This method minimizes unnecessary dominance tests (i.e., pairwise comparisons) by constructing a decision tree based on the dominance testing. DC uses dominance relations that can be obtained from the decision rules of the decision tree to determine incomparability between data points. DC can also be easily applied to improve the performance of various existing skyline query methods. After describing the theoretical background of DC and applying it to existing skyline queries, we present the results of various experiments showing that DC can improve skyline query performance by up to 23.15 times.Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government(MSIT)Industrial Strategic Technology Development Program funded by the Ministry of Trade, Industry & Energy (MOTIE, Korea)

Incremental Entity Summarization with Formal Concept Analysis

This is the author's accepted manuscript; the final version is available from IEEE via the DOI in this record.Knowledge graph describes entities by numerous RDF data (subject-predicate-object triples), which has been widely applied in various fields, such as artificial intelligence, Semantic Web, entity summarization. With time elapses, the continuously increasing RDF descriptions of entity lead to information overload and further cause people confused. With this backdrop, automatic entity summarization has received much attention in recent years, aiming to select the most concise and most typical facts that depict an entity in brief from lengthy RDF data. As new descriptions of entity are continually coming, creating a compact summary of entity quickly from a lengthy knowledge graph is challenging. To address this problem, this paper firstly formulates the problem and proposes a novel approach of Incremental Entity Summarization by leveraging Formal Concept Analysis (FCA), called IES-FCA. Additionally, we not only prove the rationality of our suggested method mathematically, but also carry out extensive experiments using two real-world datasets. The experimental results demonstrate that the proposed method IES-FCA can save about 8.7% of time consumption for all entities than the non-incremental entity summarization approach KAFCA at best. As for the effectiveness, IES-FCA outperforms the state-of-the-art algorithms in terms of F1-measure, MAP, and NDCG.National Natural Science Foundation of ChinaFundamental Research Funds for the Central Universitie

Query-oriented Entity Spatial-temporal Summarization in Fuzzy Knowledge Graph

This is the author accepted manuscript. The final version is available from the Association for Computing Machinery via the DOI in this recordKnowledge Graph (KG) is a relatively new concept that has garnered a lot of attention. Furthermore, the information in KGis frequently ambiguous and imprecise, necessitating the creation of a Fuzzy Knowledge Graph (FKG). FKG describes the imprecise information of the entity by employing the fuzzy value of predicates or objects. Entity summarization can extract the most concise and important information from lengthy descriptions of an entity. Existing work, however, focuses solely on entity summarization in KG while ignoring the fuzziness of entity relationships in FKG. Thus, this paper proposed an FFCA-based approach for query-oriented entity spatial-temporal summarization. Fuzzy Formal Concept Analysis (FFCA) is used to turn the FKG into the regular KG initially. The summarized RDF triples can then be obtained by combining the time-centric and location-centric triadic concepts from diverse FKGs. Finally, various templatebased queries are designed for evaluating the performance of the proposed approach.National Natural Science Foundation of ChinaEuropean Union Horizon 2020Fundamental Research Funds for the Central Universitie

Incremental Entity Summarization with Formal Concept Analysis

Knowledge graph describes entities by numerous RDF data (subject-predicate-object triples), which has been widely applied in various fields, such as artificial intelligence, Semantic Web, entity summarization. With time elapses, the continuously increasing RDF descriptions of entity lead to information overload and further cause people confused. With this backdrop, automatic entity summarization has received much attention in recent years, aiming to select the most concise and most typical facts that depict an entity in brief from lengthy RDF data. As new descriptions of entity are continually coming, creating a compact summary of entity quickly from a lengthy knowledge graph is challenging. To address this problem, this paper firstly formulates the problem and proposes a novel approach of Incremental Entity Summarization by leveraging Formal Concept Analysis (FCA), called IES-FCA. Additionally, we not only prove the rationality of our suggested method mathematically, but also carry out extensive experiments using two real-world datasets. The experimental results demonstrate that the proposed method IES-FCA can save about 8.7% of time consumption for all entities than the non-incremental entity summarization approach KAFCA at best. As for the effectiveness, IES-FCA outperforms the state-of-the-art algorithms in terms of F1-measure, MAP, and NDCG