Dynamic constrained optimization problems (DCOPs) are difficult to solve because both objective function and constraints can vary with time. Although DCOPs have drawn attention in recent years, ...little work has been performed to solve DCOPs with multiple dynamic feasible regions from the perspective of locating and tracking multiple feasible regions in parallel. Moreover, few benchmarks have been proposed to simulate the dynamics of multiple disconnected feasible regions. In this paper, first, the idea of tracking multiple feasible regions, originally proposed by Nguyen and Yao, is enhanced by specifically adopting multiple subpopulations. To this end, the dynamic species-based particle swam optimization (DSPSO), a representative multipopulation algorithm, is adopted. Second, an ensemble of locating and tracking feasible regions strategies is proposed to handle different types of dynamics in constraints. Third, two benchmarks are designed to simulate the DCOPs with dynamic constraints. The first benchmark, including two variants of G24 (called G24v and G24w), could control the size of feasible regions. The second benchmark, named moving feasible regions benchmark (MFRB), is highly configurable. The global optimum of MFRB is calculated mathematically for experimental comparisons. Experimental results on G24, G24v, G24w, and MFRB show that the DSPSO with the ensemble of strategies performs significantly better than the original DSPSO and other typical algorithms.
Evolutionary community discovery is a hot research topic related to the dynamic or temporal social networks. The communities detected in a dynamic network should get reasonable partition for the ...current network and do not deviate drastically from the previous ones. This paper is an extended version of our previous work in Gao et al. (in: Proceedings of the 2016 international conference on big data and smart computing (BigComp), pp 53–60,
2016
). First, an evolutionary community discovery algorithm named
EvoLeaders
, which is inspired by
TopLeaders
algorithm, is proposed. Second, based on
TopLeaders
, an improved
TopLeaders
algorithm (i.e.,
AutoLeaders
) is proposed. Experiments on three classic data sets are conducted, and experimental results show that the
AutoLeaders
can correctly find the number of communities and at the same time can discover reasonable communities. Third, the
EvoAutoLeaders
algorithm is proposed for detecting the communities in a dynamic network. Compared with the
TopLeaders
algorithm and
EvoLeaders
, experimental results over two real-world data sets demonstrate that the
EvoAutoLeaders
is more suitable for dynamic scenarios.
Entity alignment refers to discovering identical entity pairs in 2 knowledge graphs, which is a significant task in knowledge fusion. Early automated entity alignment techniques are based mainly on ...similarity calculation and comparing symbolic features, i.e., entity names, between entities. Nevertheless, such methods’ performance would reduce significantly when the difference between knowledge graphs is enormous because of relying on predefined comparison rules. Recently, embedding-based methods calculate the similarity between entity pairs through vector embeddings and thus can deal with different knowledge graphs. However, embedding-based methods mostly require humans to annotate data, which is laborious. Therefore, we learn from each other to propose an unsupervised entity alignment framework in this work, which can generate initial alignment seeds automatically by considering symbolic similarities. It can effectively avoid the waste of human resources and is suitable for handling multiple types of knowledge graphs. In addition, we investigate improving the quality and quantity of initial alignment by integrating multiple symbolic similarity features of entities and dealing with the situation of entity information missing better. Experimental results on 3 real datasets demonstrate its state-of-the-art performance.
With the advent of the era of big data, the scale of data has grown dramatically, and there is a close correlation between massive multi-source heterogeneous data, which can be visually depicted by a ...big graph. Big graph, especially from Web data, social networks, or biometric data, has attracted more and more attention from researchers, which usually contains complex relationships and multiple attributes. How to perform efficient query and matching on big graph data is the basic problem on analyzing big graph. Using multi-constrained graph pattern matching, we can design patterns that meet our specific requirements, and find matched subgraphs to locate the required patterns to accomplish specific tasks. So how to find matched subgraphs with good attributes in big graph becomes the key problem on big graph research. Considering the possibility that a node in a subgraph may fail due to reliability, in order to select more and better matched subgraphs, in this paper, we introduce fuzziness and reliability into multi-objective graph pattern matching, and then use a multi-objective genetic algorithm NSGA-II to find the subgraphs with higher reliability and better attributes including social trust and social relationship. Finally, a reliability-based multi-fuzzy-objective graph pattern matching method (named as RMFO-GPM) is proposed. The experimental results on real data sets show the effectiveness of the proposed RMFO-GPM method comparing with other state-of-art methods.
Entity alignment (EA) aims to automatically determine whether an entity pair in different knowledge bases or knowledge graphs refer to the same entity in reality. Inspired by human cognitive ...mechanisms, we propose a coarse-to-fine entity alignment model (called CFEA) consisting of three stages: coarse-grained, middle-grained, and fine-grained. In the coarse-grained stage, a pruning strategy based on the restriction of entity types is adopted to reduce the number of candidate matching entities. The goal of this stage is to filter out pairs of entities that are clearly not the same entity. In the middle-grained stage, we calculate the similarity of entity pairs through some key attribute values and matched attribute values, the goal of which is to identify the entity pairs that are obviously not the same entity or are obviously the same entity. After this step, the number of candidate entity pairs is further reduced. In the fine-grained stage, contextual information, such as abstract and description text, is considered, and topic modeling is carried out to achieve more accurate matching. The basic idea of this stage is to use more information to help judge entity pairs that are difficult to distinguish using basic information from the first two stages. The experimental results on real-world datasets verify the effectiveness of our model compared with baselines.
Online education promotes the sharing of learning resources. Knowledge tracing (KT) is aimed at tracking the cognition function of students according to their performance on various exercises at ...different times and has attracted considerable attention. Existing KT models primarily use bisection representations for the performance and cognitive states of students, thus limiting the application scope of these models and the accuracy of the evaluation of student cognitive performance in learning processes. Therefore, fuzzy Bayesian KT models (namely, FBKT and T2FBKT) are proposed to address continuous score scenarios (e.g., subjective examinations) so that the applicability of KT models may be broadened. Moreover, fine-grained cognitive states can be discerned. In particular, referring to type-2 fuzzy theory, T2FBKT mitigates the model uncertainty of FBKT induced by uncertain parameters. Finally, extensive experiments demonstrate the effectiveness of the proposed fuzzy KT models.
Summary In person‐centric applications, the prevalence of shared names significantly hampers document retrieval, web search, and database integration, highlighting the critical need for name ...disambiguation. Network embedding based unsupervised name disambiguation methods have received much attention due to their wide applicability and superior disambiguation performance. However, existing methods require complex feature engineering, such as extracting biographical and source content features or constructing supplementary features from external knowledge bases, which are unavailable in privacy‐preserving scenarios. Moreover, they may face challenges such as imbalanced node training or overlooking global statistical information during node embedding learning. In this article, we propose a method to tackle the name disambiguation problem based on high‐degree penalty and global statistical information using only relational data. First, we construct a weighted source similarity network based on multi‐hop collaborator relationships. Second, we employ a high‐degree penalty based global statistical network embedding model to learn low‐dimensional node embeddings and preserve the structural features of the network. Finally, we cluster the same sources using a joint clustering algorithm that does not require prior knowledge of the number of clusters. The experiment validates the effectiveness of the proposed method on two real‐life datasets.
•We propose a two-stage framework for entity alignment from the perspective of combining the advantages of both symbol-based and embedding-based methods.•A series of symbol-based methods are adopted ...to align the relation pairs in stage I.•Symbol-based methods and a hybrid embedding model are combined to match the entity pairs in stage II.•Experimental results from real-word datasets demonstrate that our proposed method is effective.•Ablation studies illustrate our proposed strategies are versatile and can also be applied to other embedding models.
The objective of entity alignment is to judge whether entities refer to the same object in the real world. Methods for entity alignment can be grossly divided into two groups: conventional symbol-based entity alignment methods and embedding-based entity alignment methods. Both groups of methods have advantages and disadvantages (which are detailed in Section 1). Therefore, combining the advantages of both methods might be a promising strategy. However, to the best of our knowledge, only the RTEA algorithm that was proposed in our previous conference paper (Proceeding of Pacific Rim International Conference on Artificial Intelligence, pp. 162–175, 2019) utilizes this strategy for entity alignment. This manuscript is an extended version of that conference paper, in which an improved algorithm, namely, ESEA (combining embedding-based and symbol-based methods for entity alignment), is proposed based on the following steps. First, a novel method for combining embedding models with symbol-based models is proposed. Entities with high vector similarities are obtained through a hybrid embedding model, and the final aligned entity pairs are calculated via symbol-based methods. Second, a series of symbol-based methods, instead of only the edit distance method in the original version, are combined with embedding-based methods for relation alignment. Third, we combine symbol-based and embedding-based methods in a more complicated framework with the objective of better exploiting the advantages of both methods. The experimental results on real-world datasets demonstrate that the proposed method outperformed several state-of-the-art embedding-based entity alignment approaches and outperformed our previous RTEA method.
An example of fusing multiple financial knowledge graphs (KGs) from different sources. Through aligning and integrating multi-source heterogeneous data, we could observe that Jack Ma (also called Mr. Ma) is not only the principal founder of the Alibaba Group, but also a director of Softbank. Therefore, a more complete knowledge graph with rich information can be obtained, which is essential for applications such as financial search and financial question answering Display omitted .
Evolutionary Algorithms (EAs) with gradient-based repair, which utilize the gradient information of the constraints set, have been proved to be effective. It is known that it would be time-consuming ...if all infeasible individuals are repaired. Therefore, so far the infeasible individuals to be repaired are randomly selected from the population and the strategy of choosing individuals to be repaired has not been studied yet. In this paper, the Species-based Repair Strategy (SRS) is proposed to select representative infeasible individuals instead of the random selection for gradient-based repair. The proposed SRS strategy has been applied to εDEag which repairs the random selected individuals using the gradient-based repair. The new algorithm is named SRS-εDEag. Experimental results show that SRS-εDEag outperforms εDEag in most benchmarks. Meanwhile, the number of repaired individuals is reduced markedly.
Genealogical knowledge graphs depict the relationships of family networks and the development of family histories. They can help researchers to analyze and understand genealogical data, search for ...genealogical descendant paths, and explore the origins of a family. However, the heterogenous, autonomous, complex, and evolving natures of genealogical data bring challenges to the development of contemporary genealogical knowledge graph models. Applying existing methods to genealogical data may be improper because general knowledge graph models lack in-depth domain knowledge. In this paper, we propose a genealogical knowledge graph model named Huapu-KG that combines HAO intelligence (human intelligence + artificial intelligence + organizational intelligence) to implement the construction and applications of genealogical knowledge graphs. Furthermore, challenges in constructing genealogical knowledge graphs are demonstrated, and experiments conducted on real-world genealogical datasets verify the feasibility and effectiveness of our proposed model.
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