International Journal of Software Engineering and Knowledge Engineering

In this study, we highlight some fundamental issues of knowledge management and cast them in the setting of Granular Computing (GrC). We show how its formal constructs — information granules are instrumental in knowledge representation and specification of its level of abstraction.

To enable artificial systems to meaningfully use a semantic language of communication is one of the long-term and key targets not only in the field of artificial cognitive agents, but also of AI research in general. Given existing solutions for grounding of modal statements of a language of communication and an idea to model internal concepts of the agent as zadehian fuzzy-linguistic concepts, this paper shows how to meaningfully combine the two within a single framework. An accomplished goal is a model for grounding of modal and non-modal statements of a language of communication based on concepts modelled internally as fuzzy sets spanned over the domain of observation. This paper describes a way in which fuzzy-linguistic concepts are activated by perceptual inputs and how an agents grounds respective non-modal statements. Further, an agent supposed to describe an unobserved part of the environment can use autoepistemic operators of possibility, belief, and knowledge to describe its cognitive attitude toward it. It is discussed how the modal extensions of statements with fuzzy-linguistic concepts should be grounded in order to preserve the common-sense. The resulting constraints put on the model of grounding are formally represented in a form of analytical restrictions put on the so-called relation of epistemic satisfaction.

Experts finding, one of the most important tasks in social networks, is aimed at identifying individuals with relevant expertise or experience in a given topic. Several approaches have been proposed for finding experts in social networks from documents or web repositories. However, the semantic approach for modeling the information to find experts has not yet been explored. In this paper, we propose a novel method to index the academic information in an ontology-based model for finding and ranking the experts in a particular domain. Additionally, we propose an effective method to construct the academic social network by exploring the relations among the experts and measuring the score of each expert. The score of an expert is measured considering the contributions of relevant publications and relationships among other expert candidates. It is very efficient to find and ranking experts to take advantage of the millions of candidate experts being with relationships. An experiment conducted to evaluate our model shows that experts finding and ranking with an ontological approach integrated with the social network is more effective than other approaches.

Many organizations have been employing Knowledge Management Systems (KMS) to improve task performance. In this paper, we propose a novel KMS by using semantic wiki framework based on a centralized Global Wiki Ontology (GWO). The main aim of this system is (i) to collect as many organizational resources as possible, and (ii) to maintain semantic consistency of the system. When enriching the KMS in a particular domain, not only linguistic resources but also conceptual structures can be efficiently captured from multiple users, and more importantly, the resources can be automatically integrated with the GWO of the KMS in real time. Once users add new organization resources, the proposed KMS can formalize and contextualize them into a set of triplets by referring to a predefined pattern-triplet mapping table and the GWO. Especially, since the ontology mapper is interleaved, the KMS can determine whether the new resources are semantically conflicted with the GWO. To evaluate the proposed methodology, we have implemented the semantic wiki-based KMS. As a case study, two user groups were invited to collect the organization resources. We found that the group with the proposed KMS has shown better performance than the other group with traditional wiki-based KMS.

Due to the proliferation of online social networking, a large number of personal data are publicly available. As such, personal attacks, reputational, financial, or family losses might occur once this personal and sensitive information falls into the hands of malicious hackers. Research on Privacy-Preserving Network Publishing has attracted much attention in recent years. But most work focus on node de-identification and link protection. In academic social networks, business transaction networks, and transportation networks, etc, node identities and link structures are public knowledge but weights and shortest paths are sensitive. In this work, we study the problem of k-anonymous path privacy. A published network graph with k-anonymous path privacy has at least k indistinguishable shortest paths between the source and destination vertices [21]. In order to achieve such privacy, three different strategies of modification on edge weights of directed graphs are proposed. Numerical comparisons show that weight-proportional-based strategy is more efficient than PageRank-based and degree-based strategies. In addition, it is also more efficient and causes less information loss than running on un-directed graphs.

In this paper we extend some well-known notions of combinatorics on multi-sets such as iterative permutation, multi-subset, iterative combination and then construct new efficient algorithms for generating all iterative permutations, multi-subsets and iterative combinations of a multi-set. Applying the parallelizing method based on output decomposition we parallelize the algorithms. Furthermore, we use these algorithms to solve an optimal problem of work arrangement and an extended knapsack one.

This paper aims to minimize power consumption and sustain customer satisfaction for a datacenter. In some distributed computing environments, especially in a cloud computing environment, the most concerned issue for a datacenter designer is power consumption instead of its speed. This is because the storage devices always consume much electricity. To save power, we can assign to each device proper workload and turn off some spare devices. Therefore, partitioning data and allocating them among the storage devices becomes the first priority. However, due to some complex constraints, the data partition problem is very time-consuming or even NP-hard. Therefore, we propose a gradient-based method to deal with the problem. The novelty of the proposed method lies in that the discretized combinatorial problem is solved by a calculus-based technique. Experimental results show that significant energy is saved by shutting down as many uncalled-for devices as possible. The results also suggest that other similar optimization problems can be solved by the linearly-convergent method.