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Aspect-Based Sentiment Analysis (ABSA) is a crucial process for assessing customer feedback and gauging satisfaction with products or services. It typically consists of three stages: Aspect Term Extraction (ATE), Aspect Categorization Extraction (ACE), and Sentiment Analysis (SA). Various techniques have been proposed for ATE, including unsupervised, supervised, and hybrid methods. However, many studies face challenges in detecting aspect terms due to reliance on training data, which may not cover all multiple aspect terms and relate semantic aspect terms effectively. This study presents a knowledge-driven approach to automatic semantic aspect term extraction from customer feedback using Linked Open Data (LOD) to enrich aspect extraction outcomes in the training dataset. Additionally, it utilizes the N-gram model to capture complex text patterns and relationships, facilitating accurate classification and analysis of multiple-word terms for each aspect. To assess the effectiveness of the proposed model, experiments were conducted on three benchmark datasets: SemEval 2014, 2015, and 2016. Comparative evaluations with contemporary unsupervised, supervised, and hybrid methods on these datasets yielded F-measures of 0.80, 0.76, and 0.77, respectively. [ABSTRACT FROM AUTHOR]

Prioritized Datalog ± is a well-studied formalism for modelling ontological knowledge and data, and has a success story in many applications in the (Semantic) Web and in other domains. Since the information content on the Web is both inherently context-dependent and frequently updated, the occurrence of a logical inconsistency is often inevitable. This phenomenon has led the research community to develop various types of inconsistency-tolerant semantics over the last few decades. Although the study of query answering under inconsistency-tolerant semantics is well-understood, the problem of explaining query answering under such semantics took considerably less attention, especially in the scenario where the facts are prioritized. In this paper, we aim to fill this gap. More specifically, we use Dung's abstract argumentation framework to address the problem of explaining inconsistency-tolerant query answering in Datalog ± KB where facts are prioritized, or preordered. We clarify the relationship between preferred repair semantics and various notions of extensions for argumentation frameworks. The strength of such argumentation-based approach is the explainability; users can more easily understand why different points of views are conflicting and why the query answer is entailed (or not) under different semantics. To this end we introduce the formal notion of a dialogical explanation, and show how it can be used to both explain showing why query results hold and not hold according to the known semantics in inconsistent Datalog ± knowledge bases. [ABSTRACT FROM AUTHOR]

The use of instructional Semantic Web rules to deliver personalized learning recommendations has become an emerging trend in intelligent tutoring systems (ITSs) because it enables experts’ domain knowledge to be easily transferred to machine-readable formats. However, many approaches to ITS design using instructional Semantic Web rules have evaluated learners’ performances without considering the uncertainty of the evaluation process or other factors such as learning behavior and speed of response. In this paper, we present an ITS framework named FUSE, which uses a novel recommendation-making mechanism based on a fuzzy-semantic reasoning process and a multiagent system. In this framework, fuzzy reasoning and semantic reasoning are integrated to form a unified reasoning process and provide personalized learning recommendations adaptively and semantically. A field experiment was conducted at a public university. The results indicated that FUSE enabled significant achievements in both enhancing learning performance and increasing learners’ participation in the learning process. [ABSTRACT FROM AUTHOR]

Conditional functional dependencies (CFDs) have been used to improve the quality of data, including detecting and repairing data inconsistencies. Approximation measures have significant importance for data dependencies in data mining. To adapt to exceptions in real data, the measures are used to relax the strictness of CFDs for more generalized dependencies, called approximate conditional functional dependencies (ACFDs). This paper analyzes the weaknesses of dependency degree, confidence and conviction measures for general CFDs (constant and variable CFDs). A new measure for general CFDs based on incomplete knowledge granularity is proposed to measure the approximation of these dependencies as well as the distribution of data tuples into the conditional equivalence classes. Finally, the effectiveness of stripped conditional partitions and this new measure are evaluated on synthetic and real data sets. These results are important to the study of theory of approximation dependencies and improvement of discovery algorithms of CFDs and ACFDs. [ABSTRACT FROM AUTHOR]

Embedding a secret message into the pixels of a cover image yields a visual distortion if these pixels belong to smooth regions. Thus, this prompted the development of some edge-based approaches in which only the edge pixels are used to hide secret bits. As a result, the visual quality of stego images is improved. However, the capacity is limited due to some unused regions in cover images. In this paper, an adaptive multi bit-planes image steganography using block data-hiding (MPBDH) is proposed. This method employs more than one bit-plane and applies an adaptive complexity threshold computation to select the complex regions of a cover image used in data hiding. Consequently, the embedding capacity and security performance are significantly improved in comparison with previous approaches based on pixel and block complexity. The results, which are obtained from experiments performed on 10,000 natural gray-images, indicate that the embedding capacity and security introduced in the proposed approach overcome the problems of previous approaches. The proposed approach is hence suitable for secure communications. [ABSTRACT FROM AUTHOR]

Association rule hiding has been playing a vital role in sensitive knowledge preservationwhen sharing data between enterprises. The aim of association rule hiding is to remove sensitive association rules from the released database such that side effects are reduced as low as possible. This research proposes an efficient algorithm for hiding a specified set of sensitive association rules based on intersection lattice of frequent itemsets. In this research, we begin by analyzing the theory of the intersection lattice of frequent itemsets and the applicability of this theory into association rule hiding problem. We then formulate two heuristics in order to (a) specify the victim items based on the characteristics of the intersection lattice of frequent itemsets and (b) identify transactions for data sanitization based on the weight of transactions. Next, we propose a new algorithm for hiding a specific set of sensitive association rules with minimum side effects and low complexity. Finally, experiments were carried out to clarify the efficiency of the proposed approach. Our results showed that the proposed algorithm, AARHIL, achieved minimum side effects and CPU-Time when compared to current similar state of the art approaches in the context of hiding a specified set of sensitive association rules. [ABSTRACT FROM AUTHOR]

Semantic heterogeneity has always been one of the most important problems to overcome. A number of systems have been proposed to address this problem, ranging from mediator-based systems to description logic-based systems to content-descriptive metadata systems. In this paper, we propose an ontology-based metadata dictionary as a basis for solving semantic heterogeneity. First, the ontology-based metadata dictionary is modeled on the basis of a bottom-up design approach. Next, an XML-based data model is employed to manipulate and express the metadata dictionary contents to demonstrate how the proposed ontology-based metadata dictionary can be applied to a practical implementation. We also present the transformation of the ontology-based metadata dictionary into an XML-based metadata dictionary representation. Finally, the proposed approach is applied to a practical case study along with some related query processing to demonstrate the viability of model realisation. [ABSTRACT FROM AUTHOR]