Semantic Clustering Algorithm Research Articles

ENHANCING SERVICE DISCOVERY IN MOBILE AD HOC NETWORKS USING SEMANTIC CLUSTERING AND HYBRID TRUST MANAGEMENT

Mobile Ad Hoc Networks (MANETs) facilitate communication among mobile devices without relying on fixed infrastructure. However, service discovery in MANETs faces challenges due to the dynamic topology and limited resources of nodes. Existing solutions often lack efficient resource utilization and fail to address trust management adequately. The conventional approaches for service discovery in MANETs are inefficient due to their inability to incorporate semantic clustering and robust trust management. Semantic clustering enhances the accuracy of service discovery by grouping nodes based on similar interests or functionalities. Meanwhile, traditional trust management mechanisms are inadequate in dynamic environments, leading to unreliable service discovery results. The proposed methodology involves developing a semantic clustering algorithm to organize nodes based on their semantic similarities. Additionally, a hybrid trust management system is implemented to assess the reliability of discovered services. The system combines both reputation-based and recommendation-based trust models to enhance the accuracy of trust evaluations. Through extensive simulations and experiments, the effectiveness of the proposed approach is demonstrated. The results indicate that the integration of semantic clustering and hybrid trust management significantly improves service discovery efficiency and reliability in MANETs. The approach achieves higher precision and recall rates compared to conventional methods, even under dynamic network conditions.

支持城市功能街区划分的有序语义聚类算法

支持城市功能街区划分的有序语义聚类算法

A Framework for Weak Signal Detection in Competitive Intelligence using Semantic Clustering Algorithms

Companies nowadays are sharing a lot of data on the web in structured and unstructured format, the data holds many signals from which we can analyze and detect innovation using weak signal detection approaches. To gain a competitive advantage over competitors, the velocity and volume of data available online must be exploited and processed to extract and monitor any type of strategic challenge or surprise whether it is in form of opportunities or threats. To capture early signs of a change in the environment in a big data context where data is voluminous and unstructured, we present in this paper a framework for weak signal detection relying on the crawling of a variety of web sources and big data based implementation of text mining techniques for the automatic detection and monitoring of weak signals using an aggregation approach of semantic clustering algorithms. The novelty of this paper resides in the capability of the framework to extend to an unlimited amount of unstructured data, that needs novel approaches to analyze, and the aggregation of semantic clustering algorithms for better automation and higher accuracy of weak signal detection. A corpus of scientific articles and patents is collected in order to validate the framework and provide a use case for future interested researchers in identifying weak signals in a corpus of data of a specific technological domain.

Hybrid semantic clustering of hashtags

Clustering hashtags based on their semantics is an important problem with many applications. The uncontrolled usage of hashtags in social media, however, makes the quality of semantics and the frequency of usage vary a lot, and this poses a challenge to the current approaches which capitalize on either the lexical semantics of a hashtag (by using metadata) or the contextual semantics of a hashtag (by using the texts associated with a hashtag). This paper presents a hybrid semantic clustering algorithm that uses the complementary strengths of lexical and contextual semantics of a hashtag to produce accurate clusters on a wider range of input data. The hybrid algorithm uses a consensus clustering approach, which finds the consensus between metadata-based sense-level semantic clusters and text-based semantic clusters. A gold standard test shows that the hybrid algorithm outperforms both the text-based algorithm and the metadata-based algorithm for a majority of ground truths tested and that it never underperforms both base algorithms. In addition, a larger-scale performance study, conducted with a focus on disagreements in cluster assignments between algorithms, show that the hybrid algorithm makes the correct cluster assignment in a majority of disagreement cases.

A fully-decentralized semantic mechanism for autonomous wireless sensor nodes

Semantic sensor neighborhood has been used to organize nodes into clusters in wireless sensor networks. Semantic clusters are self-adaptable according to information collected/gathered from sensor nodes and collaboratively processed. In this paper, we show that semantic clustering based on fully-decentralized semantic neighborhood mechanisms provides an energy-efficient solution, thus contributing to increase the autonomy of sensors. Our results show that fully-decentralized semantic clustering outperforms partially decentralized semantic clustering algorithms besides traditional clustering algorithms regarding the energy consumed to establish and maintain the clusters.

A methodology for structured ontology construction applied to intelligent transportation systems

The number of computers installed in urban and transport networks has grown tremendously in recent years, also the local processing capabilities and digital networking currently available. However, the heterogeneity of existing equipment in the field of ITS (Intelligent Transportation Systems) and the large volume of information they handle, greatly hinder the interoperability of the equipment and the design of cooperative applications between devices currently installed in urban networks. While the dynamic discovery of information, composition and invocation of services through intelligent agents are a potential solution to these problems, all these technologies require intelligent management of information flows. In particular, it is necessary to wean these information flows of the technologies used, enabling universal interoperability between computers, regardless of the context in which they are located. The main objective of this paper is to propose a systematic methodology to create ontologies, using methods such as a semantic clustering algorithms for retrieval and representation of information. Using the proposed methodology, an ontology will be developed in the ITS domain. This ontology will serve as the basis of semantic information to a SS (Semantic Service) that allows the connection of new equipment to an urban network. The SS uses the CORBA standard as distributed communication architecture.

Text Classification Retrieval Based on Complex Network and ICA Algorithm

With the development of computer science and information technology, the library is developing toward information and network. The library digital process converts the book into digital information. The high-quality preservation and management are achieved by computer technology as well as text classification techniques. It realizes knowledge appreciation. This paper introduces complex network theory in the text classification process and put forwards the ICA semantic clustering algorithm. It realizes the independent component analysis of complex network text classification. Through the ICA clustering algorithm of independent component, it realizes character words clustering extraction of text classification. The visualization of text retrieval is improved. Finally, we make a comparative analysis of collocation algorithm and ICA clustering algorithm through text classification and keyword search experiment. The paper gives the clustering degree of algorithm and accuracy figure. Through simulation analysis, we find that ICA clustering algorithm increases by 1.2% comparing with text classification clustering degree. Accuracy can be improved by 11.1% at most. It improves the efficiency and accuracy of text classification retrieval. It also provides a theoretical reference for text retrieval classification of eBook

An Object-Oriented Semantic Clustering Algorithm for High-Resolution Remote Sensing Images Using the Aspect Model

In this letter, we present a novel object-oriented semantic clustering algorithm for high-spatial-resolution remote sensing images using the probabilistic latent semantic analysis (PLSA) model coupled with neighborhood spatial information. First of all, an image collection is generated by partitioning a large satellite image into densely overlapped subimages. Then, the PLSA model is employed to model the image collection. Specifically, the image collection is partitioned into two subsets. One is used to learn topic models, where the number of topics is determined using a minimum description length criterion. The other is folded in using the learned topic models. Therefore, every pixel in each subimage has been allocated a topic label. At last, the cluster label of every pixel in the large satellite image is derived from the topic labels of multiple subimages which cover the pixel in the image collection. Experimental results over a QUICKBIRD image show that the clusters of the proposed algorithm are better than K-means and Iterative Self-Organizing Data Analysis Technique Algorithm in terms of object-oriented property.