SMS: A Framework for Service Discovery by Incorporating Social Media Information

Abstract

With the explosive growth of services, including Web services, cloud services, APIs and mashups, discovering the appropriate services for consumers is becoming an imperative issue. The traditional service discovery approaches mainly face two challenges: 1) the single source of description documents limits the effectiveness of discovery due to the insufficiency of semantic information; 2) more factors should be considered with the generally increasing functional and nonfunctional requirements of consumers. In this paper, we propose a novel framework, called SMS, for effectively discovering the appropriate services by incorporating social media information. Specifically, we present different methods to measure four social factors (semantic similarity, popularity, activity, decay factor) collected from Twitter. Latent Semantic Indexing (LSI) model is applied to mine semantic information of services from meta-data of Twitter Lists that contains them. In addition, we assume the target query-service matching function as a linear combination of multiple social factors and design a weight learning algorithm to learn an optimal combination of the measured social factors. Comprehensive experiments based on a real-world dataset crawled from Twitter demonstrate the effectiveness of the proposed framework SMS, through some compared approaches.

Sociocultural factors during COVID-19 pandemic: Information consumption on Twitter

“…It is also confirmed that the use of language, when users interact and exchange messages with @pandemic_Covid-19, aims to communicate, request and propose topics or actions that will mitigate the uncertainty generated by the COVID-19 pandemic. The linguistic expressions used in the messages represent a type of functional knowledge that reveals sociocultural factors ( Liang et al, 2016 ). It is verified that the lexicon used in the messages related to COVID-19 depends on the meaning that users give to the information they consume in different communication channels and, in turn, how the content of the message influences their sociocultural factors.…”

“…In turn, the use of language aims to build and maintain identities and social relationships and even define the boundaries of communities ( Nguyen et al., 2016 ). On the one hand, the semantics that people use when structuring a message are a type of functional knowledge that reveals sociocultural factors ( Liang et al, 2016 ). Social factors such as attitudes, motivations, and the social and political context are just as important as linguistic factors in multilingual environments ( Bhatt & Bolonyai, 2011 ).…”

Resilience and Elasticity for Continuous Service-Based Processes in Pandemic Ages

“…Such a team does not purely evaluate technical constraints but also regulatory compliance. Hence, we need a stronger team formation for pandemic-specific human services as well as support from machine learning for long-tailed services discovery [16] and discovery improvement with social information [17].…”

Semantic Web Services: An Empirical Methodology to Security

“…Herminio García-González, José Emilio Labra Gayo and MPuerto Paule-Ruiz [5] have described a new educational tool that relies on semantic Web technologies to teach text content.A research review related to the belief in the virtual world and semantic Web based on social network analysis has been reviewed in [6]. Largescale data has been analyzed within a virtual world by exploring the flow of trust in different layers of social networks with the help of Semantic Webtechnologies.The problem of knowledge base has been studies using semantic integration with crowd intelligence [7].The authors have proposed a hybrid framework for KB semantic integration considering the semantic heterogeneity of KB class structures.In [8], a method has been proposed by matching the semantic service based on word embedding thatimproves the efficiency of the service discovery.Two types of semantic services such as event-recognition and event-handling services are specified about the events and the words are matched through the evaluation of equality which is organized with word embedding.The framework has been proposed for effective search of suitable services by including information from social media [9]. The authors have insinuated a framework using various methods for measuring four social factors like semantic equality, popularity, activity, decay factor on data collected from Twitter.…”