SwCS: Section-Wise Content Similarity Approach to Exploit Scientific Big Data

The growing collection of scientific data in various web repositories is referred to as Scientific Big Data,as it fulfills the four“V’s”of Big Data—volume,variety,velocity,and veracity.This phenomenon has created new opportunities for startups;for instance,the extraction of pertinent research papers from enormous knowledge repositories using certain innovative methods has become an important task for researchers and entrepreneurs.Traditionally,the content of the papers are compared to list the relevant papers from a repository.The conventional method results in a long list of papers that is often impossible to interpret productively.Therefore,the need for a novel approach that intelligently utilizes the available data is imminent.Moreover,the primary element of the scientific knowledge base is a research article,which consists of various logical sections such as the Abstract,Introduction,Related Work,Methodology,Results,and Conclusion.Thus,this study utilizes these logical sections of research articles,because they hold significant potential in finding relevant papers.In this study,comprehensive experiments were performed to determine the role of the logical sections-based terms indexing method in improving the quality of results(i.e.,retrieving relevant papers).Therefore,we proposed,implemented,and evaluated the logical sections-based content comparisons method to address the research objective with a standard method of indexing terms.The section-based approach outperformed the standard content-based approach in identifying relevant documents from all classified topics of computer science.Overall,the proposed approach extracted 14%more relevant results from the entire dataset.As the experimental results suggested that employing a finer content similarity technique improved the quality of results,the proposed approach has led the foundation of knowledge-based startups.

A Knowledge Graph-Based Deep Learning Framework for Efficient Content Similarity Search of Sustainable Development Goals Data

Sustainable development denotes the enhancement ofliving standards in the present without compromising future generations'resources.Sustainable Development Goals(SDGs)quantify the accomplishment of sustainable development and pave the way for a world worth living in for future generations.Scholars can contribute to the achievement of the SDGs by guiding the actions of practitioners based on the analysis of SDG data,as intended by this work.We propose a framework of algorithms based on dimensionality reduction methods with the use of Hilbert Space Filling Curves(HSFCs)in order to semantically cluster new uncategorised SDG data and novel indicators,and efficiently place them in the environment of a distributed knowledge graph store.First,a framework of algorithms for insertion of new indicators and projection on the HSFC curve based on their transformer-based similarity assessment,for retrieval of indicators and loadbalancing along with an approach for data classification of entrant-indicators is described.Then,a thorough case study in a distributed knowledge graph environment experimentally evaluates our framework.The results are presented and discussed in light of theory along with the actual impact that can have for practitioners analysing SDG data,including intergovernmental organizations,government agencies and social welfare organizations.Our approach empowers SDG knowledge graphs for causal analysis,inference,and manifold interpretations of the societal implications of SDG-related actions,as data are accessed in reduced retrieval times.It facilitates quicker measurement of influence of users and communities on specific goals and serves for faster distributed knowledge matching,as semantic cohesion of data is preserved.

Dynamic Trust Model Based on Service Recommendation in Big Data

In big data of business service or transaction,it is impossible to provide entire information to both of services from cyber system,so some service providers made use of maliciously services to get more interests.Trust management is an effective solution to deal with these malicious actions.This paper gave a trust computing model based on service-recommendation in big data.This model takes into account difference of recommendation trust between familiar node and stranger node.Thus,to ensure accuracy of recommending trust computing,paper proposed a fine-granularity similarity computing method based on the similarity of service concept domain ontology.This model is more accurate in computing trust value of cyber service nodes and prevents better cheating and attacking of malicious service nodes.Experiment results illustrated our model is effective.

Hierarchical Stream Clustering Based NEWS Summarization System

News feed is one of the potential information providing sources which give updates on various topics of different domains.These updates on various topics need to be collected since the domain specific interested users are in need of important updates in their domains with organized data from various sources.In this paper,the news summarization system is proposed for the news data streams from RSS feeds and Google news.Since news stream analysis requires live content,the news data are continuously collected for our experimentation.Themajor contributions of thiswork involve domain corpus based news collection,news content extraction,hierarchical clustering of the news and summarization of news.Many of the existing news summarization systems lack in providing dynamic content with domain wise representation.This is alleviated in our proposed systemby tagging the news feed with domain corpuses and organizing the news streams with the hierarchical structure with topic wise representation.Further,the news streams are summarized for the users with a novel summarization algorithm.The proposed summarization system generates topic wise summaries effectively for the user and no system in the literature has handled the news summarization by collecting the data dynamically and organizing the content hierarchically.The proposed system is compared with existing systems and achieves better results in generating news summaries.The Online news content editors are highly benefitted by this system for instantly getting the news summaries of their domain interest.

Exploiting Structural Similarities to Classify Citations

Citations play an important role in the scientific community by assisting in measuring multifarious policies like the impact of journals,researchers,institutions,and countries.Authors cite papers for different reasons,such as extending previous work,comparing their study with the state-of-the-art,providing background of the field,etc.In recent years,researchers have tried to conceptualize all citations into two broad categories,important and incidental.Such a categorization is very important to enhance scientific output in multiple ways,for instance,(1)Helping a researcher in identifying meaningful citations from a list of 100 to 1000 citations(2)Enhancing the impact factor calculation mechanism by more strongly weighting important citations,and(3)Improving researcher,institutional,and university rankings by only considering important citations.All of these uses depend upon correctly identifying the important citations from the list of all citations in a paper.To date,researchers have utilized many features to classify citations into these broad categories:cue phrases,in-text citation counts,and metadata features,etc.However,contemporary approaches are based on identification of in-text citation counts,mapping sections onto the Introduction,Methods,Results,and Discussion(IMRAD)structure,identifying cue phrases,etc.Identifying such features accurately is a challenging task and is normally conducted manually,with the accuracy of citation classification demonstrated in terms of these manually extracted features.This research proposes to examine the content of the cited and citing pair to identify important citing papers for each cited paper.This content similarity approach was adopted from research paper recommendation approaches.Furthermore,a novel section-based content similarity approach is also proposed.The results show that solely using the abstract of the cited and citing papers can achieve similar accuracy as the stateof-the-art approaches.This makes the proposed approach a viable technique that does not depend on manual identification of complex features.