Discovering semantic aspects of socially constructed knowledge hierarchy to boost the relevance of Web searching

The research intends to boost the relevance of Web search results by classifyingWebsnippet into socially constructed hierarchical search concepts, such as the mostcomprehensive human edited knowledge structure, the Open Directory Project (ODP). Thesemantic aspects of the search concepts (categories) in the socially constructed hierarchicalknowledge repositories are extracted from the associated textual information contributed bysocieties. The textual information is explored and analyzed to construct a category-documentset, which is subsequently employed to represent the semantics of the socially constructedsearch concepts. Simple API for XML (SAX), a component of JAXP (Java API for XMLProcessing) is utilized to read in and analyze the two RDF format ODP data files, structure.rdfand content.rdf. kNN, which is trained by the constructed category-document set, is used tocategorized the Web search results. The categorized Web search results are then ontologicallyfiltered based on the interactions of Web information seekers. Initial experimental resultsdemonstrate that the proposed approach can improve precision by 23.5%

Digital Web Ecosystem Development for Managing Social Network Data Science

The World Wide Web (WWW) unfolds with diverse domains and associated data sources, complicating the network data science. In addition, heterogeneity and multidimensionality can make data management, documentation, and even integration more challenging. The WWW emerges as a complex digital ecosystem on Big Data scale, and we conceptualize the web network as a Digital Web Ecosystem (DWE) in an analytical space. The purpose of the research is to develop a framework, explore the association between attributes of social networks and assess their strengths. We have experimented network users and usability attributes of social networks and tools, including misgivings. We construe new insights from data views of DWE metadata. For leveraging the usability and popularity-sentiment attribute relationships, we compute map views and several regressions between instances of technology and society dimensions, interpreting their strengths and weaknesses. Visual analytics adds values to the DWE meta-knowledge, establishing cognitive data usability in the WWW

An integrating text retrieval framework for Digital Ecosystems Paradigm

The purpose of the research is to provide effective information retrieval services for digital ?organisms? in a digital ecosystem by leveraging the power of Web searching technology. A novel integrating digital ecosystem search framework (a new digital organism) is proposed which employs the Web search technology and traditional database searching techniques to provide economic organisms with comprehensive, dynamic, and organization-oriented information retrieval ranging from the Internet to personal (semantic) desktop

IR issues for digital ecosystems users

The purpose of this research is to discuss some challenges of information retrieval, especially Web information retrieval, in digital ecosystems from a user?s perspective. As a dominant search tool, search engines usually return millions of search results in a long flat list in which many or even most of the results can be irrelevant. The long flat list conveys nothing about knowledge structure related to the retrieved results and personal search preferences and interests are not explored.Although some search engines try to cluster the Web results, the automatically formed titles and knowledge hierarchy is prone to mismatching the searcher?s human mental model. In digital ecosystems, while many different search tools are available, they are not integrated. To address these issues, a search framework which combines categorization, clustering, ontology, and personalization is proposed, and thus the quality of search results in digital ecosystems is expected to be boosted

Personalized information retrieval in digital ecosystems

Search results personalization is considered a promising approach to boost the quality of text retrieval. In this paper, a personalized information retrieval paradigm is proposed which not only implicitly creates user profile by learning users? search history, search preferences, and desktop information by kNN algorithm; but also intends to deal with the problem of search concepts drift through adjusting theweight of category which represents users? search preference.By comparing the cosine similarities between vectors represent personal valued search concepts in user profiles, and vectors represent search concepts in the retrieved search results, the search results will be tailed to better match users? information needs

Determining and satisfying search users real needs via socially constructed search concept classification

The focus of the research is to disambiguate search query by categorizing search results returned by search engines and interacting with the user to achieve query and results refinement. A novel special search-browser has been developed which combines search engine results, the Open DirectoryProject (ODP) based lightweight ontology as navigator and classifier, and search results categorizing. Categories are formed based on the ODP as a predefined ontology and Lucene is to be employed to calculate the similarity between retrieved items of the search engine and concepts in the ODP. With theinteraction of users, the search-browser improves the quality of search results by excluding the irrelevant documents and ontologically categorizing results for user inspection

Improving web search by categorization, clustering, and personalization

This research combines Web snippet1 categorization, clustering and personalization techniques to recommend relevant results to users. RIB - Recommender Intelligent Browser which categorizes Web snippets using socially constructed Web directory such as the Open Directory Project (ODP) is to bedeveloped. By comparing the similarities between the semantics of each ODP category represented by the category-documents and the Web snippets, the Web snippets are organized into a hierarchy. Meanwhile, the Web snippets are clustered to boost the quality of the categorization. Based on an automatically formed user profile which takes into consideration desktop computer informationand concept drift, the proposed search strategy recommends relevant search results to users. This research also intends to verify text categorization, clustering, and feature selection algorithms in the context where only Web snippets are available

An Integrated Information Retrieval Framework for Managing the Digital Web Ecosystem

The information explosion makes the digital Web ecosystem exploration, as a valid web search tool challenging for retrieving relevant information and knowledge. The existing tools are not integrated, and search results are not well managed. In this article, we describe effective information retrieval services for users and agents in various digital ecosystem scenarios. A novel integrated information retrieval framework (IIRF) is proposed, which employs the Web search technologies and traditional database searching techniques to provide comprehensive, dynamic, personalized, and organization-oriented information retrieval services, ranging from the Internet, intranet, to personal desktop. Experiments are carried out demonstrating the improvements in the search process with an average precision of Web search results to standard 11 recall level, attaining improvement from 41.7% of a comparable system to 65.2% of search. A 23.5% precision improvement is achieved with the framework. The comparison made among search engines presents a similar development with satisfactory search results

Information ecological imbalance and information intervention policies in digital ecosystems

This article explores ecological imbalance in digital ecology environment. With the dramatic growth of digital information in the world, organizations, in an age of information ecology can easily be affected by the consequences of information ecological imbalance. This imbalance may be triggered by maladjustment of the circulation in the system, by the abnormal flowing of some kinds of information, or the malfunction of some components in the chain of information flow. An information flow intervention approach, which imposes some control over information flow by means of semantic annotation, is proposed from the perspective of operationality and feasibility. The principal components in the proposed model are explained and the working principle of the model is explained. This research aims to realize ecological balance in a digital ecosystem by intervening in the flow of information, and consequently, providing some mechanism that will lead to the universal governance of the digital ecosystem

Digital Web Ecosystem Development for Managing Social Network Data Science

The World Wide Web (WWW) unfolds with diverse domains and associated data sources, complicating the network data science. In addition, heterogeneity and multidimensionality can make data management, documentation, and even integration more challenging. The WWW emerges as a complex digital ecosystem on Big Data scale, and we conceptualize the web network as a Digital Web Ecosystem (DWE) in an analytical space. The purpose of the research is to develop a framework, explore the association between attributes of social networks and assess their strengths. We have experimented network users and usability attributes of social networks and tools, including misgivings. We construe new insights from data views of DWE metadata. For leveraging the usability and popularity-sentiment attribute relationships, we compute map views and several regressions between instances of technology and society dimensions, interpreting their strengths and weaknesses. Visual analytics adds values to the DWE meta-knowledge, establishing cognitive data usability in the WWW