Discovering Beaten Paths in Collaborative Ontology-Engineering Projects using Markov Chains

Biomedical taxonomies, thesauri and ontologies in the form of the International Classification of Diseases (ICD) as a taxonomy or the National Cancer Institute Thesaurus as an OWL-based ontology, play a critical role in acquiring, representing and processing information about human health. With increasing adoption and relevance, biomedical ontologies have also significantly increased in size. For example, the 11th revision of the ICD, which is currently under active development by the WHO contains nearly 50,000 classes representing a vast variety of different diseases and causes of death. This evolution in terms of size was accompanied by an evolution in the way ontologies are engineered. Because no single individual has the expertise to develop such large-scale ontologies, ontology-engineering projects have evolved from small-scale efforts involving just a few domain experts to large-scale projects that require effective collaboration between dozens or even hundreds of experts, practitioners and other stakeholders. Understanding how these stakeholders collaborate will enable us to improve editing environments that support such collaborations. We uncover how large ontology-engineering projects, such as the ICD in its 11th revision, unfold by analyzing usage logs of five different biomedical ontology-engineering projects of varying sizes and scopes using Markov chains. We discover intriguing interaction patterns (e.g., which properties users subsequently change) that suggest that large collaborative ontology-engineering projects are governed by a few general principles that determine and drive development. From our analysis, we identify commonalities and differences between different projects that have implications for project managers, ontology editors, developers and contributors working on collaborative ontology-engineering projects and tools in the biomedical domain.Comment: Published in the Journal of Biomedical Informatic

Web Page Annotation Using Web Usage Mining and Domain Knowledge Ontology

Today’s world the growth of the WWW has increased tremendously, the user is totally relying on web for information. Search engine provides the result pages to the user but all are not relevant so the challenging task is extracting the pages from web and provide to the user. WUM is an approach to extract knowledge and use it to the different purposes. In this paper new semantic approach is proposed based on WUM and Domain Knowledge Ontology. Ontology database preparation, it is also challenging task in this project

Tracking Federated Queries in the Linked Data

Federated query engines allow data consumers to execute queries over the federation of Linked Data (LD). However, as federated queries are decomposed into potentially thousands of subqueries distributed among SPARQL endpoints, data providers do not know federated queries, they only know subqueries they process. Consequently, unlike warehousing approaches, LD data providers have no access to secondary data. In this paper, we propose FETA (FEderated query TrAcking), a query tracking algorithm that infers Basic Graph Patterns (BGPs) processed by a federation from a shared log maintained by data providers. Concurrent execution of thousand subqueries generated by multiple federated query engines makes the query tracking process challenging and uncertain. Experiments with Anapsid show that FETA is able to extract BGPs which, even in a worst case scenario, contain BGPs of original queries

Web Mining Functions in an Academic Search Application

This paper deals with Web mining and the different categories of Web mining like content, structure and usage mining. The application of Web mining in an academic search application has been discussed. The paper concludes with open problems related to Web mining. The present work can be a useful input to Web users, Web Administrators in a university environment.Database, HITS, IR, NLP, Web mining

Knowledge Discovery from Web Logs - A Survey

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