A Personalized Ontology Model for Web Information Gathering

Tao, Xiaohui; Li, Yuefeng; Zhong, Ning

doi:10.1109/tkde.2010.145

Cited by 91 publications

(41 citation statements)

References 36 publications

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“…Tao et al [2] 2011,proposes a personalized ontology model is proposed for knowledge representation and reasoning over user profiles. This model learns ontological user profiles from both a world knowledge base and user local instance repositories.…”

Section: Literature Surveymentioning

confidence: 99%

HealthCare Recommendation for Personalized Framework

Ahire¹,

Khanuja²

2015

IJCA

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Now a days, for any kind of information, people depend on internet. They use search engines like Google to search information over internet. The queries have to be accurate that will give the information related to user's Health Care.But there is huge amount of information on the internet and so it's difficult to get the relevant information easily. In case of searches on right food exercise, people usually have their own preferences. Also people are restricted to some medical conditions so some foods and exercises are avoided so they are attracted towards other food and exercises. Also the civilization where people reside will have impact on the number of choices and varieties of food.So there is need for easy to use framework for food and exercise recommendation. We propose ontology based framework for Health Care recommendation system which will provide precise information based on users requirements and constraints. This framework will use semantic web technology to analyze user's preferences and will build a nourished and health associated use's profile and will use the profile to categorize the associated knowledge so that users can make delicious food and exercise inquiries. We will also use the Decision Tree algorithm for retrieving related information from the database. General TermsData mining and Information Retrieval, Decision tree.

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Section: Literature Surveymentioning

confidence: 99%

HealthCare Recommendation for Personalized Framework

Ahire¹,

Khanuja²

2015

IJCA

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“…Considering the second point, there are two main user profile representations: a set of weighted keywords or terms [45] and rich semantic based structures, sometimes enhanced with the use of ontologies [43], [50]. The most common representation for user profiles is the first one, which we will use.…”

Section: User Profilesmentioning

confidence: 99%

Using Personalization to Improve XML Retrieval

Campos

Fernández‐Luna

Huete

et al. 2014

IEEE Trans. Knowl. Data Eng.

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Abstract-As the amount of information increases every day and the users normally formulate short and ambiguous queries, personalized search techniques are becoming almost a must. Using the information about the user stored in a user profile, these techniques retrieve results that are closer to the user preferences. On the other hand, the information is being stored more and more in an semi-structured way, and XML has emerged as a standard for representing and exchanging this type of data. XML search allows a higher retrieval effectiveness, due to its ability to retrieve and to show the user specific parts of the documents instead of the full document. In this paper we propose several personalization techniques in the context of XML retrieval. We try to combine the different approaches where personalization may be applied: query reformulation, reranking of results and retrieval model modification. The experimental results obtained from a user study using a parliamentary document collection support the validity of our approach.

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“…Ontologies are widely used to represent user profiles in personalized web information gathering. Ontology mining discovers interesting and on-topic knowledge from the concepts (Tao et al, 2011). Ontologies provided a shared and common understanding of a domain that can be communicated between people and across application systems (Lilac and Nadeen, 2010).…”

Section: Related Workmentioning

confidence: 99%

“…Usually errors occur in query-based opinionated summary for blog entries. Such errors are identified and blog summarization was compared with news texts summarization (Tao et al, 2011). Fuzzy logic with swarm intelligence could play an important role in the selection process of the most important sentences to be included in the final summary.…”

Section: Related Workmentioning

confidence: 99%

Unified Semantic Blog Mining Framework and Summarized Blog Retrieval

Sathianesan¹,

Sankaranarayanan²

2013

Journal of Computer Science

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In today's scenario, publishing the personal content and technical content has become very easy for the publishers, because of the readily available prominent social media called blog. Content developers, teachers, researchers post various articles relevant to their research or newly emerging topics. Blog content in one blog source may resemble the semantics of blog from other source. Readers, who are fascinated in reading the blog content, anticipate retrieving the blogs from different sources for their query. A large number of posts are available in the web. Hence the blog reader's task becomes very complex to search the relevant content for their query. This study introduces a novel idea to collect the blog using unified Semantic Blog Mining Framework (SBMF). SBMF collects blogs from different blog sources using the ontology constructed for education domain. Blogs collected from different sources are collection which contains relevant or irrelevant blogs. The new blog summarizer summarizes the blog content and ranks the blogs according to the similarity of the blog with query given by the user. The proposed blog summarizer check the similarity of each sentence in a blog, sort the order of sentence based on the similarity of the text with query word and reduces the number of sentences. The experimental results shows that the proposed unified SBMF and blog summarizer produces better relevant and summarized number of blogs compared to various search engines. SBMF confirms the hundred percentage relevancy compared to other blog search engines. Blog summarizer yields accurate summarization for the collected blogs.

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A Personalized Ontology Model for Web Information Gathering

Cited by 91 publications

References 36 publications

HealthCare Recommendation for Personalized Framework

HealthCare Recommendation for Personalized Framework

Using Personalization to Improve XML Retrieval

Unified Semantic Blog Mining Framework and Summarized Blog Retrieval

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