Fig 2 - available via license: Creative Commons Attribution 4.0 International
Content may be subject to copyright.
Source publication
The objectives raised in this paper are to pave the new dimension to Internet searching and bring the semantic core strategies to the forefront to add values to the search process. In precise, "the search must be what user wish, not what user types". To know the process of search intricacy, we observed the vocabulary contradiction and mismatch prob...
Similar publications
In this paper, we improve the performance of the recently proposed Direct Query Classifier (DQC). The (DQC) is a classifier based retrieval method and in general, such methods have been shown to be superior to the OCR-based solutions for performing retrieval in many practical document image datasets. In (DQC), the classifiers are trained for a set...
Citations
... Terms that are often used in this paper when referring to Google search are explained in this section. A typical search query is made of one to three words [22] e.g. "credit crunch". ...
Personalization promises to improve the accuracy of Web search and has been drawing much research attention recently. Some evidence indicates that for educational purposes, the disadvantages of personalized search are not justified by its benefits. The potential issues with search personalization, especially in an educational context, include loss of serendipity and capability, commercialization of education and the "Filter Bubble" effect where users are denied information if search engine algorithms decide it is irrelevant to them. The majority of students in higher education make use of general-purpose search engines to find academic information, however we have little knowledge about the effects of personalization on learners' experience and achievements. This observation motivates the research in this paper. First, we surveyed 120 university students to investigate which research sources, including search engines they predominately use and how much they depend on each for educational purposes. We learned that the majority of students prefer Google to other search engines; indeed sometimes it is their primary or only information-seeking tool. Additionally, about 80% of them use search engines for educational purposes on daily basis. Second, we measured the difference between personalized and non-personalized search results for 120 academic search queries divided equally into four categories: Education, IT, Health sciences and Business. Our results showed that on average only 53% of links appear, not necessarily in the same order, in both personalized and non-personalized search results. Interestingly, we observed only slight differences in the extent of personalization based on academic topics.
... The highest mean value of ontology is selected for domain ontology. As in [13] provides the semantic similarity measure by key word present between two sentences. It is a simply a keyword based matching technique, does not provide any semantic weight to the document. ...
Information Searching and retrieval is a challenging task in the traditional keyword based textual information retrieval system. In the growing information age, adding huge data every day the
searching problem also augmented. Keyword based retrieval system returns bulk of junk document irrelevant to query. To address the limitations, this paper proposed query terms along with semantic terms for information retrieval using multiple ontology reference. User query sometimes reflects multiple
domain of interest that persist us to collect semantically related ontologies. If no related ontology exists then WordNet ontology used to retrieve semantic terms related to query term. In this approach, classes on the ontology derived as semantic related text keywords, these keywords considered for rank the
documents.
