For example, a chef would like to tweak the time and the price sliders in order to find the most rated recipes that fit to her time or budget limitations. This bunch of facets may overwhelm the average user, however for cuisine and nutrition experts it may serve as a tool to filter search results for their specific demands. Although it can yield pretty good results for the the basic keyword search, its power lies in the facets (i.e filters) located in its left sidebar. It uses even more semantic annotation than Recipe Puppy (e.g. This recipes search engine is focused on some of the most authoritative websites like Allrecipes and Epicurious. In addition, this search engine has an "Advanced Search" page that enables filtering search results by rating or by number of ingredients.
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In particular, Recipe Puppy utilize the recipe markup to search recipes by their ingredients (as opposed to keyword search). Recipe Puppy is an example for the utilization of the semantic web new standards to search engines. The additional ingredients are displayed as hyperlinks with + sign so clicking on one them will add it to the Your Ingredients box. In addition to the to the traditional excerpts section, every search result snippet has on its bottom line a list of ingredients with the desired ingredients in boldface type. When clicking on a term twice, it will list only recipes that not use this ingredient. In addition, when clicking on a term in the Your Ingredients box in the right sidebar once, the search engine will list only recipes that use this ingredient. The search engine displays the search results that match completely to the query terms on top and the results that match partially afterwards. The cloud is updated whenever a term is added to the Search by Ingredients or Keyword Search boxes. This ingredient based recipe search engine has a dynamic tag cloud of ingredients on the right sidebar. These would give the readers some clues to find advanced features in other search UIs, or at least supply them with tools to utilize these search engines for best results. In this post will introduce three valuable recipes search engines and elaborate some of their advanced features. Google advanced operators in the case of CSE). In addition, when we deal with search engine that was built on top of well known platform like Google Custom Search Engine (CSE), we may use the platform advanced features (e.g. One hint to shorten the learning curve might be to look for faceted search sidebar. Even though, a fine topical search engine may yield high-quality and relevant results even without using its advanced features. travel search engines ) or turn out to be of low quality in terms of irrelevant or poor search results.
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However, for topical search engines this learning curve might be unworthy, especially if this search engine is seldom used (e.g. To deepen its use, its community of users is recommended through frequent asked questions.When talking about general search engines like Google or Yahoo!, the long learning curve of advanced search features might prove to be worthwhile. The performance of Google Data Search is similar to other Google search engines, allowing simple and advanced searches in thousands of data repositories in a simultaneous manner.
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Those are coming from reference sources such as public and private organizations, universities and any organization that has open repositories or shares data from studies, research and open analysis. Google Data Search is presented as a specialized search engine in open data, allowing retrieving data sets accessible through Internet. The so-called data revolution is driving the arrival of new specialized tools in extracting the maximum value of open access or open data. In recent years, the evolution of Internet is driving the digital transformation of organizations, industries and business models. Google Data Search, open data specialised search engine Data revolution is driving new tools development such as Google Dataset Search, a search engine specialized in open datasets.