A Proposed Upgrade to the Keyword Based Searches Essay

`Google’. It is complicated in the initial phase of searching process, when it does not have any

More importantly, she mainly covers why Google is the most efficient search engine and how it operates more accurately than other engines and Web browsers. Kraft shares the same positive outlook on Google as the preferred search engine as is evidenced in this paper.

Discussion for search through websites. During my research through websites, I noticed that it is

(Ryen W. White, 2008) Any given Web internet searcher may give higher quality results than others for certain questions. Thusly, it is to clients ' greatest advantage to use different web crawlers. In this paper, we propose and assess a system that boosts clients ' hunt viable ness by guiding them to the motor that yields the best

Competition in the search industry is high. There are several search engines available, albeit Google holds the top percentage. Some of Google’s opposing forces are Yahoo!, Bing, and MSN search. The strongest is competitive rivalry and the weakest is buyer power. There is a big rivalry amongst search engines in gaining the newest advances and best technology to suit the customer. Buyer power is weak because there is no substitute for an online search engine. You could use an encyclopedia or something of that nature, but with online search engines,

Another method is context search. The context search adds context to the search criteria. “Context search helps improve the keyword search solution by addressing the problems present in keyword searches related to synonymy and polysemy. Synonymy is a common linguistic issue where different words are used to express the same concept, and polysemy is where the same word can have different meanings” (Fordham, 2013). The context search method combines several advance techniques in a simple user interface, making searches

As data continues to grow exponentially, machine learning will be critical for refining the relevance of search results. Using the Solr Learning to Rank toolkit, you can train search model algorithms offline, in a batch mode, using training data. When training the algorithm, you can use the supervisory mode and have more manual input into training the data or use a more data-driven approach and learn from the data signals. By running these signals through the Learning to Rank toolkit, it will learn and rank product results based on behaviors such as products that are purchased should rank higher in the search results than products that only receive page views.

Most frequently used search engine for this unit was Google as it is quite obvious but I also tried to use other search engines which I am not that familiar to such as Bing, Ask Jeeves, Dogpile, Answers, MSN Search, Yahoo and AOL.

1Research Scholar, 2Associate Professor, Department of Computer Science, AIM & ACT, Banasthali University, Banasthali-304022, email: jangid.divya@gmail.com

Keyword research is one of the important elements in SEO Campaign. It is in make or break stage i.e., if it is right then it takes to the top and vice verse. If it is then all of your efforts get useless. For that you need a keyword research tool to find the best keyword. In the past there is a keyword research tool called THE. This is the free keyword tool was provided by the Google. Unfortunately due to some reasons they stopped this good tool.

While in search engines user give words key and that search engine compiled such keywords from web page into database which user could query. These search engines continuously update their information to provides the accurate and actual result of users search. Example of search engines includes alta vista,lucos and excite.

1. The top three search engines include Google, Bing, and Yahoo. Google is so dominant that it has become synonymous with the word “search,” which is illustrated by the phrase “Google it.” It can become incredibly time consuming laboriously looking through page after page of search results for that piece of information you need. I have found a couple of tips to best use search engines, which saves time by narrowing search results to more closely fit what you are looking for. By putting quotes around an exact phrase being searched for, this tells the search engine to only give you results that exactly match what’s in quotes in that exact order. This gives you a more focused result. An example of this is “allergy free dog”. Another tip to narrow results is to search within a web address using the “inurl” command in Google. This allows you to search for web sites with a particular word in their URL. For example, if you are looking for results that have “cat” in their URL, you would enter inurl:cat into Google’s search bar. Lastly, I find using the Ctrl+F feature is helpful in searching for a word on a web page. It saves a tremendous amount of time in searching for exactly what you are looking for on a site. Just press Ctrl+F, then type any word you are looking for and that word is highlighted on the page.

Since 1998, Google has been the premier search engine when it comes to desktop home computing. Google allows users to search the internet for any key phrase they can imagine, providing ranked results based on a combination of popularity and relevance. After 18 years, Google is still utterly dominating its nearest rivals. With 3.5 billion searches performed per day worldwide (on average) and a typical 75% (or more) yearly market share, it doesn 't look like the California tech titan will be disappearing any time soon.

In Li et al. [2007], independent models for long-term and short-term user preferences are proposed to compose the user profiles. The long-term preferences are captured by using Google Directory, while the short-term preferences are determined from the user’s document preferences (the most frequently browsed documents).

Abstract — Keyword search is a useful tool for searching large corpuses whose structure is either unknown or constantly changing.RDF is the first W3C standard for enriching information resources of the web with detailed meta data. RDF can be modeled as a directed graph, where nodes are subjects/objects, and edge labels are predicates. The keyword search against an RDF graph looks for a sub graph that has minimum length to connect all the keywords from the common node. The existing approaches for searching is, keywords are mapped to nodes in the graph and their neighborhoods are explored to extract sub graph .Sometimes it lead to zero or less number of subgraphs.To address this issue, we propose a reverse method for searching. The experiment on RDF data shows that proposed method is more efficient.