Keyword Search Quieries

The « Page Rank », is the first successful explanation. The mechanism of the Page rank is globally the following, the most linked the website is, and the most popular it is, the highest the Page rank is. With those tools, the results are the most useful on the web.

Bi et al \cite{Rec:Bi} provides ranked related entities to the user query along with the results of the main entity. In order to do this, this articles makes use of user's search history, click history and knowledge base. A matrix is created comprising of the user information which connects to the entities along with the ranking, click results. A tri-linear function\cite{Rec:Bi} is defined mapping these details and which will be used to rank the related entities

Have you ever actually tried finding a needle in a haystack? Not in a proverbial sense — everyone’s done that, but an actual, physical piece of metal often used for sewing? If you have, feel free to stop reading. Light this paper on fire if you’ve got the urge, make a paper airplane, keep reading, whatever floats your boat. But for those of you who haven’t, allow me, a seasoned haystack searcher, to provide you with some insight on the matter.

There are many challenges that I am facing while searching data using the online Olivet library. I am using the Boolean operators, which are “used in electronic databases and other search engines to define the relationships between keywords or phrases” (Bui, 2014, p. 59).

I am surprised for my performance on this GTW because I only have two errors in total and none of them is a global error. However, I keep having same error from the first GTW, even in the latest GTW: word choice. I believe word choice has become an annoying for my writings because it appears again and again in my writing. Though it is not a serious problem for the whole writing, it reflects the problem that I have the wrong definition for wordings, or I can’t determine the appropriate words for the sentences. Totally, I have a weak word bank, which means I need to expand my understanding on more

Whenever you are writing an SEO-friendly article, Webpage content or essay keyword plays a crucial role. A keyword density checker is a tool to calculate the occurrence of the important keyword in your webpage or any content so that you can verify whether the keywords are appearing as desired.

(King-Lup Liu, 2001) Given countless motors on the Internet, it is troublesome for a man to figure out which web search tools could serve his/her data needs. A typical arrangement is to build a metasearch motor on top of the web indexes. After accepting a client question, the metasearch motor sends it to those fundamental web indexes which are liable to give back the craved archives for the inquiry. The determination calculation utilized by a metasearch motor to figure out if a web index ought to be sent the inquiry ordinarily settles on the choice in light of the web search tool agent, which contains trademark data about the database of a web search tool. Be that as it may, a hidden web index may not will to give the required data to the metasearch motor. This paper demonstrates that the required data can be evaluated from an uncooperative web crawler with great exactness. Two bits of data which license precise web crawler determination are the quantity of reports filed by the web index and the greatest weight of every term. In this paper, we display systems for the estimation of these two bits of data.

When comparing the collection of information from a search engine such as Google, and a database such as EBSCO, one can notice many differences and a proper evaluation and assessment is necessary. An evaluation can be done by looking first at the accessibility of an article pertaining to a specific type of information, such as on the

A search engine is a website or program used to find information on the internet. That is a very basic and semi-vague definition of what a search engine is and what they do. More specifically search engines actually search the internet based on a keyword or phrase that the user inputs into it. Once someone has begun to search for something on a search engine. The search engine finds relevant information on the keyword or phrase that has already been gathered and organized. The gathered information is then displayed to the user as search engine result pages (or SERPs) based on how relevant it is to the search information they entered. That may be presented as websites, videos, sound files, pictures, or other types of information or files. Also as more users search for different key words or phrases the search engine beings to group search results that go along with similar keywords and phrases so that future users can have access to the most up to date and relevant files and information that is available on the internet.

A person’s name is the one thing that cannot be taken away from him or her. It is the only thing that stays with us from the day we are born until the day we take our last breath. Emily Annabelle Pierce is the name given to me by my parents. I did not choose this name or change it in any way. My parents named me this before they knew me, but my names parallel a lot with my character.

Text Retrieval and techniques are modified by many commercial and many open sources in the domain of information retrieval. Text retrieval refers to the process of searching for texts, information within collections, or metadata about documents. It is assigned to answer for relevant documents, not just simple matches to patterns. When considering indexing and searching applications, users may find and consider among many products available on the market. Mainly, the products can be grouped into two categories. The first of these categories are information retrieval libraries that can be easily developed and embedded into application. The second consists of ready to apply indexing and searching applications that are basically designed to work with particular types of data, and are therefore less flexible (Molková 2011). Some of the famous libraries cover text retrieval topic are introduced in next section.

If we search for an answer to a question in a typical search engines such as Google, Bing, or Yahoo, it usually gives us relevant pages based on the key words in the query. We may need to follow several links or pages to reach a document providing a relevant answer. If we can store such search pathways to an answer for a given user query and reuse it for future searches it may speed up this process. Our question answering system motivated by reuse of prior web search pathways to yield an answer a user query. We represent queries and search pathways in a semi-structured format that contains query terms and referenced classes within a realm based ontology. First part of my research is to build a system that can automatically tag the terms in a user query to relevant classes from a domain-based ontology. The other part is to rank the prior searches (contains user queries, assigned classes, and search pathways) stored in the database based on the

Made out of Web locales interconnected by hyperlinks, the World Wide Web can be seen as an enormous yet tumultuous wellspring of data. For choice making numerous business applications need to rely on upon web keeping in mind the end goal to total data from various sites. Programmed information extraction assumes an essential part in preparing results gave via internet searchers in the wake of presenting the question by client. presently days "site" has begun keeping more significance to our life. without which it is hard to oblige even one day .so it has turned into the need that the site ought to be more enlightening and alluring . be that as it may, the sites are created and just grew purposely or unwittingly

Query processor system parses the query and interprets the meaning of the end-user’s query terms. This enables the construction of a meaningful query. Before any actual query re-formulation, the mapping between the vocabulary of the ontologies and the query is required. The mapping is indispensable for retrieval improvement using ontology based query approaches. The first step of the processor is to identify the set of ontologies likely to provide the information requested by the user. Hence it searches for near syntactic matches within the ontology indexes, using lexically related words obtained from WordNet [27] and from the ontologies, used as background knowledge sources. It identifies the subject, predicate and object, which is used to generate the DL query and runs it against the ontology to attempt to

The main aim of this project is to research on the integration of “Natural Language Processing “ and information systems engineering to enhance query retrieval in natural language processing.