Tampilkan postingan dengan label search engines optimization. Tampilkan semua postingan
Tampilkan postingan dengan label search engines optimization. Tampilkan semua postingan
Minggu, 17 Juni 2012
What is SEO and why is it so important?
SEO is an acronym for Search Engine Optimizing and refers to a set of good practices that teach you how to improve your website so that it would be easily tracked, indexed and ranked by search engines. Getting your site indexed by Google and other major search engines will help you attract visitors and increase traffic.
It is the process of increasing the amount of visitors to a Web site by ranking high in the search results of a search engine. The higher a Web site ranks in the results of a search, the greater the chance that that site will be visited by a user. It is common practice for Internet users to not click through pages and pages of search results, so where a site ranks in a search is essential for directing more traffic toward the site.
SEO helps to ensure that a site is accessible to a search engine and improves the chances that the site will be found by the search engine.
Basic SEO Tips:
1. Insert keywords within the title tag so that search engine robots will know what your page is about. The title tag is located right at the top of your document within the head tags.
2. Use the same keywords as anchor text to link to the page from different pages on your site. This is especially useful if your site contains many pages. The more keywords that link to a specific page the better.
3. Make sure that the text within the title tag is also within the body of the page. It is unwise to have keywords in the title tag which are not contained within the body of the page.
4. Do not use the exact same title tag on every page on your website. Search engine robots might determine that all your pages are the same if all your title tags are the same. If this happens, your pages might not get indexed.
5. Do not spam the description or keyword meta tag by stuffing meaningless keywords or even spend too much time on this tag.
6. Do not link to link-farms or other search engine unfriendly neighborhoods. A good rule of thumb is if your pages do not contain any words that reflect the content of the site you are linking to, do not link to it.
7. Do not use doorway pages. Doorway pages are designed for robots only, not humans.
8. Title tags for text links. Insert the title tag within the HTML of your text link to add weight to the link and the page where the link resides. This is like the alt tag for images.
9. Describe your images with the use of the alt tag. This will help search engines that index images to find your pages and will also help readers who use text only web browsers.
10. Submit to the search engines yourself. Do not use submission service or submission software. Doing so could get your site penalized or even banned.
So in this way Search Engine Optimization, is important, and helps to bring more visitors and Link Exchanging, is important because it can drive more visitors then search engines, once established. Directories ad valuable content for your customers/visitors, and this it improves your SEO.
Visit us:
http://www.halfvalue.com/top-articles/seo-resources.html
Selasa, 18 Oktober 2011
Why? - Search Engines
What is a search engine?
Here I am going to define the Search engine with respect of two view as given below :
1. In the respect of Visitor (Who want getting best results)
2. In the respect of Website (Who want make best results)
In the respect of visitorthe Search engines enable to their visitors to enter a specific word or term, known as keywords. Once submitted, all pages containing those keywords that can be found in the search engine's directory are listed on the search engine result pages.
In the respect of websitewho wants listing to search engine a search engine is a website such as Google on which people can search for other websites on the Internet. Search engines do not include sites manually, but have an automated "search engine spiders" that "crawls" the web through links. If your website has just one link to it, it will eventually be found and indexed in a search engine. The term "search engine" is often used generically to describe both crawler-based search engines and human-powered directories.
There are two type of Search Engine:
1. Crawler-based search engines
2. Human-powered directories
These two types of search engines gather their listings in radically different ways.
Crawler-Based Search Engines
Crawler-based search engines, such as Google, create their listings automatically. They "crawl" or "spider" the web, then people search through what they have found.
If you change your web pages, crawler-based search engines eventually find these changes, and that can affect how you are listed. Page titles, body copy and other elements all play a role.
Human-Powered Directories
A human-powered directory, such as the Open Directory, depends on humans for its listings. You submit a short description to the directory for your entire site, or editors write one for sites they review. A search looks for matches only in the descriptions submitted.
Changing your web pages has no effect on your listing. Things that are useful for improving a listing with a search engine have nothing to do with improving a listing in a directory. The only exception is that a good site, with good content, might be more likely to get reviewed for free than a poor site.
Visit us:
http://www.halfvalue.com/top-articles/seo-resources.html
Here I am going to define the Search engine with respect of two view as given below :
1. In the respect of Visitor (Who want getting best results)
2. In the respect of Website (Who want make best results)
In the respect of visitorthe Search engines enable to their visitors to enter a specific word or term, known as keywords. Once submitted, all pages containing those keywords that can be found in the search engine's directory are listed on the search engine result pages.
In the respect of websitewho wants listing to search engine a search engine is a website such as Google on which people can search for other websites on the Internet. Search engines do not include sites manually, but have an automated "search engine spiders" that "crawls" the web through links. If your website has just one link to it, it will eventually be found and indexed in a search engine. The term "search engine" is often used generically to describe both crawler-based search engines and human-powered directories.
There are two type of Search Engine:
1. Crawler-based search engines
2. Human-powered directories
These two types of search engines gather their listings in radically different ways.
Crawler-Based Search Engines
Crawler-based search engines, such as Google, create their listings automatically. They "crawl" or "spider" the web, then people search through what they have found.
If you change your web pages, crawler-based search engines eventually find these changes, and that can affect how you are listed. Page titles, body copy and other elements all play a role.
Human-Powered Directories
A human-powered directory, such as the Open Directory, depends on humans for its listings. You submit a short description to the directory for your entire site, or editors write one for sites they review. A search looks for matches only in the descriptions submitted.
Changing your web pages has no effect on your listing. Things that are useful for improving a listing with a search engine have nothing to do with improving a listing in a directory. The only exception is that a good site, with good content, might be more likely to get reviewed for free than a poor site.
Visit us:
http://www.halfvalue.com/top-articles/seo-resources.html
Sabtu, 23 Juli 2011
Latent Semantic Analysis (LSA) and Search Engines (SEO)
By Jose Nuñez
Latent Semantic Analysis (LSA) is applied by taking millions of web pages, where the search engines can learn which words are related and which noun concepts relate to one another. Searh Engines are considering related terms and recognizing which terms that frequently occur together, maybe on the same page, or in close enough proximity. So it is mainly used for language modeling or most other applications.
Part of this process involves looking at the copy content of a page, or included on the links, and looking through the ways on how they are related. Latent Semantic Analysis (LSA) is based on the well known Singular Value Decomposition Theorem from Matrix Algebra but applied to text. That is why some of the semantic analysis that is done at the page content level it may also be done on the linkage data.
LSA represents the meaning of words as a vector, thus calculating word similarity. Iit has been very efficient to that purpose, and is still used. Regarding text for this application, is considered linear. This makes LSA slow due to using a matrix method called Singular Value Decomposition to create the concept space. But it does only address semantic similarity and not ranking, which is the SEO priority.
Scientific SEOs have a similar goal. They try to discover which words and phrases are most semantically linked together for a given keyword phrase, so when Search Engines crawl the web, they find that links to particular pages and content within them is semantically related to other information that is currently in their database. So, in conclusion, LSA calculates a measure of similarity for words based on possible occurrence patterns of words in documents and on how often words appear in the same context or together with the same set of common elements.
Latent Semantic Analysis (LSA) is applied by taking millions of web pages, where the search engines can learn which words are related and which noun concepts relate to one another. Searh Engines are considering related terms and recognizing which terms that frequently occur together, maybe on the same page, or in close enough proximity. So it is mainly used for language modeling or most other applications.
Part of this process involves looking at the copy content of a page, or included on the links, and looking through the ways on how they are related. Latent Semantic Analysis (LSA) is based on the well known Singular Value Decomposition Theorem from Matrix Algebra but applied to text. That is why some of the semantic analysis that is done at the page content level it may also be done on the linkage data.
LSA represents the meaning of words as a vector, thus calculating word similarity. Iit has been very efficient to that purpose, and is still used. Regarding text for this application, is considered linear. This makes LSA slow due to using a matrix method called Singular Value Decomposition to create the concept space. But it does only address semantic similarity and not ranking, which is the SEO priority.
Scientific SEOs have a similar goal. They try to discover which words and phrases are most semantically linked together for a given keyword phrase, so when Search Engines crawl the web, they find that links to particular pages and content within them is semantically related to other information that is currently in their database. So, in conclusion, LSA calculates a measure of similarity for words based on possible occurrence patterns of words in documents and on how often words appear in the same context or together with the same set of common elements.
Senin, 18 April 2011
Latent Semantic Indexing and Search Engines Optimimization (SEO)
By Jose Nuñez
The closest search engines have come to actual applications of this technology so far is know as "Associative Indexing" and it is put in effect under Stemming, or the indexing of words on the basis of their uninflected roots (plurals, adverbs, and adjectival forms are reduced to simple noun and verb forms before indexing).
Latent Semantic Analysis (LSA) is a technique in natural language processing, in particular in vectorial semantics, invented in 1990 [1] by Scott Deerwester, Susan Dumais, George Furnas, Thomas Landauer, and Richard Harshman. In the context of its application to information retrieval, it is sometimes called Latent Semantic Indexing (LSI).
Here are some quick facts about Latent Semantic Indexing:
1. LSI is 30% more effective than popular word matching methods.
2. LSI uses a fully automatic statistical method (Singular Value Decomposition)
3. It is very effective in cross-languages retrievals.
5. LSI can retrieve relevant information that does not contain query words.
6. It finds more relevant information than other methods.
Latent Semantic Indexing adds an important step to the document indexing process. In addition to recording which keywords a document contains, the method examines document collections as a whole, to see which others do contain some of those same words. LSI considers documents that have many words in common to be semantically close, and ones that have few words in common to be semantically distant. This method correlates surprisingly well with how a human being looking at content, classifies multiple documents.
The closest search engines have come to actual applications of this technology so far is know as "Associative Indexing" and it is put in effect under Stemming, or the indexing of words on the basis of their uninflected roots (plurals, adverbs, and adjectival forms are reduced to simple noun and verb forms before indexing).
Latent Semantic Analysis (LSA) is a technique in natural language processing, in particular in vectorial semantics, invented in 1990 [1] by Scott Deerwester, Susan Dumais, George Furnas, Thomas Landauer, and Richard Harshman. In the context of its application to information retrieval, it is sometimes called Latent Semantic Indexing (LSI).
Here are some quick facts about Latent Semantic Indexing:
1. LSI is 30% more effective than popular word matching methods.
2. LSI uses a fully automatic statistical method (Singular Value Decomposition)
3. It is very effective in cross-languages retrievals.
5. LSI can retrieve relevant information that does not contain query words.
6. It finds more relevant information than other methods.
Latent Semantic Indexing adds an important step to the document indexing process. In addition to recording which keywords a document contains, the method examines document collections as a whole, to see which others do contain some of those same words. LSI considers documents that have many words in common to be semantically close, and ones that have few words in common to be semantically distant. This method correlates surprisingly well with how a human being looking at content, classifies multiple documents.
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