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Search Engine Optimization: Features and Application

Describe about search engine ranking factors and optimizations.

During the last few years, collection and ranking of data systems have changed drastically (Brin and Page 2012). The most notable changes have been observed in the approach of the search engines (SEs). Search engines like Yahoo!, Bing, and Google are now emphasizing on analysis of user experience and feedback for deploying their ranking system (Su et al. 2014). One of the latest methods for handling the daily changes in search engine ranking system is the Search Engine Optimization (SEO) method (Marszałkowski, Marszałkowski and Drozdowski 2014).

The main objective of this assignment is to identify the influences of different characteristics of SEO method on the ranking system of the search engines between two different domains. Moreover, this assignment follows the analysis of the integration semantics mechanism of the search engines. The main emphasis of the assignment is on the latest updates of the search engines’ ranking system in Web 2.0 and Web 3.0 frameworks.

1 Search Engine Optimization: Features and Application

Search Engine Optimization (SEO) is a mechanism, which is used to promote the functions of search engines. Search engines may be used to promote or expand a business but a website is to be made SEO friendly in order to promote the specific website (Mavridis and Symeonidis 2015). There are generally two types of SEO mechanisms. Off Page Optimization is used to recover back links from some specific external websites. In this optimization process, the webpages are linked to various forums like blogs, articles and others. Main role is played by webpage titles for optimizing any website with applicable keywords (Moreno and Martinez 2013). On the other hand, On Page Optimization is used to make essential changes in a specific website for making it SEO friendly. SEO friendly websites demand certain aspects. Meta tags should be placed in each and every webpage (Mavridis and Symeonidis 2015). The meta tags used must be exclusive and useful. Additionally, during optimization, the size of each webpage also counts. The pages with very high weights take more time to load in a selected browser, whereas, pages with less weight and low loading time get more priority (Shih et al. 2013). It is advised that website owners should use lesser images in their webpages. Moreover, content of a webpage must be exclusive, informative, without grammatical or technical errors and must contain relevant and specific keywords (Luh et al. 2016). There are four general factors that determine whether an website will be SEO friendly. These are: Occurrence, Prominence, Proximity and Significance (Killoran 2013).

Search Engine Ranking Factors: Survey of Ranking Factors

2 Search Engine Ranking Factors: Survey of Ranking Factors

With time, there have been massive upgrades on the Web structure, which only enables read purposes. From basic Web, Web 2.0 was developed for performing both read and write and later, Web 3.0, which enables user to read, write and infer (Ur Rehman and Khan 2013). These upgrades have enhanced ranking system of the search engines by a large scale. According to a survey conducted by Moz, some factors, which influence search engines ranking, are considered as genuine standards (Ghosh, Ipeirotis and Li 2014).

3 New Age Browsing and Crawling

All major search engines try to crawl into enhanced sources of data for the purpose of obtaining maximum possible information from the documents. In this way, the search engines can gain a lucid view of the information contained in the documents they crawled in (Marszałkowski, Marszałkowski and Drozdowski 2014).

For obtaining user publications and presenting suitable measures for the user’s scientific implications, headless browsing system has been employed. This system has been successful in parsing PDF and JavaScript files, which cannot be accessed by ordinary crawling method (Halavais 2013).

4 Web Content Semantics

Semantic analysis is the most important aspect of the SEO. Most of the latest approaches are generally focused on the basic link structure. Basically, semantic analysis can be further divided into authorship, markups and substance analysis (Moran and Hunt 2014).

5 LSHrank Architecture

This is designed to follow a number of operational steps. First, LSHrank generates queries and sends them to search engines for verification and results. The results are then ranked according to some metrics that are related to SEO (Mavridis and Symeonidis 2015). In this case, headless browsing is used and information are extracted for verification and analysis. Second, characteristics of the webpage are extracted and verified. On the other hand, LSHrank also considers characters related to semantic markups (Ghosh, Ipeirotis and Li 2014). Lastly, web document contents are extracted and used for creation and selection procedures.

6 Trials and Reports on Search Engine Ranking Process

Various experiments have been conducted on SE ranking system for identifying consequences of use of various webpage and semantic aspects in the main search engines (Brin and Page 2012). From the experimental analyses, conclusions have been drawn that the choice of domains influences search engines’ value factors. Basically, many webpages produce high averages of attribute values, which have been captured for the search engines (Luh et al. 2016). Semantic characteristics have been considered as probable ranking aspects and experimental results have shown that majority of the search engines have implemented Semantic Web in their general operation mechanisms (Su et al. 2014). However, some other metrics are also being designed as alternatives of SEO in providing examination measures.

New Age Browsing and Crawling

Significant Attributes of Search Engine Ranking System – From experimental results, several attributes can be established regarding search engine ranking system.

  • Different factors affect the search engines of different domains with a varying amount of basic influence (MarszaÅ‚kowski, MarszaÅ‚kowski and Drozdowski 2014). For instance, Semantic triples (Str) mainly influence Google, in which, number of microformats (Mf in short) and rel microformats (Nr in short) are used in software domain. It has been proven that any webpage domain influences signals of rankings that are considered by most chief search engines (Ur Rehman and Khan 2013).
  • In the Yahoo! and Google sports domain, association is shown by Authorities (Nga), where most of the websites provide news related to sports and the main contents come from reputed and verified journalists and authors (Killoran 2013).
  • From experimental results, it has been verified that Yahoo! mostly uses Authorities domain (Mavridis and Symeonidis 2015).
  • From the experiments, it has also been revealed that association of authorities is a positive activity, i.e. if rank goes down, the total number of authorities increases. This indicates a case of spam detection technique, which has been applied by major search engines. In these cases, multiple authorships are employed to attain a higher rating (Ghosh, Ipeirotis and Li 2014).
  • High association of rankings are exhibited by Internal Links (Li) in all domains that are available in the search engines, with the exception of sports domain of Google. It has been deduced that the link architecture of any webpage is necessary for determining operations of a search engine (Brin and Page 2012).
  • Google rankings have been found to have association with Scripts (Ns) in the sports domain. The positive association of Google in software engineering shows that it is a step taken by Google in order to detect spam and errors. In addition, it also looks to penalize and delete webpages, which contain hidden links in the JavaScript (Su et al. 2014).
  • Both Google and Bing rankings display high associative actions with Semantic triple (Str in short) in the domain of software engineering. Higher ranking can be achieved by having a high Str rating (Ur Rehman and Khan 2013).
  • Microformats (Mf in short) and Rel microformats (Nr in short) have exhibited negative association with Google and Bing search engine rankings in the domain of software engineering. This fact proves that Semantic Web data are mainly used by major search engines for algorithm purposes (Luh et al. 2016).
  • Finally, rankings of Yahoo! are not found to be associated with any semantic data, with the exception of authorities, where possibility remains that it might be a false indication as there are a very low amount of authorities (MarszaÅ‚kowski, MarszaÅ‚kowski and Drozdowski 2014).

7 Trials and Reports on Moz Metrics

Several experiments have been conducted for identifying the variations within the description of the metrics (Ghosh, Ipeirotis and Li 2014). MozRank is influenced by the total count of the available internal links. MozRank has revealed a large number of correlated scores in Spearman (Marszałkowski, Marszałkowski and Drozdowski 2014). Higher the MozRank score, higher the total number of internal links.

Conclusion and Future Work

Based on the analysis in this assignment, we can conclude that LSHrank mechanism can produce high quality Search Engine Optimization, which is mainly based on latest search engine metrics and LDA techniques. The basic focus of the assignment has been on Web 2.0 and 3.0 contents, including significant factors of Moz metrics. From the assignment, it is evident that the webpage and semantic qualities are both affected by the domain of queries and this in turn influences the score of rankings in various search engines. In addition, many search engines exhibit similar features as they implement similar link structures.

In future, further works can be done on the application of LSHrank tool to investigate designed data in the semantic level. Moreover, the prospects of further improving semantics related aspects using LSHrank can be explored.


Brin, S. and Page, L., 2012. Reprint of: The anatomy of a large-scale hypertextual web search engine. Computer networks, 56(18), pp.3825-3833.

Ghose, A., Ipeirotis, P.G. and Li, B., 2014. Examining the impact of ranking on consumer behavior and search engine revenue. Management Science,60(7), pp.1632-1654.

Halavais, A., 2013. Search engine society. John Wiley & Sons.

Killoran, J.B., 2013. How to use search engine optimization techniques to increase website visibility. Professional Communication, IEEE Transactions on, 56(1), pp.50-66.

Luh, C.J., Yang, S.A. and Huang, T.L.D., 2016. Estimating Google’s Search Engine Ranking Function from a Search Engine Optimization Perspective.Online Information Review, 40(2).’

MarszaÅ‚kowski, J., MarszaÅ‚kowski, J.M. and Drozdowski, M., 2014. Empirical study of load time factor in search engine ranking. Journal of Web Engineering, 13(1&2), pp.114-128.

Mavridis, T. and Symeonidis, A.L., 2015. Identifying valid search engine ranking factors in a Web 2.0 and Web 3.0 context for building efficient SEO mechanisms. Engineering Applications of Artificial Intelligence, 41, pp.75-91.

Moran, M. and Hunt, B., 2014. Search engine marketing, inc.: Driving search traffic to your company's website. IBM Press.

Moreno, L. and Martinez, P., 2013. Overlapping factors in search engine optimization and web accessibility. Online Information Review, 37(4), pp.564-580.

Shih, B.Y., Chen, C.Y. and Chen, Z.S., 2013. An empirical study of an internet marketing strategy for search engine optimization. Human Factors and Ergonomics in Manufacturing & Service Industries, 23(6), pp.528-540.

Su, A.J., Hu, Y.C., Kuzmanovic, A. and Koh, C.K., 2014. How to improve your search engine ranking: Myths and reality. ACM Transactions on the Web (TWEB), 8(2), p.8.

Ur Rehman, K. and Khan, M.N.A., 2013. The foremost guidelines for achieving higher ranking in search results through Search Engine Optimization. International Journal of Advanced Science and Technology,52, pp.101-110.

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