Towards experience management for Search Engine Optimisation

Websites of Small and Medium-sized Enterprises (SMEs) can gain an added advantage by getting listed in the search engine’s results page during the search sessions of the searchers. The Search Engine Optimisation (SEO) enables websites to become visible in search engines during search sessions for its featured products or services. It generates additional revenue for the websites. SEO is a complex technique. Its knowledge and experience gained from optimising websites in the past is highly valuable and applicable to optimise websites. This paper dis- cusses the problem of optimisation of websites based on the experience gained by the authors from optimisation of several case study websites. Process models have been generated in order to capture experience of implementing essential elements of SEO and to explain the procedure of implementation of the fundamental on-page SEO techniques that yielded results for the case study websites

Search engine optimization for Small and Medium Enterprises (SMEs)

This paper shows how Small and Medium Enterprises (SMEs) can implement the Search Engine Optimization (SEO) elements on their websites and make them visible on the search engines. Four SMEs have been considered in this study. Two SMEs had absolutely no web presence whereas the other two had operational e-commerce websites. For the first two SMEs new websites were created, SEO techniques were implemented and these websites became visible on Google. On the other hand, advanced SEO techniques were implemented for the existing e-commerce websites which enabled them to gain higher ranking on search engines for their targeted keywords. On gaining these rankings on search engines the SMEs established their identity on the web, which would ultimately help them attract visitors and prospective clients searching for their products or services on the search engines. By undertaking this process it was shown that the websites' visibility on search engines have a positive contribution for the growth of SMEs’ businesses

Managing search engine optimisation experience using the INRECA methodology

This paper describes the reuse of Search Engine Optimisation (SEO) experience. The SEO domain is characterised by more than 200 factors leading to an obscurity of important factors. Such complex domains require experience-knowledge to enable the novice users adopt the domain. The Case Based Reasoning (CBR) approach is well suited to train new users in using this relatively new SEO technique to improve the visibility of their websites. Based on the principle of similarity, CBR enables the solution of similar recurring SEO problems for optimising websites for search engines. New users can effectively rely on SEO experience knowledge to solve new problems. Moreover, SEO techniques follow a similar procedure of implementation. Such procedural knowledge can be generalised and stored for future reference. For this purpose an experience base has been created to store SEO experience knowledge based on the principle of INRECA methodology. The experience is described using software process models. Until now the INRECA experience base has stored CBR system building experience. This research has extended the INRECA methodology for storing and retrieving SEO experience, taking into account the dynamic nature of the domain of SEO. An experiment illustrates the approach

Harnessing search engine optimization experience to enhance the visibility of websites

Research has identified that websites can gain an added advantage by getting listed in Search Engine results Pages (SERPs) during search sessions by searchers as SERPS refer targeted traffic to the websites. Search Engine Optimization (SEO) enables websites to become visible in search engines during search sessions for featured products or services. SEO is a complex technique which is directly affected by the ranking algorithms of search engines such as Google. Bearing in mind that Google employs in excess of 200 dynamic ranking factors in its algorithm it can be seen that optimization is not straightforward. Given this complex environment, websites find it difficult to initiate and implement SEO. SEO knowledge and experience gained from optimizing websites in the past is highly valuable and applicable to optimize websites both now and in the future. Therefore the main aim of the research in this thesis is to investigate the problem of optimization of websites using the prior experience gained through the optimization of several case study websites. To facilitate this, novel process models have been designed in order to capture the experience of implementing essential techniques of SEO and to explain the procedure of implementation of fundamental on-page SEO techniques that have been shown previously to yield results (i.e. increases in ranking) for past case study websites. Quantitative experiments and qualitative evaluation were undertaken to verify the efficacy of the novel process models through their application to case study websites. Mixed methods were used in order to answer the research questions, inductive experimental methods to produce, finesse and test the process models and qualitative enquiry through means of a focus group to gather peer review from professionals within the field who had previously been trained and conducted a trial using the process models. Implementation procedures of acknowledged essential on-page SEO techniques were identified from past case study websites, which have been represented in the novel process models designed in the current research and empirically investigated by applying them in the experimental case study websites. These models were applied through quantitative experiments that identified essential on-page SEO techniques which were then implemented in two experimental case study websites as per the procedures represented in the process models. These experiments have yielded positive results, resulting in establishing and/or enhancing the visibility of case study websites in SERPs. Further the implementation procedures of essential on-page SEO techniques were represented in the designed process models and stored in an SEO experience base on the principle of INRECA-II methodology. Results of the focus group suggest that the process models do achieve credible results (i.e. establishing and/or enhancing visibility of websites in SERPs) through their application and are suitable for use by both novices and professionals alike. Overall the results achieved from both the quantitative experiments and qualitative evaluation provide promising support to validate the created knowledge

Knowledge maintenance in myCBR

CBR systems, being knowledge based systems, process knowledge. Due to changes in the environment a CBR system’s knowledge model can become outdated, thus creating a need for constant maintenance of said knowledge model. In this paper, we describe an implementation of (semi-)automatic knowledge maintenance of two of the four knowledge containers of CBR systems, specifically case base maintenance and maintenance of similarity measures within the CBR system development SDK myCBR. We describe our approach to create, elicit and manage quality measures that are used to trigger maintenance actions if the quality measures fall below defined thresholds, indicating a declining efficiency/accuracy of a case base or particular similarity measure. We further detail on the implementation of our approach into myCBR Workbench to enable a knowledge engineer to incorporate the notion of maintenance already at the design stage of a CBR system. The approach relies on the notion of maintenance attributes to be able to measure the quality of case bases and similarity measures. Initial experiments using the newly introduced quality measurement attributes indicate that our approach is promising

Knowledge Maintenance in myCBR

CBR systems, being knowledge based systems, process knowledge.\ud Due to changes in the environment a CBR system's knowledge\ud model can become outdated, thus creating a need for constant maintenance\ud of said knowledge model. In this paper, we describe an implementation\ud of (semi-)automatic knowledge maintenance of two of the four\ud knowledge containers of CBR systems, especi�cally case base maintenance\ud and maintenance of similarity measures within the CBR system development\ud SDK myCBR. We describe our approach to create, elicit and\ud manage quality measures that are used to trigger maintenance actions if\ud the quality measures fall below de�fined thresholds, indicating a declining\ud e�fficiency/accuracy of a case base or particular similarity measure.\ud We further detail on the implementation of our approach into myCBR\ud Workbench to enable a knowledge engineer to incorporate the notion of\ud maintenance already at the design stage of a CBR system. The approach\ud relies on the notion of maintenance attributes to be able to measure the\ud quality of case bases and similarity measures. Initial experiments using\ud the newly introduced quality measurement attributes indicate that our\ud approach is promising

Leveraging metadata from information resources to provide a technical solution for populating electronic reading lists - UWL's experiments with Talis Aspire

This paper explains the importance of providing a better learning experience to higher education students by increasing the accessibility of module specific learning resources. In doing so the University of West London Library (UWL Library) implemented reading list software called Talis Aspire reading lists 1. As with any new software, this software also requires some input to make it work for the actual users, the academic staff and students of UWL. This paper explains the approach adopted by UWL Library to populate the reading lists by using existing data to classify information resources that are then re-used to create new, module specific, reading lists. As the Talis Aspire Software has been employed in a number of universities in the UK our approach provides an opportunity for reuse of our experience with the new approach we took for these universities in order to solve similar problems, should they occur. Our approach currently is able to classify information resources with high accuracy, ranging between 86 and 98 per-cent correct classiffications