## 7 Conclusions and Outlook  
During this project, we essentially developed a program for assisted search engine use with the additional value of generating short, extractive summaries of search results in the context of recommending technologies based on keywords. The design science research method has the objective of yielding both knowledge as a contribution to science as well as a problem-solving artefact that can be used in a day-to-day task [14]. In our case, we managed to develop an artefact that if further improved can be of help for a user who must retrieve larger amounts of summarized information from the web. In this way, our study indicates the potential of automatic knowledge discovery from the large amounts of unstructured information found in the web by means of natural language processing and artificial intelligence-based algorithms. Techniques from web crawling and scraping can effectively search and extract unstructured text information which can further be processed by text summarization algorithms in the context of recommender systems applications. Our research question RQ1 can be answered with yes as our research of secondary sources during our literature review, together with the insights we have gained into developing such systems throughout the development stage of this project, have shown that the technology is available and that collective knowledge on the relevant topics is vast. However, it must be clarified that in the end, despite finding it possible, no technology recommender system in the truest sense of the word has been developed by us. This is also due to the reason that our solution is very generic and is not specialized for the search of technology. Similarly, in view of our own solution, research question RQ2 (effectivity) would have to be answered with a no since the results yielded by the program were not of satisfactory quality, mainly due to the reason of a relatively random extraction of text from the content source with sentences that are complete but not necessarily arranged in a meaningful order. Another indicator of low-quality results was the fact that some of the summaries did not match the search terms very well. However, as to effectivity and usefulness, our artefact delivers value for one newly identified use case: since it extracts summaries from websites that use the search terms in any possible way, the screening of summaries that are generated by our program offers the user a unique way to find out in which contexts the search term is also used, so that a user can efficiently find complementary products and services as well as substitutes. It could be argued however that this can also be achieved by performing a simple Google search which however has the disadvantage of requiring a user to screen each website since the meta descriptions displayed by Google on the search results list contain less information than our extractive summaries. Research question RQ3 can be answered positively, as the time to retrieve summarized information from the web could be considerably reduced with our artefact if compared to the execution of the manual process using only the Google search engine. Finally, we can partially approve our hypothesis as the technologies of web crawling, and natural language processing, including web scraping have been found to be suitable for development projects that aim at simplifying the search for and understanding of technology. With regards to effectivity however, additional technology such as machine learning and/or advanced statistical methods within and/or outside the scope of NLP would have to be considered in the development of such an artefact. In order to fulfil the expectations a user could have toward a technology recommender system, further programming and research would be required. The following future work could provide additional benefits to users.