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Can AI-Based Search Tools Replace Google Scholar?

The industrial revolution brought significant changes along with it. It also managed to alter to the entire way of living for many. The industrial revolution was an era of massive innovation. This is when humans starting adapting to machine technologies as well. Machines started to replace human labour to a great extent. The use of machines also paved the way for mass production, and efficiency. Changes in technology disrupt the established structures to a great degree. Yet it’s important to highlight that technology change has its pros, and cons too. The increased use of Artificial intelligence has also brought forth significant changes especially in AI-based search tools.

Artificial Intelligence has enabled the processing of data on a large scale. It is currently in use by a lot of industries, and organisations. Using innovation in tools to assist with daily tasks is essential for humanity. In the present times as well, we’re witnessing such innovations regarding automation. AI-based search tools are currently in use by academics, and researchers as well. Now the question arises whether these tools can replace Google scholar, or not. This article aims to make a comparative analysis of AI-based search tools. It will inform the readers about the emerging AI-based search tools as well. It will also address the arising concerns for whether these tools will replace Google Scholar, or not.

Semantic Scholar

Semantic Scholar is aiming to outdo Google Scholar. It is also expanding its collection of papers to do this. It aims to include almost 10 million research papers that are used by millions of dissertation writers. These research papers, however, are limited to computer science, and Nano-science domains. Semantic Scholar has allied with Microsoft. Scientists also believe that it will be a game-changer. They predict that it will process information in an efficient manner.

Currently, Google Scholar covers more than 200 million articles. Google Scholar uses keywords searches, but it cannot access metadata, and citation metrics. Semantic Scholar on the other hand, seeks to rank and categorise papers in a more rigorous manner. This AI-based search engine claims to use advanced filters to search for publications and papers. Its makers boast that it’ll filter out papers and citations as per their relevance. It will also filter them out on the basis of their importance. This aspect will help the researchers in coming across the research papers that are important to their research. It also aims to rank articles according to their citation count for highlighting that they’re in demand.

Initially, Semantic Scholar was restricted to 3 million papers of Computer Science. After collaborating with Allen Institute for Brain Science, it expanded its reach. The AI-based search engine has since then added millions of papers and introduced new filters too. But these new filters are limited to neurology, and medicine studies domains only.

Microsoft Academic

Semantic Scholar is not the sole competitor of Google Scholar. This is because Microsoft giant also launched its AI-based search engine named Microsoft Academic. Microsoft Academic uses Application Programming Interface (API) for its search algorithms. Search algorithms using API, and Open Academic society makes it quite convenient for researchers to find relevant data.

For that purpose, Microsoft has partnered up with Microsoft Research A12. Kaunsan Wang, responsible for carrying out this project, claims that Microsoft Academic is much better than Semantic Scholar. It is powered by semantic search, and uses basic filters such as the author’s name, journal, and field of study as well. It can access approximately 160 million publications. Also, It provides more personalised options to researchers by compiling the most cited works. It has a leader board of notable scientists from each discipline, and sub-discipline. The AI-based search algorithm ranks, and sorts the papers as per their citation counts.

In a nutshell, Microsoft Academic aims to provide the following services to researchers across the globe;

  • Assimilation of Google Scholar, and Semantic Scholar’s massive scope in search algorithms.
  • Structural organisation of bibliometric databases such as Scopus, and Web of Science.
  • Providing more personalised options to users by notifying them about highly cited research papers relevant to their field.

Implications for Google Scholar

According to Coursework writing services experts, key players in AI-based search tools include Google Scholar, Semantic Scholar, and Microsoft Academic. They’re more aware of this since they use such platforms on a daily basis for conducting research. There are some newcomers as well, although they’re still in the developing phase. Following are the new entrants in AI-based search tools’ domain;

  • Yewno
  • Sparrho
  • UNSILO

Conclusion

For the time being, Google Scholar still holds a prominent position in terms of scholarly research search engines. It is the favourite of many academics too. Yet change is a constant phenomenon in the ever-changing world. Innovation thrives upon identifying gaps in the market.

Google Scholar uses keywords, and it has no access to metadata, and citation metrics. On the other hand, its competitors are innovating and providing more personalised options to their users. The advanced filters combined with Application Programming Interface (API) will enhance the search effort within this context. Google Scholar can help the scholars in finding relevant studies, but it cannot tell them how many times that work was cited. So it would be fair to conclude that Google Scholar is not comprehensive. And that its competitors can exploit these lacunas to replace it in the near future.

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