A New Search Engine Targeting Scientific Researchers Touts AI
January 27, 2017
The article titled How a New AI Powered Search Engine Is Changing How Neuroscientists Do Research on Search Engine Watch discusses the new search engine geared towards scientific researchers. It is called Semantic Scholar, and it uses AI to provide a comprehensive resource to scientists. The article explains,
This new search engine is actually able to think and analyze a study’s worth. GeekWire notes that, “Semantic Scholar uses data mining, natural language processing, and computer vision to identify and present key elements from research papers.” The engine is able to understand when a paper is referencing its own study or results from another source. Semantic Scholar can then identify important details, pull figures, and compare one study to thousands of other articles within one field.
This ability to rank and sort papers by relevance is tremendously valuable given the vast number of academic papers online. Google Scholar, by comparison, might lead a researcher in the right direction with its index of over 200 million articles, it simply does not have the same level of access to metadata that researchers need such as how often a paper or author has been cited. The creators of Semantic Scholar are not interested in competing with Google, but providing a niche search engine tailored to meet the needs of the scientific community.
Chelsea Kerwin, January 27, 2017
Obey the Almighty Library Laws
January 23, 2017
Recently I was speaking with someone and the conversation turned to libraries. I complimented the library’s collection in his hometown and he asked, “You mean they still have a library?” This response told me a couple things: one, that this person was not a reader and two, did not know the value of a library. The Lucidea blog discussed how “Do The Original 5 Laws Of Library Science Hold Up In A Digital World?” and apparently they still do.
S.R. Ranganathan wrote five principles of library science before computers dominated information and research in 1931. The post examines how the laws are still relevant. The first law states that books are meant to be used, meaning that information is meant to be used and shared. The biggest point of this rule is accessibility, which is extremely relevant. The second laws states, “Every reader his/her book,” meaning that libraries serve diverse groups and deliver non-biased services. That still fits considering the expansion of the knowledge dissemination and how many people access it.
The third law is also still important:
Dr. Ranganathan believed that a library system must devise and offer many methods to “ensure that each item finds its appropriate reader”. The third law, “every book his/her reader,” can be interpreted to mean that every knowledge resource is useful to an individual or individuals, no matter how specialized and no matter how small the audience may be. Library science was, and arguably still is, at the forefront of using computers to make information accessible.
The fourth law is “save time for the reader” and it refers to being able to find and access information quickly and easily. Search engines anyone? Finally, the fifth law states that “the library is a growing organism.” It is easy to interpret this law. As technology and information access changes, the library must constantly evolve to serve people and help them harness the information.
The wording is a little outdated, but the five laws are still important. However, we need to also consider how people have changed in regards to using the library as well.
Whitney Grace, January 23, 2017
Some Things Change, Others Do Not: Google and Content
January 20, 2017
After reading Search Engine Journal’s, “The Evolution Of Semantic Search And Why Content Is Still King” brings to mind how there RankBrain is changing the way Google ranks search relevancy. The article was written in 2014, but it stresses the importance of semantic search and SEO. With RankBrain, semantic search is more of a daily occurrence than something to strive for anymore.
RankBrain also demonstrates how far search technology has come in three years. When people search, they no longer want to fish out the keywords from their query; instead they enter an entire question and expect the search engine to understand.
This brings up the question: is content still king? Back in 2014, the answer was yes and the answer is a giant YES now. With RankBrain learning the context behind queries, well-written content is what will drive search engine ranking:
What it boils to is search engines and their complex algorithms are trying to recognize quality over fluff. Sure, search engine optimization will make you more visible, but content is what will keep people coming back for more. You can safely say content will become a company asset because a company’s primary goal is to give value to their audience.
The article ends with something about natural language and how people want their content to reflect it. The article does not provide anything new, but does restate the value of content over fluff. What will happen when computers learn how to create semantic content, however?
Whitney Grace, January 20, 2016
How Google Used Machine Learning and Loved It
January 16, 2017
If you use any search engine other than Google, except for DuckDuckGo, people cringe and doubt your Internet savvy. Google has a reputation for being the most popular, reliable, and accurate search engine in the US. It has earned this reputation, because, in many ways, it is the truth. Google apparently has one upped itself, however, says Eco Consultancy in the article, “How Machine Learning Has Made Google Search Results More Relevant.”
In 2016, Google launched RankBrain to improve search relevancy in its results. Searchmatics conducted a study and discovered that it worked. RankBrain is an AI that uses machine learning to understand the context behind people’s search. RankBrain learns the more it is used, similar to how a person learns to read. A person learning to read might know a word, but can understand what it is based off context.
This increases Google’s semantic understanding, but so have the amount of words in a search query. People are reverting to their natural wordiness and are not using as many keywords. At the same time, back linking is not as important anymore, but the content quality is becoming more valuable for higher page rankings. Bounce rates are increasing in the top twenty results, meaning that users are led to a more relevant result than pages with higher optimization.
RankBrain also shows Google’s growing reliance on AI:
With the introduction of RankBrain, there’s no doubt that Google is taking AI and machine learning more seriously. According to CEO, Sundar Pichai, it is just the start. He recently commented that ‘be it search, ads, YouTube, or Play, you will see us — in a systematic way — apply machine learning in all these areas.’ Undoubtedly, it could shape more than just search in 2017.
While the search results are improving their relevancy, it spells bad news for marketers and SEO experts as their attempts to gain rankings are less effective.
Whitney Grace, January 16, 2016
-
- Subscribe to Beyond Search
Feature archive
News archive
-
