Visualizing a Web of Sites

February 6, 2017

While the World Wide Web is clearly a web, it has not traditionally been presented visually as such. Digital Trends published an article centered around a new visualization of Wikipedia, Race through the Wikiverse for your next internet search. This web-based interactive 3D visualization of the open source encyclopedia is at Wikiverse.io. It was created by Owen Cornec, a Harvard data visualization engineer. It pulls about 250,000 articles from Wikipedia and makes connections between articles based on overlapping content. The write-up tells us,

Of course it would be unreasonable to expect all of Wikipedia’s articles to be on Wikiverse, but Cornec made sure to include top categories, super-domains, and the top 25 articles of the week.

Upon a visit to the site, users are greeted with three options, each of course having different CPU and load-time implications for your computer: “Light,” with 50,000 articles, 1 percent of Wikipedia, “Medium,” 100,000 articles, 2 percent of Wikipedia, and “Complete,” 250,000 articles, 5 percent of Wikipedia.

Will this pave the way for web-visualized search? Or, as the article suggests, become an even more exciting playing field for The Wikipedia Game? Regardless, this advance makes it clear the importance of semantic search. Oh, right — perhaps this would be a better link to locate semantic search (it made the 1 percent “Light” cut).

Megan Feil, February 6, 2017

Alleged Google-Killer Omnity Is Now Free

January 31, 2017

Omnity is a search engine designed to deliver more useful results than one obtains from outfits like Google. The company, according to “Omnity Is a Semantic Mapping Search Engine That’s Now Offered for Free”,

…sometimes there’s a need for another kind of search, namely to locate documents that aren’t explicitly linked or otherwise referenced between each other but where each contains the same rare terms. In those cases, a method called “semantic mapping” becomes valuable, and there’s now a free option that does just that…

My query for “Omnity” returned these results:

image

When I checked the links in the central display and scanned the snippet in the left hand sidebar, I did not locate many relevant results. I noted a number of NASA related hits. A bit of checking allowed me to conclude that a company called Elumenati once offered product called Omnity.

If you want to experiment with the system, point your browser thing at www.omnity.io. You will have to register. Once you verify via an email, you are good to go.

We don’t have an opinion yet because we don’t know the scope of the index nor the method of determining relevance for an entity. The “semantic” jargon doesn’t resonate, but that may be our ignorance, ineptitude, or some simple interaction of our wetware.

Omnity may have some work to do before creating fear at the GOOG.

Stephen E Arnold, January 31, 2017

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

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

Associative Semantic Search Is a New Technology, Not a Mental Diagnosis

December 6, 2016

“Associative semantic” sounds like a new mental diagnosis for the DSM-V (Diagnostic and Statistical Manuel of Mental Disorders), but it actually is the name of a search technology that sounds like it amplifies the basic semantic searchAistemos has the run down on the new search technology in the article, “Associative Semantic Search Technology: Omnity And IP.”  Omnity is the purveyor of the “associative semantic search” and it makes the standard big data promise:

…the discovery of otherwise hidden, high-value patterns of interconnection within and between fields of knowledge as diverse as science, medicine, engineering, law and finance.

All of the companies centered on big data have this same focus or something similar, so what does Omnity offer that makes it stand out?  It proposes to find connections between documents that do not directly correlate or cite one another.  Omnity uses the word “accelerate” to explain how it will discover hidden patterns and expand knowledge.  The implications mean semantic search would once again be augmented and more accurate.

Any industry that relies on detailed documents would benefit:

Such a facility would presumably enable someone to find references to relevant patents, technologies and prior art on a far wider scale than has hitherto been the case. The legal, strategic and commercial implications of being able to do this, for litigation, negotiation, due diligence, investment and forward planning are sufficiently obvious for us not to need to list them here.

The article suggests those who would most be interested in Omnity are intellectual property businesses.  I can imagine academics would not mind getting their hands on the associative semantic search to power their research or law enforcement could use it to fight crime.

Whitney Grace, December 6, 2016

The Noble Quest Behind Semantic Search

November 25, 2016

A brief write-up at the ontotext blog, “The Knowledge Discovery Quest,” presents a noble vision of the search field. Philologist and blogger Teodora Petkova observed that semantic search is the key to bringing together data from different sources and exploring connections. She elaborates:

On a more practical note, semantic search is about efficient enterprise content usage. As one of the biggest losses of knowledge happens due to inefficient management and retrieval of information. The ability to search for meaning not for keywords brings us a step closer to efficient information management.

If semantic search had a separate icon from the one traditional search has it would have been a microscope. Why? Because semantic search is looking at content as if through the magnifying lens of a microscope. The technology helps us explore large amounts of systems and the connections between them. Sharpening our ability to join the dots, semantic search enhances the way we look for clues and compare correlations on our knowledge discovery quest.

At the bottom of the post is a slideshow on this “knowledge discovery quest.” Sure, it also serves to illustrate how ontotext could help, but we can’t blame them for drumming up business through their own blog. We actually appreciate the company’s approach to semantic search, and we’d be curious to see how they manage the intricacies of content conversion and normalization. Founded in 2000, ontotext is based in Bulgaria.

Cynthia Murrell, November 25, 2016
Sponsored by ArnoldIT.com, publisher of the CyberOSINT monograph

Word Embedding Captures Semantic Relationships

November 10, 2016

The article on O’Reilly titled Capturing Semantic Meanings Using Deep Learning explores word embedding in natural language processing. NLP systems typically encode word strings, but word embedding offers a more complex approach that emphasizes relationships and similarities between words by treating them as vectors. The article posits,

For example, let’s take the words woman, man, queen, and king. We can get their vector representations and use basic algebraic operations to find semantic similarities. Measuring similarity between vectors is possible using measures such as cosine similarity. So, when we subtract the vector of the word man from the vector of the word woman, then its cosine distance would be close to the distance between the word queen minus the word king (see Figure 1).

The article investigates the various neural network models that prevent the expense of working with large data. Word2Vec, CBOW, and continuous skip-gram are touted as models and the article goes into great technical detail about the entire process. The final result is that the vectors understand the semantic relationship between the words in the example. Why does this approach to NLP matter? A few applications include predicting future business applications, sentiment analysis, and semantic image searches.

Chelsea Kerwin,  November 10, 2016
Sponsored by ArnoldIT.com, publisher of the CyberOSINT monograph

Ontotext: The Fabric of Relationships

November 9, 2016

Relationships among metadata, words, and other “information” are important. Google’s Dr. Alon Halevy, founder of Transformic which Google acquired in 2006, has been beavering away in this field for a number of years. His work on “dataspaces” is important for Google and germane to the “intelligence-oriented” systems which knit together disparate factoids about a person, event, or organization. I recall one of his presentations—specifically the PODs 2006 keynote–in which he reproduced a “colleague’s” diagram of a flow chart which made it easy to see who received the document, who edited the document and what changes were made, and to whom recipients of the document forward the document.

Here’s the diagram from Dr. Halevy’s lecture:

image

Principles of Dataspace Systems, Slide 4 by Dr. Alon Halevy at delivered on June 26, 2006 at PODs. Note that “PODs” is an annual ACM database-centric conference.

I found the Halevy discussion interesting.

Read more

The Semantic Web: Clarified and Mystified

November 4, 2016

Navigate to “Semantic Web Speculations.” After working through the write up, I believe there are some useful insights in the write up.

I highlighted this passage:

Reaching to information has been changed quite dramatically from printed manuscripts to Google age. Being knowledgeable less involves memorizing but more the ability to find an information and ability to connect information in a map-like pattern. However, with semantic tools become more prevalent and a primary mode of reaching information changes, this is open to transform.

I understand that the Google has changed how people locate needed information. Perhaps the information is accurate? Perhaps the information is filtered to present a view shaped by a higher actor’s preferences? I agree that the way in which people “reach” information is going to change.

I also noted this statement:

New way of being knowledgeable in the era of semantic web does not necessarily include having the ability to reach an information.

Does this mean that one can find information but not access the source? Does the statement suggest that one does not have to know a fact because awareness that it is there delivers the knowledge payload?

I also circled this endorsement of link analysis, which has been around for decades:

It will be more common than any time that relations between data points will have more visibility and access. When something is more accessible, it brings meta-abilities to play with them.

The idea that the conversion of unstructured information into structured data is a truism. However, the ability to make sense of the available information remains a work in progress as is the thinking about semantics.

Stephen E Arnold, November 4, 2016

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