News Flash: SEO Leads to Buying Ads with or without Semantic Blabber

June 26, 2020

A Search Engine Optimization blog offers some axioms on semantic search for a crowd used to manipulating keywords, backlinks, URL structure, and the like. David Amerland posts, “Five Semantic Search Principles to Help Organize your Content and Marketing.” To us, the result seems like a reconstruction of an Incan incantation with a mystical diagram tossed in for added magic. Amerland writes:

“Semantic search is as open to analysis and interpretation of the elements that govern it as the good ol’ Boolean search of the past was. Yet the effort required to achieve a positive outcome (i.e. higher visibility in search) is now every bit as labor and cost intensive as doing the right thing. Semantic search, in other words, does not automatically make us all behave in a morally better way because it is the right thing to do. It makes us behave morally better because there is no viable alternative.”

So far so good. The piece then gets into search as psychology. We’re told the structure of search has always shaped users’ perceptions of the information presented and, by extension, their behaviors. We cannot argue with that much. Then Amerland continues:

“Semantic search has much in common with Gestalt psychology. It looks at the phenomena it studies as organized and structured wholes rather than the sum of their parts and, like semantic search, it deals with entities and how we perceive them. The question that arises with semantic search, now, is that since there are so many elements that drive it and since many of them are roughly equal so that none has a significant advantage over the other, how can we create a strategy that actually works? This is where Gestalt psychology comes into its own.”

Gestalt psychology as an SEO strategy—interesting. See the article to go further down the rabbit hole, where it discusses, with illustrations, its five principles: the law of proximity, the law of similarity, the law of perceptual organization, the law of symmetry, and the law of closure. We grant that SEO professionals are nothing if not creative, but perhaps there is such a thing as over thinking one’s approach to algorithm manipulation.

Cynthia Murrell, June 11, 2020

Semantic Search: From Whence to What

April 2, 2020

A post from semantic SEO firm InLinks traces “The Evolution of Semantic Search.” The buzzword-filled summary does relate an interesting saga, which prompts us to wonder why enterprise search results are generally still pretty poor.

The write-up traces the evolution from the card-catalogue-like directories of early Yahoo to today’s semantic search. Along the way it details these concepts and milestones: directory-based search vs. text-based search; the crawl and discover phase; JavaScript challenges; turning text into math; the continuous bag of words (COBW) and nGrams; vectors; semantic markup; and trusted seed sets. See the post for elaboration on any of these headings.

The piece concludes:

“We started the journey of search by discussing how human-led web directories like Yahoo Directory and the Open Directory Project was surpassed by full-text search. The move to Semantic search, though, is a blending of the two ideas. At its heart, Google’s Knowledge-based extrapolates ideas from web pages and augments its database. However, the initial data set is trained by using ‘trusted seed sets’. the most visible of these is the Wikipedia foundation. Wikipedia is curated by humans and if something is listed in Wikipedia, it is almost always listed as an entity in Google’s Knowledge Graph. … So in many regards. the Knowledge Graph is the old web Directory going full circle. The original directories used a tree-like structure to give the directory and ontology, whilst the Knowledge Graph is more fluid in its ontology. In addition, the smallest unit of a directory structure was really a web page (or more often a website) whilst the smallest unit of a knowledge graph is an entity which can appear in many pages, but both ideas do in fact stem from humans making the initial decisions.”

Here is where we are reminded of the post’s source—For the SEO platform, the takeaway is that what Google considers an “entity” has become key to effective SEO marketing. For our part, we look forward to the continuation of the saga, hopefully resulting in truly effective enterprise search solutions. Some day.

Cynthia Murrell, April 2, 2020

Semantic Sci-Fi: Search Is Great

March 23, 2020

I read “Keyword Search is DEAD; Semantic Search Is Smart.” I assume the folks at Medium consider each article, weigh its value, and then release only the highest value content.


Semantic search is better than any other type of search in the galaxy.

Let’s assume that the write up is correct and keyword search is dead. Further, we shall ignore the syntax of SQL queries, the dependence of policeware and intelware systems on users’ looking for named entities, and overlook the interaction of people using an automobile’s navigation service by saying, “Home.” These are examples of keyword search, and I decided to give a few examples, skipping how keyword search functions in desktop search, chemical structure systems, medical research, and good old, bandwidth trimming YouTube.

Okay, what’s the write up say beyond “keyword search is dead.”

Here are some points I extracted as I worked my way through the write up. I required more than three minutes (the Medium estimate) because my blood pressure was spiking, and I was hyper ventilating.

Factoid 1 from the write up :

If you do semantic search, you can get all information as per your intent.

What’s with this “all.” Content domains, no matter what the clueless believe, are incomplete. There is no “all” when it comes online information which is indexed.

Factoid 2 from the write up:

semantic search seeks to understand natural language the way a human would.

Yep, natural language queries are possible within certain types of content domains. However, the systems I have worked with and have an opportunity to use in controlled situations exhibit a number of persistent problems. These range from computational constraints. One system could support four simultaneous users on a corpus of fewer than 100,000 text documents. Others simply output “good enough” results. Not surprisingly when a physician needs an antitoxin to save a child’s life, keywords work better than “good enough” in my experience. NLP has been getting better, but the idea that systems can integrate widely different data which may be incomplete, incorrect, or stale and return a useful output is a big hurdle. So far no one has gotten over it on a consistent, affordable basis. Short cuts to reduce index look ups can be packaged as semantics and NLP but mostly these are clever ways to improve “efficiency.” Understanding sometimes. Precision and recall? Not yet.

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Semantic Search Allegedly Adds A Boost To Product Discovery

March 20, 2020

Semantic search is one of the old reliable pieces of jargon for improving a search application, but it appears to be old hat. Semantic search, however, can, when correctly implemented, add a much needed boost for product discovery.

Grid Dynamics explains semantic magic in the article, “Boosting Product Discovery With Semantic Search.” We all know that human language is a complicated beast, which is why it has taken decades to develop decent voce to text and automated foreign language translation algorithms.

Humans learn from infancy to process speech based on the context and life experience. As technology has progressed, search engines are expected to perform the same actions which is where semantic search enters the game. Semantic search not only matches key words and phrases, but it brings meaning to them. Ecommerce Web sites require more than keyword and phrase search. Customers want to sort products based on price, brands, ratings, etc.

I am a librarian, and I know that irrelevant results often appear in any search and there are two types of these results: Obviously irrelevant values and values with subtle differences. A simple solution does not exist to fix all the irrelevant results.

Solutions are usually built a hybrid of semantic search and unstructured data. For the semantic search part, they must have: single words must be part of unbreakable multi-word phrases, business domain knowledge retracts/enhances query options, ambiguous matching need to be fixed with saliency to match attributes. Boolean queries also can be implemented in new ways to alter searches. Semantic search can also be used with different physical properties and merchandising rules.

Semantic search is a powerful tool for ecommerce Web sites, but:

“However, the power of semantic search largely depends on the richness and quality of the domain data – product attribution as well as synonyms. If your customers often perform out-of-dictionary search, then semantic search quality will suffer. It can include

• searches by subjective features like occasion of clothing (church dress) or age group for hi-tech device (laptops for kids)

• searches for brands which aren’t carried by your site, but it has similar products which can be suggested instead of just dropping the brand value from a query”

Never doubt how semantic search can improve a ecommerce search engine, but be sure to instill proper parameters for it to work correctly. Semantic search will remain a favorite of marketing whether a system is helping the person looking for information or hindering relevancy.

Whitney Grace, March 20, 2020

Curious about Semantic Search the SEO Way?

November 12, 2019

DarkCyber is frequently curious about search: Semantic, enterprise, meta, multi-lingual, Boolean, and the laundry list of buzzwords marshaled to allow a person to find an answer.

If you want to get a Zithromax Z-PAK of semantic search talk, navigate to ‘Semantic Search Guide.” One has to look closely at the url to discern that this “objective” write up is about search engine optimization or SEO. DarkCyber affectionately describes SEO as the “relevance” killer, but that’s just our old-fashioned self refusing to adapt to the whizzy new world.

The link will point to a page with a number of links. These include:

  • Target audience and contributions
  • The knowledge graph explained
  • The evolution of search
  • Using Google’s entity search tool
  • Getting a Wikipedia listing

DarkCyber took a look at the “Evolution of Search” segment. We found it quirky but interesting. For example, we noted this passage:

Now we turn to the heart of full-text search. SEOs tend to dwell on the indexing part of search or the retrieval part of the search, called the Search Engine Results Pages (SERPs, for short). I believe they do this because they can see these parts of the search. They can tell if their pages have been crawled, or if they appear. What they tend to ignore is the black box in the middle. The part where a search engine takes all those gazillion words and puts them in an index in a way that allows for instant retrieval. At the same time, they are able to blend text results with videos, images and other types of data in a process known as “Universal Search”. This is the heart of the matter and whilst this book will not attempt to cover all of this complex subject, we will go into a number of the algorithms that search engines use. I hope these explanations of sometimes complex, but mostly iterative algorithms appeal to the marketer inside you and do not challenge your maths skills too much. If you would like to take these ideas in in video form, I highly recommend a video by Peter Norvig from Google in 2011:

Oh, well. This is one way to look at universal search. But Google has silos of indexes. The system after 20 plus years does not federate results across indexes. Semantic search? Yeah, right. Search each index, scan results, cut and paste, and then try to figure out the dates and times. Semantic search does not do time particularly well.

Important. Not to the SEO. Search babble may be more compelling.

If this approach is your cup of tea, inLinks has the hot water you need to understand why finding information is not what it seems.

Stephen E Arnold, November 12, 2019

Knowledge Graphs: Getting Hot

July 4, 2019

Artificial intelligence, semantics, and machine learning may lose their pride of place in the techno-jargon whiz bang marketing world. I read “A Common Sense View of Knowledge Graphs,” and noted this graph:


This is a good, old fashioned, Gene Garfield (remember him, gentle reader) citation analysis. The idea is that one can “see” how frequently an author or, in this case, a concept has been cited in the “literature.” Now publishers are dropping like flies and are publishing bunk. Nevertheless, one can see that using the phrase knowledge graph is getting popular within the sample of “literature” parsed for this graph. (No, I don’t recommend trying to perform citation analysis in Bing, Facebook, or Google. The reasons will just depress me and you, gentle reader.)

The section of the write I found useful and worthy of my “research” file is the collection of references to documents defining “knowledge graph.” This is useful, helpful research.

The write up also includes a diagram which may be one of the first representations of a graph centric triple. I thought this was something cooked up by Drs. Bray, Guha, and others in the tsunami of semantic excitement.

One final point: The list of endnotes is also useful. In short, good write up. The downside is that if the article gets wider distribution, a feeding frenzy among money desperate consultants, advisers, and analysts will be ignited like a Fourth of July fountain of flame.

Stephen E Arnold, July 4, 2019

Google: SEO Like a True Google Human Actor

April 18, 2019

We know Google’s search algorithm comprehends text, at least enough to produce relevant search results (though, alas, apparently not enough to detect improper comments in kiddie videos on YouTube). The mechanisms, though, remain murky. Yoast ponders, “How Does Google Understand Text?” Writer Jesse van de Hulsbeek observes Google keeps the particulars close to the vest, but points to some clues, like patents Google has filed. “Word embeddings,” or assessing closely related words, and related entities are two examples. Writing for his SEO audience, van de Hulsbeek advises:

If Google understands context in some way or another, it’s likely to assess and judge context as well. The better your copy matches Google’s notion of the context, the better its chances. So thin copy with limited scope is going to be at a disadvantage. You’ll need to cover your topics exhaustively. And on a larger scale, covering related concepts and presenting a full body of work on your site will reinforce your authority on the topic you specialize in.

We also noted:

Easier texts which clearly reflect relationships between concepts don’t just benefit your readers, they help Google as well. Difficult, inconsistent and poorly structured writing is more difficult to understand for both humans and machines. You can help the search engine understand your texts by focusing on: Good readability (that is to say, making your text as easy-to-read as possible without compromising your message)…Good structure (that is to say, adding clear subheadings and transitions)…Good context (that is to say, adding clear explanations that show how what you’re saying relates to what is already known about a topic).

The article does point out that including key phrases is still important. Google is trying to be more like a human reader, we’re reminded, so text that is good for the humans is good for the SEO ranking. Relevance? Not so much.

Cynthia Murrell, April 18, 2019

Elsevier: Raising Prices Easier Than Implementing Security?

March 19, 2019

Elsevier is a professional publishing company. The firm has a reputation for raising prices for its peer reviewed journals and online services. The challenge is that many subscribers are libraries and libraries are not rolling in cash. Raising prices is easy. One calls a meeting, examines models of money in vs subscribers out, and emails the price hike. No problem.

Security, however, works a bit differently. Elsevier may have learned this is the information in “Education and Science Giant Elsevier Left Users’ Passwords Exposed Online” is accurate. The write up asserts:

Elsevier, the company behind scientific journals such as The Lancet, left a server open to the public internet, exposing user email addresses and passwords. The impacted users include people from universities and educational institutions from across the world.

The article reports that Elsevier fixed the problem. The password security issue, not the burden on libraries.

Stephen E Arnold, March 19, 2019

Elastic Teams With Startup for Semantic Search

August 10, 2018

We’ve learned that a Search company we’ve been following with some interest, Elastic, is pairing with a Palo Alto-based startup to develop and integrate semantic search tools. Computer Weekly shares some details in, “Elastic Puts ‘Semantic Code Search’ Into Stack With” Writer Adrian Bridgwater tells us:

“Known for its Elasticsearch and Elastic Stack products, Elastic insists that’s technology is ‘highly complementary’ to other Elastic use cases and solutions—indeed, is built on the Elastic Stack. provides an interface to search and navigate the source code that is said to ‘go beyond’ simple free text search. Current programming language support includes C/C++, Java, Scala, Ruby, Python, and PHP. This ‘beyond text search’ function gives developers the ability to search for code pertaining to specific application functionality and dependencies. Essentially it provides IDE-like code intelligence features such as cross-reference, class hierarchy and semantic understanding. The impact of such functionality should stretch beyond exploratory question-and-answer utility, for example, enabling more efficient onboarding for new team members and reducing duplication of work for existing teams as they scale.”

According to Elastic’s CEO, integration of the technology will be familiar to anyone who observed how they did it with past acquisitions, like Opbeat and Prelert. We’re also assured that all of’s workers are being welcomed into Elastic’s development fold. Bridgwater notes that, with the startup’s Beiging-based engineering team, Elastic now has its first “formal” dev team located in China. Founded in 2012, Elastic is now based in Mountain View, California.

Cynthia Murrell, August 10, 2018

Mondeca: Another Semantic Search Option

April 9, 2018

Mondeca, based in France, has long been focused on indexing and taxonomy. Now they offer a search platform named, simply enough, Semantic Search. Here’s their description:

“Semantic search systems consider various points including context of search, location, intent, variation of words, synonyms, generalized and specialized queries, concept matching and natural language queries to provide relevant search results. Augment your SolR or ElasticSearch capabilities; understand the intent, contextualize search results; search using business terms instead of keywords.”

A few details from the product page caught my eye. Let’s begin with the Search functionality; the page succinctly describes:

“Navigational search – quickly locate specific content or resource. Informational search – learn more about a specific subject. Compound term processing, concept search, fuzzy search, simple but smart search, controlled terms, full text or metadata, relevancy scoring. Takes care of language, spelling, accents, case. Boolean expressions, auto complete, suggestions. Disambiguated queries, suggests alternatives to the original query. Relevance feedback: modify the original query with additional terms. Contextualize by user profile, location, search activity and more.”

The software includes a GUI for visualizing the semantic data, and features word-processing tools like auto complete and a thesaurus. Results are annotated, with key terms highlighted, and filters provide significant refinement, complete with suggestions. Results can also be clustered by either statistics or semantic tags. A personalized dashboard and several options for sharing and publishing round out my list. See the product page for more details.

Established in 1999, Mondeca delivers pragmatic semantic solutions to clients in Europe and North America, and is proud to have developed their own, successful semantic methodology. The firm is based in Paris. Perhaps the next time our beloved leader, Stephen E Arnold, visits Paris, the company will make time to speak with him. Previous attempts to set up a meeting were for naught. Ah, France.

Cynthia Murrell, April 9, 2018

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