The Semantic Web Identity Crisis? More Like Intellectual Cotton Candy?

February 22, 2021

The Semantic Web identity Crisis: In Search of the Trivialities That Never Were” is a 5,700 word essay about confusion. The write up asserts that those engaged in Semantic Web research have an “ill defined sense of identity.” What I liked about the essay is that semantic progress has been made, but moving from 80 percent of the journey over the last 20 percent is going to be difficult. I would add that making the Semantic Web “work” may be impossible.

The write up explains:

In this article, we make the case for a return to our roots of “Web” and “semantics”, from which we as a Semantic Web community—what’s in a name—seem to have drifted in search for other pursuits that, however interesting, perhaps needlessly distract us from the quest we had tasked ourselves with. In covering this journey, we have no choice but to trace those meandering footsteps along the many detours of our community—yet this time around with a promise to come back home in the end.

Does the write up “come back home”?

In order to succeed, we will need to hold ourselves to a new, significantly higher standard. For too many years, we have expected engineers and software developers to take up the remaining 20%, as if they were the ones needing to catch up with us. Our fallacy has been our insistence that the remaining part of the road solely consisted of code to be written. We have been blind to the substantial research challenges we would surely face if we would only take our experiments out of our safe environments into the open Web. Turns out that the engineers and developers have moved on and are creating their own solutions, bypassing many of the lessons we already learned, because we stubbornly refused to acknowledge the amount of research needed to turn our theories into practice. As we were not ready for the Web, more pragmatic people started taking over.

From my point of view, it looks as if the Semantic Web thing is like a flashy yacht with its rudders and bow thrusters stuck in one position. The boat goes in circles. That would drive the passengers and crew bonkers.

Stephen E Arnold, February 22, 2021

Where Did You Say “Put the Semantic Layer”?

February 10, 2021

Eager to add value to their pricey cloud data-warehouses, cloud vendors are making a case for processing analytics right on their platforms. Providers of independent analytics platforms note such an approach falls short for the many companies that have data in multiple places. VentureBeat reports, “Contest for Control Over the Semantic Layer for Analytics Begins in Earnest.” Writer Michael Vizard tells us:

“Naturally, providers of analytics and business intelligence (BI) applications are treating data warehouses as another source from which to pull data. Snowflake, however, is making a case for processing analytics in its data warehouse. For example, in addition to processing data locally within its in-memory server, Alteryx is now allowing end users to process data directly in the Snowflake cloud. At the same time, however, startups that enable end users to process data using a semantic layer that spans multiple clouds are emerging. A case in point is Kyligence, a provider of an analytics platform for Big Data based on open source Apache Kylin software.”

Alteryx itself acknowledges the limitations of data-analysis solutions that reside on one cloudy platform. The write-up reports:

“Alteryx remains committed to a hybrid cloud strategy, chief marketing officer Sharmila Mulligan said. Most organizations will have data that resides both in multiple clouds and on-premises for years to come. The idea that all of an organization’s data will reside in a single data warehouse in the cloud is fanciful, Mulligan said. ‘Data is always going to exist in multiple platforms,’ she said. ‘Most organizations are going to wind up with multiple data warehouses.’”

Kyligence is one firm working to capitalize on that decentralization. Its analytics platform pulls data from multiple platforms in an online analytical processing database. The company has raised nearly $50 million, and is releasing an enterprise edition of Apache Kylin that will run on AWS and Azure. It remains to be seen whether data warehouses can convince companies to process data on their platforms, but the push is clearly part of the current trend—the pursuit of a never-ending flow of data.

Cynthia Murrell, February 10, 2021

SEO Semantics and the Vibrant Vivid Vees

January 29, 2021

Years ago, one of the executives at Vivisimo, which was acquired by IBM, told me about the three Vees. These were the Vees of Vivisimo’s metasearch system. The individual, who shall remain nameless, whispered: Volume, Velocity, and Variety. He smiled enigmatically. In a short time, the three Vees were popping up in the context of machine learning, artificial intelligence, and content discovery.

The three Vivisimo Vees seem to capture the magic and mystery of digital data flows. I am not on that wheezing bus in Havana.

Volume is indeed a characteristic of online information. Even if one has a trickle of Word documents to review each day, the individual reading, editing, and commenting on a report has a sense that there are more Word documents flying around than the handful in this morning’s email. But in the context of our datasphere, no one knows how much digital data exist, what it contains, who has access, etc. Volume is a fundamental characteristic of today’s datasphere. The only way to contain data is to pull the plug. That is not going to happen unless there is something larger than Google. Maybe a massive cyber attack?

The second Vee is variety. From the point of view of the Vivisimo person, variety referred to the content that text centric system processed. Text, unlike a tidy database file, is usually a mess. Without structure, transform and load outfits have been working for decades to convert the messy into the orderly or at least pull out certain chunks so that one can extract key words, dates, and may entities with reasonable accuracy. Today there is a lot of variety; however, for every new variant old ones become irrelevant. At best, the variety challenge is like a person in a raft trying to paddle to keep from being swamped with intentional and unintentional content types. How about those encrypted message? Another hurdle for the indexing outfit: Decryption, metadata extraction and assignment, and processing throughput. So the variety Vee is handled by focusing on a subset of content. Too bad for those who think that “all” information is online.

The third Vee is a fave among the real time crowd. The idea that streams and flows of data in real time can be processed on the fly, patterns identified, advanced analytics applied, and high value data emitted. This notion is a good one when working in print shop in the 17th century. Those workflows don’t make any sense when figuring out the stream of data produced by an unidentified drone which may be weaponized. Furthermore, if a monitoring device notes a several millisecond pattern before a person’s heart attack, that’s not too helpful when the afflicted individual falls over dead a second later. What is “real time”? Answer: There are many types, so the fix is to focus, narrow, winnow, and go for a high probability signal. Sometimes it works; sometimes it doesn’t.

The three Vees are a clever and memorable marketing play. A company can explain how its system manages each of these issues for a particular customer use case. The one size fit all idea is not what generates information processing revenues. Service fees, subscriptions, and customization are the money spinners.

The write up “The Four V’s of Semantic Search” adds another Vee to the Vivisimo three: Veracity. I don’t want to argue “truth” because in the datasphere for every factoid on one side of an argument, even a Bing search can generate counter examples. What’s interesting is that this veracity Vee is presented as part of search engine optimization using semantic techniques. Here’s a segment I circled:

The fourth V is about how accurate the information is that you share, which speaks about your expertise in the given subject and to your honesty. Google cares about whether the information you share is true or not and real or not, because this is what Googles [sic] audience cares about. That’s why you won’t usually get search results that point to the fake news sites.

Got that. Marketing hoo hah, sloganeering, and word candy —  just like the three Vivisimo Vees.

Stephen E Arnold, January 29, 2021

Marketing Insight or Marketing Desperation?

January 6, 2021

A couple of weeks ago, I became aware of a shift in techno babble. Here are some examples and their sources:

Fire-and-forget. Shoot a missile and smart software does the rest… when necessary. Source: War News

Hyperedge replacement graph grammars (HRGs). A baffler. Source: Something called NEURIPS

Performative. I think this means go fast or complete a task in a better way. Source: Mashable

Proceleration. The Age of Earthquakes.

Tangential content. The idea is that information does not have to be related; for example, if you write about car polish for a living, including articles about zebras is a good thing. Source: Next Web

Transition from pets to cattle. Moving from the status of a beloved poodle to a single, soon to be eaten bovine. Source: Amazon AWS

Fascinating terminology. Time for digital detox and maybe red tagging. No, I don’t know what these terms means either. I assume that vendors of smart software which can learn without human fiddling knows these terms and many more because of experience intelligence platforms.

Stephen E Arnold, January 6, 2021

Expert System Has Embraced the AI Revolution

November 19, 2020

It’s official. Expert System S.p. A. (Italy) is now Expert.ai. I know because the firm’s Web site displays this message:

image

Expert System has moved along a business path like one of those Amalfi coast cliff side roads: Breathtaking turns, chilling confrontations with other vehicles, and a lack of guard rails.

image

Repositioning a big rig is a thrill for sure.

The company’s tag line is:

It’s time to make all data actionable.

Yep, “all.” Even video, encrypted messages among employees, and confidential compensation data? Sure, “all.”

Plus, the firm has tweaked its description of its focus to assert:

Expert.ai is the premier artificial intelligence platform for language understanding. Its unique hybrid approach to NL combines symbolic human-like comprehension and machine learning to transform language-intensive processes into practical knowledge, providing the insight required to improve decision making throughout organizations.

Vendors of search and content processing widgets are responding to today’s business environment with marketing. Expert System was founded in 1989 in Modena, Italy.

Premier too.

Stephen E Arnold, November 19, 2020

Fixing Language: No Problem

August 7, 2020

Many years ago I studied with a fellow who was the world’s expert on the morpheme _burger. Yep, hamburger, cheeseburger, dumbburger, nothingburger, and so on. Dr. Lev Sudek (I think that was his last name but after 50 years former teachers blur in my mind like a smidgen of mustard on a stupidburger.) I do recall his lecture on Indo-European languages, the importance of Sanskrit, and the complexity of Lithuanian nouns. (Why Lithuanian? Many, many inflections.) Those languages evolving or de-volving from Sanskrit or ur-Sanskrit differentiated among male, female, singular, neuter, plural, and others. I am thinking 16 for nouns but again I am blurring the Sriacha on the Incredible burger.

This morning, as I wandered past the Memoryburger Restaurant, I spotted “These Are the Most Gender-Biased Languages in the World (Hint: English Has a Problem).” The write up points out that Carnegie Mellon analyzed languages and created a list of biased languages. What are the languages with an implicit problem regarding bias? Here a list of the top 10 gender abusing, sexist pig languages:

  1. Danish
  2. German
  3. Norwegian
  4. Dutch
  5. Romanian
  6. English
  7. Hebrew
  8. Swedish
  9. Mandarin
  10. Persian

English is number 6, and if I understand Fast Company’s headline, English has a problem. Apparently Chinese and Persian do too, but the write up tiptoes around these linguistic land mines. Go with the Covid ridden, socially unstable, and financially stressed English speakers. Yes, ignore the Danes, the Germans, Norwegians, Dutch, and Romanians.

So what’s the fix for the offensive English speakers? The write up dodges this question, narrowing to algorithmic bias. I learned:

The implications are profound: This may partially explain where some early stereotypes about gender and work come from. Children as young as 2 exercise these biases, which cannot be explained by kids’ lived experiences (such as their own parents’ jobs, or seeing, say, many female nurses). The results could also be useful in combating algorithmic bias.

Profound indeed. But the French have a simple, logical, and  “c’est top” solution. The Académie Française. This outfit is the reason why an American draws a sneer when asking where the computer store is in Nimes. The Académie Française does not want anyone trying to speak French to use a disgraced term like computer.

How’s that working out? Hashtag and Franglish are chugging right along. That means that legislating language is not getting much traction. You can read a 290 page dissertation about the dust up. Check out “The Non Sexist Language Debate in French and English.” A real thriller.

The likelihood of enforcing specific language and usage changes on the 10 worst offenders strikes me as slim. Language changes, and I am not sure the morpheme –burger expert understood decades ago how politicallycorrectburgers could fit into an intellectual menu.

Stephen E Arnold, August 7, 2020

Twitch: Semantic Search Stream to Lure Gamers, Trolls, and Gals?

July 31, 2020

Amazon Twitch may be more versatile than providing the young at heart with hours of sophisticated content. There are electronic games, trolls (lots of trolls armed with weird icons), and what appear to be females.

Now Twitch will be moving along the content spectrum with the addition of a stream about semgrep. If you are not on a first name basis, semgrep is a semantic search thing. You can join in for free, no waiting rooms, and no big technical hurdles. I suppose one could create a lecture about semantic methods in TikTok 30-second videos which might be a first for the non-invasive, controversial app. Nah, go for Twitch. Skip YouTube and Facebook. Go Bezos bulldozer.

Navigate to https://twitch.tv and go to the jeanqasaur stream. The time on July 31, 2020? The show begins at 4 pm US Eastern time.

The program is definitely perceived by some as super important. A motivated semantic wizard posted a message on the TweetedTimes.com semantic page. Here’s what the message looks like:

image

DarkCyber’s suggestions:

  • Do not become distracted by Raj recruiting, Bad Bunny, or Celestial Fitness. Keep your eye on the grep as it were.
  • Sign up because Amazon wants you to be part of the family. Prime members may receive extra Bezos bucks somewhere down the line
  • Exercise good grammar, be respectful, and keep your clothes on. Twitch banned SweetSaltyPeach who reinvented herself as RachelKay, Web developer, fashion model, and gamer icon. You may have to reincarnate yourself too.
  • Avoid the lure of Animal Crossing Arabia II.

Stephen E Arnold, July 31, 2020

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.

comic

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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