Bitext and MarkLogic Join in a Strategic Partnership

June 13, 2017

Strategic partnerships are one of the best ways for companies to grow and diamond in the rough company Bitext has formed a brilliant one. According to a recent press release, “Bitext Announces Technology Partnership With MarkLogic, Bringing Leading-Edge Text Analysis To The Database Industry.” Bitext has enjoyed a number of key license deals. The company’s ability to process multi-lingual content with its deep linguistics analysis platform reduces costs and increases the speed with which machine learning systems can deliver more accurate results.

bitext logo

Both Bitext and MarkLogic are helping enterprise companies drive better outcomes and create better customer experiences. By combining their respectful technologies, the pair hopes to reduce data’s text ambiguity and produce high quality data assets for semantic search, chatbots, and machine learning systems. Bitext’s CEO and founder said:

““With Bitext’s breakthrough technology built-in, MarkLogic 9 can index and search massive volumes of multi-language data accurately and efficiently while maintaining the highest level of data availability and security. Our leading-edge text analysis technology helps MarkLogic 9 customers to reveal business-critical relationships between data,” said Dr. Antonio Valderrabanos.

Bitext is capable of conquering the most difficult language problems and creating solutions for consumer engagement, training, and sentiment analysis. Bitext’s flagship product is its Deep Linguistics Analysis Platform and Kantar, GFK, Intel, and Accenture favor it. MarkLogic used to be one of Bitext’s clients, but now they are partners and are bound to invent even more breakthrough technology. Bitext takes another step to cement its role as the operating system for machine intelligence.

Whitney Grace, June 13, 2017

Quote to Note: Hate That Semantic Web Stuff

June 8, 2017

I read “JSON-LD and Why I Hate the Semantic Web. “

Here’s the quote I noted:

I hate the narrative of the Semantic Web because the focus has been on the wrong set of things for a long time. That community, who I have been consciously distancing myself from for a few years now, is schizophrenic in its direction. Precious time is spent in groups discussing how we can query all this Big Data that is sure to be published via RDF instead of figuring out a way of making it easy to publish that data on the Web by leveraging common practices in use today. Too much time is spent assuming a future that’s not going to unfold in the way that we expect it to. That’s not to say that TURTLE, SPARQL, and Quad stores don’t have their place, but I always struggle to point to a typical startup that has decided to base their product line on that technology (versus ones that choose MongoDB and JSON on a regular basis).

There you go.

Stephen E Arnold, June 8, 2017

Deep Diving into HTML Employing Semantics

May 31, 2017

HTML, the programming language on which websites are based can employ semantics to make search easier and understanding, especially for those who use assistive technologies.

Web Dev Studios in an in-depth article titled Accessibility of Semantics: How Writing Semantic HTML Can Help Accessibility says:

Writing HTML is about more than simply “having stuff appear on the page.” Each element you use has a meaning and conveys information to your visitors, especially to those that use assistive technologies to help interpret that meaning for them.

Assistive technologies are used by people who have limited vision or other forms of impairment that prohibits them from accessing the web efficiently. If semantics is employed, according to the author of the article, impaired people too can access all features of the web like others.

The author goes on to explain things like how different tags in HTML can be used effectively to help people with visual impairments.

The Web and related technologies are evolving, and it can be termed as truly inclusive only when people with all types of handicaps are able to use it with equal ease.

Vishal Ingole, May 31, 2017

Semantic Platform Aggregates Scientific Information

May 1, 2017

A new scientific repository is now available from a prominent publisher, we learn from “GraphDB, Leading Semantic Database from Ontotext, Powers Springer Nature’s New Linked Open Data Platform” at PRWeb. (We note the word “leading” in the title; who verifies this assertion? Just curious.) The platform, dubbed SciGraph, aggregates data from Springer Nature and its academic partners. The press release specifies:

Thanks to semantic technologies, Linked Open Data and the GraphDB semantic database, all these data are connected in a way which semantically describes and visualizes how the information is interlinked. GraphDB’s capability to seamlessly integrate disparate data silos allows Springer Nature SciGraph to comprise metadata from journals and articles, books and chapters, organizations, institutions, funders, research grants, patents, clinical trials, substances, conference series, events, citations and reference networks, Altmetrics, and links to research datasets.

The dataset is released under a certain international creative commons license, and can be downloaded (by someone with the appropriate technical knowledge) here.

An early explorer of semantic technology, Ontotext was founded in 2000. Based in Bulgaria, the company keeps their North American office in New Jersey. Ontotext’s client roster includes big names in publishing, government agencies, and cultural institutions.

Cynthia Murrell, May 1, 2017

Keyword Search vs. Semantic Search for Patent Seekers

April 26, 2017

The article on BIP Counsels titled An Introduction to Patent Search, Keyword Search, and Semantic Searches offers a brief overview of the differences between keyword, and semantic search. The article is geared towards inventors and technologists in the early stages of filing a patent application. The article states,

If an inventor proceeds with the patent filing process without performing an exhaustive prior art search, it may hamper the patent application at a later point, such as in the prosecution process. Hence, a thorough search involving all possible relevant techniques is always advisable… Search tools such as ‘semantic search assistant’ help the user find similar patent families based on freely entered text.  The search method is ideal for concept based search.

Ultimately the article fails to go beyond the superficial when it comes to keyword and semantic search. One almost suspects that the author (BananaIP patent attorneys) wants to send potential DIY-patent researchers running into their office for help. Yes, terminology plays a key role in keyword searches. Yes, semantic search can help narrow the focus and relevancy of the results. If you want more information than that, you may want to visit the patent attorney. But probably not the one that wrote this article.

Chelsea Kerwin, April 26, 2017

Mondeca: Tweaking Its Market Position

February 22, 2017

One of the Beyond Search goslings noticed a repositioning of the taxonomy capabilities of Mondeca. Instead of pitching indexing, the company has embraced ElasticSearch (based on Lucene) and Solr. The idea is that if an organization is using either of these systems for search and retrieval, Mondeca can provide “augmented” indexing. The idea is that keywords are not enough. Mondeca can index the content using concepts.

Of course, the approach is semantic, permits exploration, and enables content discovery. Mondeca’s Web site describes search as “find” and explains:

Initial results are refined, annotated and easy to explore. Sorted by relevancy, important terms are highlighted: easy to decide which one are relevant. Sophisticated facet based filters. Refining results set: more like this, this one, statistical and semantic methods, more like these: graph based activation ranking. Suggestions to help refine results set: new queries based on inferred or combined tags. Related searches and queries.

This is a similar marketing move to the one that Intrafind, a German search vendor, implemented several years ago. Mondeca continues to offer its taxonomy management system. Human subject matter experts do have a role in the world of indexing. Like other taxonomy systems and services vendors, the hook is that content indexed with concepts is smart. I love it when indexing makes content intelligent.

The buzzword is used by outfits ranging from MarkLogic’s merry band of XML and XQuery professionals to the library-centric outfits like Smartlogic. Isn’t smart logic better than logic?

Stephen E Arnold, February 22, 2017

The Pros and Cons of Human Developed Rules for Indexing Metadata

February 15, 2017

The article on Smartlogic titled The Future Is Happening Now puts forth the Semaphore platform as the technology filling the gap between NLP and AI when it comes to conversation. The article posits that in spite of the great strides in AI in the past 20 years, human speech is one area where AI still falls short. The article explains,

The reason for this, according to the article, is that “words often have meaning based on context and the appearance of the letters and words.” It’s not enough to be able to identify a concept represented by a bunch of letters strung together. There are many rules that need to be put in place that affect the meaning of the word; from its placement in a sentence, to grammar and to the words around – all of these things are important.

Advocating human developed rules for indexing is certainly interesting, and the author compares this logic to the process of raising her children to be multi-lingual. Semaphore is a model-driven, rules-based platform that allows us to auto-generate usage rules in order to expand the guidelines for a machine as it learns. The issue here is cost. Indexing large amounts of data is extremely cost-prohibitive, and that it before the maintenance of the rules even becomes part of the equation. In sum, this is a very old school approach to AI that may make many people uncomfortable.

Chelsea Kerwin, February 15, 2017

Semantics: Biting the Semantic Apple in the Garden of Search Subsystems

February 8, 2017

I love the Phoenix like behavior of search and content processing subsystems. Consider semantics or figuring out what something is about and assigning an index term to that aboutness. Semantics is not new, and it is not an end in itself. Semantic functions are one of the many Lego blocks which make up a functioning and hopefully semi accurate content processing and information accessing system.

I read “With Better Scaling, Semantic Technology Knocks on Enterprise’s Door.” The headline encapsulates decades of frustration for the champions of semantic solutions. The early bird vendors fouled the nest for later arrivals. As a result, nifty semantic technology makes a sales call and finds that those who bother to attend the presentation are [a] skeptical, [b] indifferent, [c] clueless, [d] unwilling to spend money for another career killer. Pick your answer.

For decades, yes, decades, enterprise search and content processing vendors have said whatever was necessary to close a deal. The operative concept was that the vendor could whip up a solution and everything would come up roses. Well, fly that idea by those who licensed Convera for video search, Fast Search for an intelligent system, or any of the other train wrecks that lie along the information railroad tracks.

This write up happily ignores the past and bets that “better” technology will make semantic functions accurate, easy, low cost, and just plain wonderful. Yep, the Garden of Semantics exists as long as the licensee has the knowledge, money, time, and personnel to deliver the farm fresh produce.

I noted this passage:

… semantics standards came out 15 or more years ago, but scalability has been an inhibitor. Now, the graph technology has taken off. Most of what people have been looking at it for is [online transactional processing]. Our focus has been on [online analytical processing] — using graph technology for analytics. What held graph technology back from doing analytics was the scaling problem. There was promise and hype over those years, but, at every turn, the scale just wasn’t there. You could see amazing things in miniature, but enterprises couldn’t see them at scale. In effect, we have taken our query technology and applied MPP technology to it. Now, we are seeing tremendous scales of data.

Yep, how much does it cost to shove Big Data through a computationally intensive semantic system? Ask a company licensing one of the industrial strength systems like Gotham or NetReveal.

Make sure you have a checkbook with a SPARQL enhanced cover and a matching pen with which to write checks appropriate to semantic processing of large flows of content. Some outfits can do this work and do it well. In my experience, most outfits cannot afford to tackle the job.

That’s why semantic chatter is interesting but often disappointing to those who chomp the semantic apple from the hot house of great search stuff. Don’t forget to gobble some cognitive chilies too.

Stephen E Arnold, February 8, 2017

More Semantic Search Cheerleading: My Ears Hurt

February 8, 2017

I read “Semantic Search. The Present and Future of Search Engine Optimization .” Let’s be clear. The point of this write up has zero to do with precision and recall. The goal strikes me as generating traffic. Period. Wrapping the blunt truth in semantic tinsel does not change the fact that providing on point information is not on the radar.

I noted this statement and circled it in wild and crazy pink:

SEO in the current times involves user intent to provide apt results which can help you to improve your online presence. Improvement is possible by emphasizing on various key psychological principles to attract readers; rank well and eventually expand business.

When I look for information, my intent is pretty clear to me. I have learned over the last 50 years that software is not able to assist me. May I give you an example from yesterday, gentle reader. I wanted information about Autonomy Kenjin, which became available in the late 1990s. It disappeared. Online was useless and the search systems I used either pointed me to board games, rock music, or Japanese culture. My intent is pretty clear to me. Intent to today’s search systems suck when it comes to my queries.

The write up points out that semantics will help out with “customer personality guiding SEO.” Maybe for Lady Gaga queries. For specialized, highly variable search histories, not a chance. Systems struggle to recognize the intent of highly idiosyncratic queries. Systems do best with big statistical globs. College students like pizza. This user belongs to a cluster of users labeled college students. Therefore, anyone in this cluster gets… pizza ads. Great stuff. Double cheese with two slices of baloney. Then there are keywords. Create a cluster, related terms to it. Bingo. Job done. Close enough for today’s good enough approach to indexing.

The real gems of the write up consist of admonitions to write about a relevant topic. Relevant to whom, gentle reader. The author, the reader, the advertiser? Include concepts. No problem. A concept to you might be a lousy word to describe something to me; for example, games and kenjin. And, of course, use keywords. Right, double talk and babble.

Semantic SEO. Great stuff. Cancel that baloney pizza order. I don’t feel well.

Stephen E Arnold, February 8, 2017

Learning the Aboutness of a Web Site or Other Other Online Text Object

February 7, 2017

Quite by accident the Beyond Search goslings came across a company offering a free semantic profile of online text objects. The idea is to plug in a url like The Leiki system will generate a snapshot of the concepts and topics the content object manifests. We ran the Beyond Search blog through the system. Here’s what we learned:

The system identified that the blog covers Beyond Search. We learned that our coverage of IBM is more intense than our coverage of the Google. But if one combines the Leiki category “Google Search” with the category “Google,” our love of the GOOG is manifest. We ran several other blogs through the Leiki system and learned about some content fixations that were not previously known to us.


We suggest you give the system a whirl.

The developer of the system provides a range of indexing, consulting, and semantic services. More information about the firm is at

Stephen E Arnold, February 7, 29017

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