So now I want to draw this to your attention, the underlying story. I was recently invited to join the Future Shapers as a contributor and I was delighted to be accepted as a future shapers contributor. this is my profile link.

There are some strong reasons to add my voice to this group so I wanted to share this with you here on my main posting site

Normally I would not try to merge my posting site with others unless I have some growing level of involvement, contribution or strong identification with. Well, this is one of those but I first wanted to wait before I publicize it here, as the official launch of a funding project kicked off late last week in Madrid,(link) that radically gives it a really different meaning, one to draw to your attention as it is radically different.

So let me explain why, so I have provided the outlines of the story below in their words

Not only do I identify with its position but when you begin to explore the other contributors you can see the value for all innovators. Let one of the founders tell the story.

All I am doing it is being a conduit to this story as I believe it does have a real future value and quite frankly, a real market needs to cut through so much “clutter” that does not add a body of knowledge to advance our innovation understanding.  It often bewilders us and actually stops us actually progressing with conflicting messages, half stories or incomplete understandings. We need different ways to ‘raise the bar.’ Much of this poor thinking on innovation holds back building a real innovation movement, one that becomes the core of any new value-adding activity within our Enterprises.

So the thinking, rational and emerging business proposition

A recent thought piece by @rcopland73  one of the founders entitled “From the Rock Market to the Stock Market” is an introduction to how  the future shapers platform is set to raise their game to the next level around innovation thought pieces, returning the value back from a growing stock of fresh innovation knowledge contributors to provide others committed to understand and make innovation work, a better return of their invested time.

Richards article sets off a raw nerve for me. Does it for you? In his words:

“In the past, I (Richard) have written about our founding principle being ‘Next Generation Organisations deserve Next Generation guidance’ and our community of Future Shapers sole aim of helping businesses and other organizations to shape and create innovation-led futures. We also believe in practicing what we preach and are boldly re-imagining the digital provision of thought leadership and essentially digital news journalism around innovation; how it is produced and delivered, how it is funded and most importantly, how it can serve macro, global and diverse communities, not advertisers.

A value intent gained from offering knowledge

“The growing, unmet demand for high-quality, trustworthy and value-adding thought leadership in innovation has manifested in a significant business opportunity. Having already provided 2-years of innovation thought leadership that has reached tens of thousands of readers we have a fantastic plan for revenue growth and will be one of the first media companies in the Innovation Thought Leadership domain to seize this rare moment to reinvent an industry.

Fewer stories of importance are told or heard. Less and less independent cutting-edge thought leadership is created. The Future Shapers and our innovative platform intend to change this”.

Growth will be driven by millennials and senior executive technologists, our core audience.

“We have identified a target audience of early adopters — influential and engaged executives and agents of change who are deeply passionate about innovation and are increasingly able and willing to pay for quality trustworthy thought leadership.

The genesis of what we are going to do is putting content on a blockchain, and having the connectivity for peer-to-peer transactions – via a digital currency such as Bitcoin or a smart contract such as Ethereum – allows complete transparency and automation of execution, as well as direct payments to copyright holders”

Become a Founding Member of The Future Shapers

“We are now looking to take our platform to the next level. Our goal; to build the largest innovation thought leadership reader base and be considered as the go-to-place for innovation insight. As such, we’re extending our digital platform to enable the re-correlation of knowledge to value. We plan to develop an Ethereum smart contract to enable and support our pay as you read model. We passionately believe that single article sales are the future of online media and we are asking for your help and contributions to assist us in bringing it to life.

We’re asking you, our technologists and agents of change, to fund this new model of innovation thought leadership. Not to donate so a few more blogs can be written about a hot topic or two, but rather to invest in a long-term solution to the provision of well researched, consumable, value-adding thought leadership.

Just as we believe that great thought leadership is now needed like never before, we believe that media that exists to serve our community of readers via a value-adding product should be worth paying for. So, help us build the next generation of media platforms by becoming a Founding Member of The Future Shapers”.

Future platforms for building next-generation advice

“We believe next-generation organizations need next-generation advice supported by a next-generation platform. We are focused on the goal of returning value back to knowledge and as part of our plans, we will be crowdfunding with Crowdcube over the coming weeks.

We passionately believe we have a compelling solution that will disrupt digital media and we are asking for your help and contributions to assist us in bringing it to life. If you are interested and would like to know more, please contact us at info@thefutureshapers.com“

From my perspective, I will certainly plan to write further for them and clearly, this knowledge, value, innovation and the future, is just one of my thought pieces bubbling away in the back of my mind to add further support to this initiative.

I certainly hope this future shaper does take off and delivers as the founders want. It is one that holds a promise that will build innovation knowledge sharing in a new different way, something I strongly identify with. Get in touch with them and find out more on how you can assist them in their objectives.

The post Return Value Back to Knowledge first appeared on Building Your Innovation & Ecosystem Intelligence.

Knowledge Graphs have a real potential to become highly valuable, topical and relevant. If only we can get them prised out of the engineer, data scientists, or software experts hands.

We simply should so we can get this concept fully out into the real world, that of applying as solutions to real client problems, it would really help. I get tired of hearing about “use cases”, where concepts like KG often get caught up in, that never-ending validation.

Is this validation simply because it does not work, it is too much hard work delivering the promise within the concept? Or the approach has too much complexity around it and needs massive resources to undertake?

KG needs a real resource momentum and a determination to break through uncertainty. Its huge value should drive it, and caution should be modified and lets go out and validate it, in the real world.

If any of these “constraints” are the case, then we do need to “hack this” differently, as Knowledge Graphs has what I see an incredible potential, as an application solution that should be deemed as far too important to keep under wraps. We need to instill a sense of urgency into this. Why, well read on.

Here I want to give a deeper explanation of knowledge graphs- a ‘potted’ history and future view. So, my attempt here is to give Knowledge Graphs a more ”layman” context so we can begin to see how this “Knowledge Graph” concept can become really important to recognize, support and apply in our lives.

It will equally become a significant industrial application as well, as increasingly be part of our everyday working life in related searches as it is today by our personal use of Google or other search engines that have Knowledge Graphs increasingly sitting behind them. It might become the next big “buzz”, I recently heard being as topical as “Artifical Intelligence” as a prediction for use in an industrial application. As big a buzz- wow.

It is once we make all the connects in the knowledge that is out there, waiting to form new relationships, we unleash our connected understanding in completely new ways.

Knowledge Graphs help us to form essential context and that has been often missing or simply the poor cousin to content. Context is king, not content!

Struggling to get out of the technical jargon, so be ready and have patience.

Most of what I have read and researched gives detailed explanations of how they work and throws around all the different jargon, protocols and such, as the need to understand the important semantic technologies, concepts, and methods like RDF, URI, SPARQL, SKOS, and OWL. That is fine but we need to turn Knowledge Graphs into the compelling story I think it is but it always have to be provided in layman’s terms, otherwise, it becomes a great idea that struggles to get its importance across, to those that are the eventual decision makers, or those that have to make it work.

As we all know, the biggest buzz on the block today is “Artificial Intelligence”, well it is within this Knowledge Graphs we have a large part of its foundation. AI is getting to a point where it will drive the next wave of technology disruption. Building AI application requires Context. Knowledge Graphs provide the context.

If we don’t have increasing context we will never realize our existing human or artificial intelligence as it constantly fails to connect-up all the parts, in any search or relating knowledge. We see the Web as increasingly the point of knowledge or referral point. That works to a point but all our internal organization data is not on the web, it still sits in silos.

Google, a leader in AI introduced in 2012 “Knowledge Graphs”. As I have heard it explained they moved from links or strings to turn them more into “things”. This now allows us to have far more multiple options and it is the graph database behind this that is providing the structure, relationships and detailed information about the topic you are wanting to find out more. It is providing more options and choices of where to go in your next step, partly as it has been set up to connect to search more on “things” or concepts.

I wanted to put in here Googles view of a layman’s guide, which I can only dare you to explore. It left me feeling totally inadequate as a real layman. Go on, beat yourself up on this but come back here as this is a real layman’s guide.

Knowledge Graphs capture relationships between people, processes, applications, data, things and their relationships. The more the connections are connected the more context you can be provided to make more informed choices. Knowledge Graphs feed off more related data to give increasing contextual information.

Machine learning works at understanding dependencies so emerging from any learning you get a growing sense of intent, objective and those important aspects that you are wanting to find out more upon, to build out even further. The move from just “stringing data together” to build a picture of ‘things’ you get greater contextual knowledge.

Today we have always struggled with where our knowledge lies

It often stays ‘resident’ with the worker, scientist or locked into more traditional enterprise applications, there for one job but not recognized as connected to other jobs you might want to do. The querying of data still has limitations without significant rewriting of new code to go and undertake a search. We need to synthesize, amalgamate, classify, coordinate, organize data, information, and knowledge in multiple “different” ways to look deeper into alternatives and new choices.  Our decisions are becoming more reliant on complexity and we need to organize our data differently to manage this. Knowledge Graphs provide part of the solution.

Within my researching around Knowledge Graphs, I came across the approach by Thomson Reuters and their Knowledge Graph feed is where they are applying key identifiers to make the connections you might miss by not having a structured Metadata that builds a richer content. I think this visual (shown below) of theirs helps begin to frame where Knowledge Graphs can go for building related documents to extract, classify and tag metadata. Metadata is data about data. It describes the underlying characteristics of the data by keywords describing the content and structural information. Metadata is critical to building Knowledge Graphs.

Go to Thompson Reuters intelligent tagging page where they outline how turns large amounts of unstructured data can become into a precise advantage, to tag the people, places, facts, and events in your content to increase its value, accessibility, and interoperability.

We are in need of building semantic relationships constantly for today’s connected world.

For example, multiple documents have within them that critical semantic relationship between the keywords, phrases, and concepts. As we can group these the relationships can potentially expose new information about their interrelationship, you might not have seen or been aware of, without making that critical connecting up. We can form deeper meaning or even gain new meaning, besides gaining a more comprehensive meaning, from forming this relationship.

The semantic knowledge graphs become the backbone of a new architecture that can enable entity-centric (things) to offer views on information, data, products, suppliers, employees, locations and research topics. Knowledge can be retrieved between these various connected objects and they convert previously unorganized data into useful informing information to determine a new action, or insight, or awareness.

The world-wide-web needs different machine learning methods and the application of new algorithms is helping knowledge graphing to catch up. We see Microsoft, Google, Amazon, along with a host of other “search” providers are all working on bringing semantic knowledge graphs to us. Knowledge Graphing has been transforming our searches on the web, now it needs to transform our daily lives, inside organizations and for us personally. Go check out Googles Knowledge Graphs.

Late last year (November 2017)  Amazon announced its Neptune a fully-managed graph database service with the objective of building and running applications that work with highly connected data sets. This database engine is optimizing and storing billions of relationships and querying the graph with millisecond latency. It is offered in the cloud, very scalable and works for open graph API’s.

My interest spiked last December with a visit to Siemens R&D center

I wrote a post “As we enter 2018 we will need Knowledge Graphs” in December 2017, from that brief visit to Siemens where I saw an early conceptual, prototype, Knowledge Graph demonstration. It turned a light bulb on for me and I saw the future of “connected innovation”. Everything we all do is based on bringing data, information and turning this into knowledge. It is connecting this all up we have the greater potential for seeing new knowledge possibilities. We begin to question current or existing knowledge as we gain greater connections and new innovation is often born from this.

Grasping the industrial application of Knowledge Graphs

The industrial application of Knowledge Graphs will build the significant “domains of meaning” that is presently isolated in silo’s, caught up in single ownership, or trapped in known locations like R&D, Production, Marketing that are not linked. Then you have the actual media that is “trapped”  in PDF, word, manuals, guidelines, all operating in islands of understanding. Without a common thread of data accessibility, we suffer from a lack of no integrated view, we limit our search functions and we continue to build inefficient workflows.

It is this lack of connecting all of this up really is placing demands on organizations that manage knowledge today, often very inefficiently. They constantly are turning to limited experts who often hold a selected knowledge in their head. Yet often these “experts” fail to see the “rich” possibilities in connecting-up all aspects of knowledge, as they simply do not have all the experiences and practical understanding of the very different connections and possibilities. Domain experts do.

The promise of Knowledge Graphs allows for the collected data and the connections to provide insight, influence decisions and set direction. The more you “see” all the connections and relationships the better these become.

Nearly every enterprise has a huge amount of data hidden in textual form (documents and files) and it is estimated 90% of this enterprise data is unstructured but if properly organized, it could create better customer experience, greater efficiencies, bring better products based on increased insight to market and constantly look to reduce inefficiencies and costs across the enterprise value chain.

We are told Artificial Intelligence is meant to be a panacea to solve all this current ‘disconnects’.  No, not without solutions like Knowledge Graphs alongside them, well actually underpinning AI. As we digitally transform and improve the IoT through cognitive/ AI solutions we will automate and personalize communications. It is recognizing the semantic technologies part that will deliver cognitive solutions that have the real value.

This value can be built in very different ways than today. It can have the incredible value of being driven by domain experts and actual business users, as they make and use their knowledge to make increased the connections, not an IT professional, unfamiliar on the relationships or lacking deep domain knowledge. That’s part of the promise of Knowledge Graphs, built by those that know, not by ones who think they know! Understanding relationships are down often to the experience you have in the domain of that knowledge

Domain experts are the new influencers of tomorrow, not media influencers as we see today. Content needs context.

As I have outlined early Knowledge Graphs underpin Artificial as well as Human Intelligence.

It is the ‘enriching of data’ by building descriptive aspects, forming and applying context, building solutions that are interoperative, able to be queried and searched that can give a very different granularity.

As we build concepts (things, again) these are units of meaning that can then link to other concepts, thus creating contextual frameworks that open-up to new possibilities. Context informs us of richer meaning. It is the building of conceptual networks both in us as humans and in our learning capabilities of computer systems give us fresh knowledge bases.

In the past, we have attempted to create taxonomies and ontologies with one arm tied behind our back. They were excessive in resources to build but with the understanding of building these through Knowledge Graphs and being increasing enhanced by machine-learning algorithms, coupled with software solutions specific to the knowledge building task,  we are getting closer to knowledge-driven reasoning, based on having a wider, connected understanding.

The ability to have greater intelligence at your fingertips holds the promise of improved ways to find, reuse, reconfigure and adapt relevant information. It can form the basis of the creation phase, providing suggestions in connections for new content.

So Knowledge Graphs build the domain knowledge by making explicit the concepts and relationships of entities (things). They are the backbone of cognitive and semantic solutions. They ‘advance search, personalization, and recommendations, based on greater connected knowledge. They turn unstructured data into structural ones that offer more connected capability.

Let’s return to some basic stuff to know about how Knowledge Graphs connect.

This is if you have not read Googles laymen’s guide mentioned above- without going into the real nitty-gritty details of Knowledge Graphs.

As brief an understanding as I, as a layman can give.  We are wanting to link data. Something I still have to get my head around is RDF’s – So far I think these are binary relationships (subject, predicate, and object, SPO’s) where subject and object are entities and a predicate is a relationship between the two. So are you with me so far?

Drawing from one paper I read “A Review of Relational Machine Learning for Knowledge Graphs, written by Messrs Nickel, Murphy, Tresp, and Gabrilovich in 2015, that gave a good example as shown above and the emerging relationships below.

It is the existence of a triple SPO indicates an existing fact. It is when we combine multiple SPO’s we form multiple graphs. Here it is nodes that represent entities (all subjects and objects) and directed edges represent relationships and where they ‘point’ builds the knowledge graph. Each SPO is a (stand-alone) fact. So we begin to build a Knowledge Graph as the above example shows.

Let me give you an outline of benefits of why Knowledge Graphs – seen by Siemens

This might help lift the understanding around Knowledge Graphs. Why Knowledge Graphs can become the “next big thing” to understand and relate too for industrial application. You connect people and their knowledge in different domains, the knowledge sharing, and the platform sharing. The value builds from greater connections and relationships.

Then the way Siemens see knowledge – as a service –  that works within their Mindsphere platform

The feeding in of understanding, into any platform like Mindsphere, improves perception, reasoning, and decision-making. You get a more “intelligible domain model” of something that might be complex, that many people (partners) need to understand and can increasingly relate too. More people connect and build deeper relationships with shared knowledge. More on this in a further post, sometime in the near future.

Why I see Knowledge Graphs as important

For industrial applications why do I see these Knowledge Graphs as important? They help in decision potential, in decision making, influencing to improve collective knowledge or signaling a greater potential can all be greater in possibility. Now that has the real power of combining knowledge graphs, extracted from related data, applying learning algorithms to end up with the outcome of Augmented Intelligence. This KG approach has the potential of being the knowledge that can lead to unlocking problems, and exploring new potential in design, solutions or prompting new actions.

If I have not lost you at this point, Knowledge Graphs move us from those islands of data, held in silo’s, we can begin to build through knowledge digitalization domain-specific knowledge, and through the building and increasing these through automated structures we can begin to discover new understanding behind knowledge as it connects up.

It is from these connected knowledge linkages we can build a learning memory and extract expertise to become the basis of our collective intelligence. We can discover, learn and build from this ‘connecting all our knowledge up’ to see new possibilities, and that is where Knowledge graphs offer, for me, the most exciting possibilities for innovation, a new discovery.

They can dramatically improve our domain knowledge, improve our common sense and discovery, by linking the relevant context and through this decent labeling of this, we can then manage and interpret far more complex schemas. Up to now knowledge has been kept “closed down” and used fairly inefficiently, parceled out sparingly, so it more than often constrains greater understanding. We have allowed it to form, ossify and become rigid and static, of limited use. It struggles to be reused or recombined into new insights, as we lack the ability to access islands of knowledge and form the relationships.

Knowledge calls for a constant re-working so we can be re-applied in a new context. It is knowledge coupled with insights that lead to new innovation. Knowledge Graphs sound tantalizing, will they be the next big thing?

• In my research and reviews on Knowledge Graphs, I found some useful references. Specifically IDC in some of their white papers on Semantic Technologies to give a deeper background understanding

The post The Arrival and Potential of Knowledge Graphs into Our World first appeared on Building Your Innovation & Ecosystem Intelligence.

My early present, well actually an idea, came the day before the event. I was included in the Industry Analysts visit to the Siemens Technology Center, at Neuperlach in Munich. We were provided a variety of insights in different presentations and demonstrations, of the technology they are working upon but one stood out for me, being introduced to Knowledge Graphs.

This one ‘thing’ really caught my attention. It was showcased in the technology center, briefly, as part of a broader set of presentations. It immediately struck me as having the potential to be very vital for the connected innovation I see, as our future.  These few insights set me off on a new train of thought and I scribbled down some hasty notes while listening to this concept. I then was able to review this a little more after the brief presentation. I then started to research on (Industrial) Knowledge Graphs for the initial depth of understanding I was seeking.

This really excites me in its potential. So let me tell you why

Everything we build upon is based on knowledge. If we retain this knowledge we are re-enforcing our potential for further learning and equally, beginning to question where that existing learning can go. The problem we have, all the time in my case, is our working memory gets layered over with new insights and knowledge. Often we fail to connect up old, already available knowledge with new thinking. We fail to see the connections. We don’t relate or access ‘resident’ knowledge

Why Knowledge Graphs excite me is we can build on Semantic Knowledge, building our Domains of Meaning and fill out the Conceptual Spaces, for adding new meaning and value. We can begin to capture all the knowledge relationships and make those essential connections between data (connecting the dots), deepen and reinforce our learning and then translate them into fresh insights. It can build our ‘collective’ memory, individually and in our communities.

Knowledge Graphs can fill a very big gap.

They can improve our domain knowledge, improve our common sense by linking the relevant context and through a decent labeling of this, we can manage far more complex schemas.

Siemens has partnered, firstly with Metaphacts and built their solutions on the AWS Neptune Engine, offering a query, open language system to build these Knowledge Graphs that works on building relations. Neptune offers knowledge graph that allows you to store information in a graph model and use graph queries, to enable your users to easily navigate highly connected data sets. Neptune supports open source and open standard APIs to allow you to quickly leverage existing information resources, to build your knowledge graphs and host them on a fully managed service.

Using a Resource Description Framework (RDF) a group of companies has been working with this in recent months. Besides Siemens, Astra Zeneca and Thomson Reuters have been looking to explore this. More importantly within Amazon, it is being used for the Amazon Alexa, so this is forming part of their future.

Up to now, knowledge has been kept and used fairly inefficiently, we have tended to keep it in our own way of doing or collecting things, it then becomes rigid in its design and inflexible to radically alter or redesign, without some massive reworking. What if that can change?

What if we can suddenly scale, have availability in different ways and design, be able to query in fast, reliable, easy and open ways? What does that do for knowledge? It opens it up finally, our silos of knowledge become challenged, ready to be built upon and capable of being changed with updated knowledge. Knowledge and insights lead to innovation. Our complete view opens up, it evolves over time and new insights, the more we build and enhance our knowledge, the richer potential we have to build with. Now that sounds tantalizing in what it possibly offers.

So, Siemens is working on Industrial Knowledge Graphs

They start with the premise that human decision-making depends on semantic knowledge for perception, reasoning, and decision-making

They are working to build the world of entities and relations to provide a more intelligible domain model instead of a complex (physical specific) data model. They build the multiple data sources (schema’s) and types of understanding, both structured and unstructured and present these in a formal semantic representation to enable inference (decision potential) and machine processing (decision-making or signaling)

This Machine Intelligence (MI) core can become the engine for realizing more Artificial Intelligence (AI) algorithms, based far more on this constant evolving richer domain knowledge. It reduces uncertainty and prompts greater actions.

Providing examples of everyday questions of Knowledge exchange

Now, what if we replaced Industrial with Innovation?

So, the industrial knowledge graph sounds really exciting, yet for me, I want to see the Innovation Knowledge Graph as my end result. Now is this a dream or reality? It is different and perhaps more intangible than industrial environments where knowledge is more tangible. You see, I have countless papers, references or visual prompters along with a vast collection of Mind Maps, built for specific innovation scenarios and knowledge capture and use. There is a vast array of insights, knowledge, and advice available within the domain of innovation, can this be captured and translated into an Innovation Knowledge Graph approach? Of course, you can question what is the “right” knowledge? What if I could translate these by capturing and forming better relationships, those can bring into a highly related set of data and knowledge understanding, connecting innovation?

We are all caught up in how digitalization is shaping much of what we do. Yet we all still need to break down isolated pockets of knowledge held in silo’s, we need to access data and knowledge faster and faster, we continue to be inefficient in how we go about this (our workflows) and we often yield far lower quality of outcome than ideal. Can this change? We are still at a very sub-optimal level, in innovation, in knowledge leverage, and in connecting all of our sources of knowledge and data.

What are the contributions of Knowledge Graphs?

Can we extract, generate, link and discover more, by having a system that allows us to connect data, as a graphics-based system, that can present in a highly visual form what we are seeking? Can we get closer to a more complete view, a view of innovation that does, finally, become holistic? Can we build a decent innovation domain ontology that allows expertise to come through, way beyond delivering only pieces of innovation knowledge? Siemens have a clear view it seems of Knowledge Graphs and their contribution to augmented intelligence. Can Innovation Knowledge Graphs help deliver a better relationship we all need in our innovation understanding?

Can we have an interactive application “in our hands” that is highly related in its data, its connections (over time) in relationships, to extract the best expert knowledge from the information, data, and observations, that allow us to focus on the important “sweet spots” of innovation?  To actually, constantly innovate, as we build the new, drawn from the knowledge built, on the best known, to form the new understanding, by connecting and forming a relationship, a network of innovation understanding.

What a thought as we enter 2018…….an Innovation Knowledge Graph that takes me closer to a better-connected future for leveraging innovation. Now then, how do I set about it? So I need to first find out more about this and then understand if Knowledge Graphs can be provided for innovation. Intriguing in its possibilities perhaps?

The post As we enter 2018 we will need Knowledge Graphs first appeared on Building Your Innovation & Ecosystem Intelligence.