#ontoforce

What is it, do you think, that makes you product stand out? One of the best ways to find that out is by simply asking your customers. And that’s what we did.

Mid-September 2015, we went to Harvard Medical School to sit down with a group of experienced users of our product DISQOVER. The goal: find out what they like about DISQOVER and what they believe is needed next. In short: we asked a group of 9 experienced and very active users of DISQOVER - mainly researchers and data scientists - to co-create our product roadmap.

Here’s how we approached our user group meeting:

Firstly, we set the scene: we explained the users where we are today, what the latest features of our product are and what’s next in the pipeline.

Then, we asked each participant to write down on a post-it her/his top 4 functionalities and top 4 databases that (s)he would like to see in DISQOVER.

Next, everyone posted her/his ideas on a wall. We clustered like-minded ideas and concepts to examine further what could be put into development.

To finish of the meeting, we asked the attendees 1) whether the session was valuable to them and 2) whether they would recommend DISQOVER to other potential users.

This feedback has now been added to our product roadmap. We’re still reviewing some timelines, but overall priorities have been set: 

“With the  addition of cell lines, antibodies etc. I would also be inclined to recommend  to researchers in R&D” Corporate Strategy Expert

“Yes, other  R&D institutes and large organizations can use this, like MIT labs,  Harvard etc.” MD

Tips for user group meetings

User group meetings are a great tool to generate business insights. Here are some general insights we took away from this session:

Chose experienced users: bring together a group of diverse but very experienced users. That way, you quickly get pinpointed requests. It’s better to avoid very generic requests that could be interpreted in various ways.

Create the right setting and clearly guide people. Tell participants exactly what it is you expect from them, and in what format. Stay focused and don’t stray from your objective.

Provide feedback back: facilitate interaction, join in the discussion and afterwards, send the resulting feedback to the users. That’s very important. Give them an overview of the ideas that were covered and what your next step will be. Even if it is not explicitly decided yet, underline that as well. People can handle a postponed decision as long the reason behind it is clearly explained.

Take the actions on board: user group feedback is not arbitrary; you have to be willing to take at least some of it on board. If not, you might as well not hold user group meetings.

Don’t jump in blindly: make sure you already have a good understanding of the concerns of your users. If you’re completely surprised by some of the suggestions, than you haven’t been in touch with your users well enough. The outcome should not be a complete overhaul of your product, but rather incremental updates.

What’s next?

This first user group meeting delivered great insights that help us build our product roadmap. But at the same time, it’s also reassuring that the exercise did not uncover any major discrepancies between us and our customers. This tells us that we’re on the same frequency and level as our users.

We are now actively using this to build our product roadmap. After all, when people take the time to provide you with feedback, it’s only right to thank them by taking their comments on board. We’re also reviewing whether we can hold more regular user group meetings using a webinar-like format. That way, we can have an even broader and more diverse user group. Look out for future sessions on this blog and our website.

Was it Henry Ford who said…

This is a question I get quite often: what is semantic search? I’ll give you the short and not-so-short answer. As well as some possible applications which we’ve included our own semantic search platform.

Semantic search in short

In short: semantic search extends the current search possibilities by looking at 1) the intent of the searcher as well as 2) the context within which the search takes place. When properly combined, you get more refined and better attuned search results.

OK, that makes sense. But when you do this on the general web, the task of uncovering intent and context becomes quite challenging. Imagine you’re planning a business trip to Paris. You go to Google and type in the keywords ‘Paris’ + ‘Hilton’, only to get dozens of celebrity pages. You’ll have to refine your keyword string (no pun intended!) to get to the actual Paris branch of the hotel chain.

And yes, Google is already making some assumptions and offers you some additional keywords to try and understand your intention better, but much of that is still after the fact.

Semantic search, not-so-short

Semantic search becomes a lot more actionable when you place it in a pre-defined context, such as an industry or a company. Instead of offering ‘best guesses’, the search technology can more quickly make assumptions about intent and context and fine tune the results accordingly.

Consider an R&D-driven industry like life sciences. What we do there, is connect different data sources (open and closed public data, internal and third party data) and tie it all together. We create an ontology, by which we mean that different descriptions for the same thing are given a unique identifier. So the system knows that ‘male’ is the same as ‘m’, is the same as ‘masculine’, and so on. That way, data from an unlimited number of different data sources can be compared and meaningful results can be generated. The strength of semantic search is that it can do this for an unlimited number of categories.

Semantic search and Big Data

In this way, semantic search overcomes the 4 Vs of Big Data: volume, velocity, variety and veracity. Making connections between ever-growing mountains of data and widely differing data sources is fueling the need for semantic search.

With semantic search, it doesn’t matter how many different databases you search in. The system will make the corresponding links between the bits of information and present it in a meaningful manner to you.

Semantic search reveals unexpected connections between your search query and different data sources. Consequently, it retrieves data you didn’t know you were looking for. Unlike regular search, which is mostly descriptive, semantic search can be both predictive and prescriptive. Semantic search makes you a whole lot smarter.

Semantic search also uncovers interconnections and relationships between data. Data is combined, compiled and represented in a meaningful way. Your data is enriched, making it more valuable.

Semantic search applications

Here are some cool items we’ve added to our own DISQOVER semantic search platform:

Saved search queries and collaboration: name your search, save it and share it with colleagues. Others can rerun your search, extend it or choose a different path, somewhere along the line, in order to uncover alternative routes for discovery. Collaborating with others on search patterns becomes a lot easier.

Rerun your saved searches. Because data changes all of the time, your research findings will change too. With saved searches, any new data since your last search, will automatically be included in your rerun. That way, you no longer miss out on relevant updates. The saved search path lets you revisit your search on a later date and rerun, resume or change it as you see fit.

Visualize data. The big bang of big data makes deriving insights hard. Visualization helps to better understand search results and facilitates telling a better data story. As your search progresses, our DISQOVER platform automatically updates the search result visualizations while new search criteria are offered for additional refinement. By simply adding or removing search filters, you can zoom in or out on the findings.

Depict the source. Instead of just giving you a search result list, we also create compiled content pages with data from different sources. But because it may be worthwhile to know which data comes from which source, we color code each part. That way, as a (re)searcher, you can define which information is more relevant or reliable, or which sources you would like to research further.

“The Semantic Web is not a separate Web but an extension of the current one, in which information is given well-defined meaning, better enabling computers and people to work in cooperation.” Tim Berners Lee

Everybody becomes a data scientist

Semantic search will become a lot more smarter soon. With ever more data becoming available—including also sensory data of where you are and what you’re state of mind is—generating useful search results will continually become more refined. Until then, companies can already create the right context and generate big wins with semantic search. Semantic search makes complex searches available to all, turning everyone into a data scientist.

How well is data and information management handled at your company? Are you using Big Data? Have you heard of semantic search? These were some of the questions we asked people at the Knowledge For Growth 2015 convention (May 2015). As a semantic search company, we learned that there’s still some education to be done around it. But, looking at it positively, semantic search is considered to be a highly relevant asset for many people within the life sciences industry. Here are the results of our survey.

Our hostesses looking for respondents.

The information and data age The Knowledge For Growth 2015 convention in Ghent welcomed 1100 attendees, and we were able to get feedback from 127 people in total (+/- 10% of attendees). When asked ‘how well is your company managing healthcare and life sciences information today?’, 47% of respondents stated that information is managed `well’ to `very well’, while 42% indicated that it is `not handled well’ to `quite well’. Which is quite shocking really, as the life sciences industry is highly dependent on properly unlocking information to fuel innovation. A point made also by Peter Piot in his morning keynote speech, when he talked about data and information as a driver for life sciences.

The attendees at the convention are very active and fervent searchers for information. 46% of the respondents spend more than half an hour each day searching for information.

With so much search activity going on, improving the accuracy and speed of search results can have a dramatic impact on overall productivity. The respondents acknowledged this: 43% of them believed that improved search functionalities could improve productivity by 20 to 50 per cent. Fourteen per cent considered the increase in productivity to be even more than 50%.

Democratizing data Because data and information disclosure is so important, we asked people how easily accessible this data is. While 35% of respondents indicated that most people within their company have data access and that they can manage the data themselves, the other 65% indicated that they are limited in some form or other to access the data.

In order to fuel innovation, though, step one is to have unrestricted, ready access to the data. This is often referred to as the democratization of data. A trend that is on the rise in recent years, also with the emphasis on more open innovation and more open collaborations across company borders.

The Big Data frenzy The Big Data hype is far from over but, in practice, there’s still very little that’s truly Big Data driven. Only 15% of respondents stated that the majority of their data activities are Big Data driven or that there is a fully integrated Big Data strategy. But a shocking 45% of respondents stated that there is no Big Data activity whatsoever, while another 12% said that they’re working on it.

Searching semantically One way of mining huge amounts of heterogeneous data is through ‘semantic search’. This terminology, however, doesn’t yet ring a bell for 60% of the people.

When briefly explained what semantic search is, 37% thinks that it would be of great benefit to their business. A percentage that increases to 45% for the people already familiar with semantics.

A semantic future Every industry that is driven by data, is challenged to create more value out of that data. And increasingly, that is literally ‘every’ industry. But limited data access, a lack of data democratization, a lack of Big Data initiatives and poorly designed search capabilities are causing companies to lose money. Technologies such as semantic search can quickly close the gap between ‘searching blind’ and ‘finding relevant results immediately’. Semantic search can separate different meanings, it categorizes them and presents the results in a filtered and clustered manner, so that the most relevant information can be pinpointed quicker and easier. Because of its extreme scalability, it can be applied to a virtually unlimited number of data sources. Semantic search ‘glues’ the relevant data together as Linked Data in order to generate more meaningful insights. Try out DISQOVER, our semantic search technology, for free for 14 days. After all, the best way to find out what semantic search can do for you and your company is to try it out.

About the survey: the data used in this blog was collected via a survey during the Knowledge For Growth 2015 convention (May 2015) at the International Conference Center in Ghent, Belgium. There were approximately 1,100 attendees from hundreds of different life sciences organizations.  Two hostesses roamed the convention’s floor with iPads (and printed survey forms) to capture the feedback. Attendees were approached randomly. A total of 127 people completed the survey and were offered a free smoothie at the ONTOFORCE stand in return.

Around 40 participants joined us for our ‘Big Data Workshop: Getting knowledge out of (Big) data’. Hans Constandt, CEO, and Filip Pattyn, Product Manager and Bioinformatician at ONTOFORCE, introduced and demonstrated DISQOVER – our platform to quickly discover insights out of vast amounts of data.

Data discovery is chiefly about next-generation business intelligence and analytics (http://www.gartner.com/newsroom/id/2970917). But all too often, expert researchers depend on data specialists to disclose the relevant data resources. What DISQOVER does, is close the gap between the data specialists and the experts. With our platform, users discover the knowledge themselves. ONTOFORCE’s mission is to let users derive their own insights and DISQOVER is built around this.

The data avalanche

The huge and continually growing amount of data isn’t troublesome, searching the data is. All too often, you only get what you know already. If you don’t know where to search or what to search for, it becomes very difficult to discover the real, hidden value. We therefore need to change the modes and techniques of searching. We need new systems that can quickly integrate new data and which facilitate searching. This enables you to discover insights that you might not even have been looking for.

To do that, DISQOVER uses semantic search, which is still a fairly novel term. When asked who knows about semantic web technologies, only a few hands go up. Consider going on a city trip to Paris, and you want to stay at the Hilton. A Google search on the combination ‘Paris’ + ‘Hilton’ generates 200+ million results, but you need to scroll through quite a few pages of celebrity gossip before you reach some results on the Hilton hotel in Paris. Semantic search separates different meanings, categorizes them and presents the results in a filtered and clustered manner, so that you can pinpoint the most relevant information quicker and easier. The advantage of semantics is the extreme scalability and, thus, it can be applied to a virtually unlimited number of data sources. Relevant data is dynamically ‘glued’ together as Linked Data to lead into meaningful insights.

How does this apply to life sciences? DISQOVER is linked to a wide variety of open data sources. As the speakers in the morning already highlighted, progress comes from open collaboration and open innovation. When looking for, say, an active pharmaceutical ingredient, you may have to search the protein databases, the molecule DBs, the gene DBs, the disease DBs… Today, too many people still have to consult each of these DBs separately, after which different files need to be collated. A typical search use-case such as the one described above, can quickly take up to 50+ hours and 4 weeks of throughput time to deliver the desired results. And much knowledge – such as search queries – gets lost along the way. What DISQOVER does, is glue all these sites and DBs together. On top of that, there’s an intuitive and simple interface to visualize the aggregated data. All very scalable. The time this takes? One hour. The insights at your fingertips.

The proof of the pudding is in the eating

Now, imagine you’re searching companies producing antimetabolite drugs that are involved in phase II clinical trials where drug cancer patients can still enroll, while EGFR must be mentioned and, in addition, you also want to know who are the top authors publishing about these drugs.

Researching this the old way would mean…

-          You have to search in different databases, -          Digest all the information yourself, -          Stitch it all together in a workable format.

The trouble: it’s a hell of a lot of work while the results remain a snapshot at a fixed moment in time. Any updates or changes in any of the consulted databases is out of sight. So if you want to update your information, you have to run the process all over again. Not the most efficient or effective way of searching.

What DISQOVER does is link all the information while keeping track of the search patterns and logic. Often, when we do a classic web search, we start at one place but ultimately end up somewhere else with a lot of open ends. Retracing your steps becomes extremely difficult. While the search logic itself – the reasoning during your search – is valuable and can be modified during a subsequent search.  

Time for the rubber to hit the road.

Filip starts up DISQOVER to try the above search example.  

-          On the start page, there’s no extensive overview of all possible functionalities. Instead, a simple keyword search field is the starting-point.  -          Filip kicks off the query with the keyword ‘EGFR’. A total of 26 databases are being searched and, almost immediately, the DISQOVER platform shows the different results found across a wide variety of data types. -          Each of these data types is a first filter:  clicking on ‘clinical trials’, for instance, leads you to the clinical trial results. -          Filip then filters further on phase II studies. With every new filter setting, all other filters are instantaneously updated. This prevents filtering combinations from producing no results. -          The next filter is the status ‘still recruiting’. A global map then indicates where these clinical trials are happening. -          As there are too many results still, one more filter is added, looking for everything related to lung cancer.

Filip meanwhile explains to the audience that the data is being visualized instantly. Although DISQOVER is not a tool for creating dashboards, it automatically includes these along with the actual search results, each containing references to the original sources.

Filip also points out that ONTOFORCE is not a data owner but a data broker. The DISQOVER platform queries open databases. ONTOFORCE continually discusses the merits of unlocking additional databases with new partners. Within DISQOVER, additional databases – be they open,  licensed or private – can be added.

-          In the meantime, Filip follows the link from clinical trials to drugs. These are the drugs related to the resulting clinical trials. A new series of filters becomes available. What is cool about the tool, is that it automatically adjusts the available search possibilities based on the available criteria. Like gentle nudges offering you opportunities to discover new places where you might not have searched before.

-          Filip includes ‘antimetabolites’ as an ATC classification filter.

-          The filter that shows all manufacturers contains the first portion of information requested.

An extra neat feature is that you don’t have to visit different websites: DISQOVER already compiles different pieces of content. To make sure the origin of each section is clear, the data can be highlighted with a different color code per individual source.

Filip now switches his attention to retrieving the key authors and publications:

-          He follows the link from the resulting drugs to publications.

-          To make the result more refined, an additional filter is added, looking only for publications of the last 5 years.

-          Automatically, the system has semantically identified the top publishers; one of them is in fact someone from the KULeuven.

All in all, this search took around 15 minutes (including the explanation to the audience). What is extra helpful, is that the search pattern is saved for later use. DISQOVER automatically keeps track of the search pattern as a sort of timeline. Clicking on a step in the search path automatically reproduces a search result. Each adjustment to a previous search step is also stored as a new branch. As such, the thinking path or pattern is stored and you (or any of your colleagues) can reproduce the search to see what was updated since the initial search was made. Just as easily, you can name your search, save it, share it with others, ‘rebranch’ the data, collaborate with peers on search patterns and extend insights accordingly.

Someone in the audience asks: “what exactly do you mean by ‘links’?”

A link is simply a piece of content available somewhere in the vast number of databases that connects two or more data concepts. An example is the connection between a drug and the clinical trial in which the drug is tested. What is cool about DISQOVER is that it also includes `mentions’: the number of times a content item has been mentioned per data type. This is something unique due to the vast amount of data sources covered in DISQOVER.

Combining resources and integrating data

The value of semantic search increases with every additional database that’s added. ONTOFORCE continues to partner with as many different data providers as possible, requesting them to open up their data. ONTOFORCE is working with, amongst others, Harvard Catalyst Group in Cambridge US. They developed the Eagle-I network, which aims to open up biomedical scientific research. The consortium already has 41 universities and research institutions in the US and ONTOFORCE is assisting to bring the platform to Europe. The case of the BCCM/LMBP Plasmid Collection, hosted at Ghent University (UGent) and part of the Belgian Coordinated Collection of Microorganisms (BCCM), clarifies further how additional data sources can be disclosed. This organization already has a searchable catalogue, but it is isolated and not linked to publications. ONTOFORCE assisted in integrating the plasmid catalogue into UGent’s eagle-i platform, which now can be searched semantically, while a referral to the original page remains in place.

Another good example of the value of semantics is applicable to The Antibody Registry. Because there’s no nomenclature used for how to define an antibody, antibody descriptions are notoriously heterogeneous. For researchers it’s extremely hard to know which antibody is the correct one. Through the application of the eagle-I ontology*, relationships between different antibody descriptions and other data types can be established, relevant filters can be defined and synonyms are identified more quickly, making antibody data retrievals much easier.

ONTOFORCE aims to continually simplify its DISQOVER user interface (UI), so that it becomes straightforward for anyone to use. DISQOVER can become the new standardized way to smartly research dozens of different data sources. You don’t need IT people or data scientists to catalogue all new information. It’s about bridging the data gap between research, academics and industry.

In our next blog, we will cover the Q&A that followed our workshop.

* In computer science and information science, an ontology is a formal naming and definition of the types, properties and interrelationships of the entities that really or fundamentally exist for a particular domain of discourse. An ontology compartmentalizes the variables needed for some set of computations and establishes the relationships between them. Source:

Some events are absolutely great and `Knowledge For Growth 2015’ fitted the bill perfectly. A big crowd (±1,100 people) of highly relevant life sciences people, a wide range of different topics, enthusiastic speakers, 90+ exhibitors, and yes, the food was great too. Here’s our report from K4G 2015.

The importance of biotech

It’s not every day a fair is opened by … a bell. The Euronext Bell Ceremony at 9 o’clock demonstrated the growing relevance of biotech on the Brussels stock market. Biotech’s market value has grown fast these past few years to €15+ billion and I’m sure many of us are looking to add even more value to this.

Peter Piot, Director & Professor of Global Health, London School of Hygiene & Tropical Medicine, was the first to take the stage. He summed up some of the major global health challenges we’re faced with today: ever-more resistant bacteria, insects that are immune to pesticides, climate change, ageing populations, … There are many things we can and must do to remedy these challenges. Professor Piot strongly emphasized the importance of 1) public private partnership models as well as 2) innovation to do this.

“It’s not just about delivery in innovation, it’s also about innovation in delivery.”

Peter Piot, @Knowledge For Growth 2015, keynote presentation, May 2015

Professor Piot had already referred to mobile and smartphones as a means to bring healthcare to people in poorer parts of the world and the second speaker, Young Sohn, President and Chief Strategy Officer at Samsung Electronics, enlarged on that. Samsung, which ships 600 million devices per year, is increasingly looking at connecting all these devices to each other. A reference, of course, to the Internet-of-Things (IoT). This will be highly relevant in delivering personalized, preventive healthcare advice to people.

“Everything we do, everything we touch will have the power of connectivity.”

Young Sohn, @Knowledge For Growth 2015, keynote presentation, May 2015

A big challenge Sohn sees, is that today, data is siloed. And siloed data does not give you many insights. So opening up data and making it more freely available will become increasingly important. Sohn demonstrated this by mentioning a few of the initiatives they support, such as an IoT platform, which enables non-developers to ‘write’ code that interconnects different devices. Many of these applications will also extend beyond healthcare: more data can help, for example, in tackling some of the climate change challenges, such as extreme drought in certain parts of the world.

Droughts and climate change are also high on the agenda of Kemal Malik, responsible for Innovation at Bayer AG, the last of the big companies that still combines pharma, healthcare and crop sciences. The reason: there’s a lot of overlap in the biology of humans and other species. Even the fruit fly shares about 60% of its DNA with humans. Increasingly, learning from different species can generate new insights and new solutions. But to do so, Malik is convinced companies need to collaborate with other parties. To achieve this, Bayer launched several open innovation projects and collaborator spaces where start-ups can work at Bayer offices. The days that big companies can do everything on their own are gone: not all the smart people work for your company and, increasingly, small companies can do more on their own. Many innovations can happen without big funding or investments.

“We can’t get the science right alone, we need partners.”

Kemal Malik, @Knowledge For Growth 2015, keynote presentation, May 2015

The importance of data in research

The three plenary speakers at the morning session all had one message in common: the importance of data in research is growing by the day and embracing the data will be the key driver to solve many of the challenges we face. Not just when it comes to medicines, but also in agriculture, improved research methods can speed up the discovery of solutions. Preventive health, digital health, personalized medicines, … many of these innovations are data driven.

“The data revolution is driving more and more of what we’re doing.”

Peter Piot, @Knowledge For Growth 2015, keynote presentation, May 2015

All speakers also emphasized who’s at the center of all activity: people. And that’s also precisely why we started ONTOFORCE. To help patients. To help people find relevance in an ever-growing amount of data.

That was also the topic of our own workshop. And the topic of PART II of our blog on Knowledge for Growth 2015.