There is a new functionality in PoolParty 5.5 that allows users to manage the skos:inScheme relationship of their concepts.
When you activate the skos:inScheme functionality for your PoolParty project you can create input data for SKOS Play! very easy. SKOS Play! is a free application that lets you render and visualize SKOS taxonomies in different formats (html, pdf) and different graphical representations (tree tabular, etc.).
With four steps you can generate such a representation based on PoolParty data:1) Activate skos:inScheme in your PoolParty project: 2) Apply skos:inScheme settings for concepts in your taxonomy.
For existing concepts, user can select the subtree in which the skos:inScheme setting should be applied. For new concepts you can define a behavior to automatically apply the inScheme setting on the active subtree.
This is a screenshot of a small PoolParty subtree, showing beverages that are used for cocktail creation:
Like usual, you can see the skos:ConceptScheme in purple. The narrower nodes in green represent skos:Concepts. All skos:Concepts in this subtree have a skos:inScheme relation to the skos:ConceptScheme with title “Beverages”.
3) SKOS Play!
When your PoolParty project is publicly available (help page explaining user groups in PoolParty), you can simply copy the URL of the corresponding SPARQL endpoint and paste it into the SKOS Play! input field during the upload process: http://labs.sparna.fr/skos-play/upload. In this example I simply used the SPARQL endpoint of the Cocktails thesaurus: http://vocabulary.semantic-web.at/PoolParty/sparql/cocktails. As an alternative you could also export you PoolParty project and import the resulting file in SKOS Play! A corresponding file you could retrieve from http://vocabulary.semantic-web.at/cocktails/export/cocktails.trig
For simplicity you can skip the advanced options.4) Get results
After you hit the Next button you receive feedback that concept data was processed successfully on the top of the page. When you scroll down you have options to select the skos:ConceptScheme and language that should be further processed. In addition you have the option to print and to visualize your data. Printing lets you select between alphabetical index and tree. Both version are clickable and can be created in html or pdf format. Visualization offers different types like a collapsible tree, zoomable square or circle representations and also an autocomplete form.
I chose the tree visualization which results in a nice interactive tree. Users can click circles to unfold the tree. When a label is clicked, the user is directed to this concept URI. In this use case the user is directed to PoolParty Linked Data Frontend.
And the cool thing is that you can simply download the generated tree by right hand mouse button > Save as…
You simply have to edit the downloaded raw html file to have a fully working visualization: delete the svg element completely to generate an empty div element (id=”body”).
The generated html code can be downloaded here: SKOSPlay_blogpost.zip
By the way, you can also see a PoolParty thesaurus visualization, powered with SKOS Play! on this page: http://www.reegle.info/glossary
I also wanted to share some statistics from registration that might be of general interest.
The total number of people attending: 3103.
Industry: 47% University: 46%
Male: 83% Female: 14%
Local (NY, NJ, or CT): 27%
North America: 70% Europe: 18% Asia: 9% Middle East: 2% Remainder: <1% including 2 from Antarctica
The 12th edition of the SEMANTiCS, which is a well known platform for professionals and researchers who make semantic computing work, will be held in the city of Leipzig from September 12th till 15th. We are proud to announce the final program of the SEMANTiCS conference. The program will cover 6 keynote speakers, 40 industry presentations, 30 scientific paper presentations, 40 poster & demo presentations and a huge number of satellite events. Special talks will given by Thomas Vavra from IDC and Sören Auer, who will feature the LEDS track. On top of that there will be a fishbowl session ‘Knowledge Graphs – A Status Update’ with lightning talks from Hans Uszkoreit (DFKI) and Andreas Blumenauer (SWC). This week, the set of our distinguished keynote speakers has been fixed and we are quite excited to have them at this years’ edition of SEMANTiCS. Please join us to listen to talks from representatives from IBM, Siemens, Springer Nature, Wikidata, International Data Corporation (IDC), Fraunhofer IAIS, Oxford University Press and the Hasso-Plattner-Institut, who will share their latest insights on applications of Semantic technologies with us. To register and be part of the SEMANTiCS 2016 in Leipzig, please go to: http://2016.semantics.cc/registration.Share your ideas, tools and ontologies, last minute submissions
Meetup: Big Data & Linked Data – The Best of Both Worlds
On the first eve of the SEMANTiCS conference we will discuss how Big Data & Linked Data technologies could become a perfect match. This meetup gathers experts on Big and Linked Data to discuss the future agenda on research and implementation of a joint technology development.
If you are interested to present your idea, approach or project which links Semantic technologies with Big Data in an ad-hoc lightning talk, please get in touch with Thomas Thurner (email@example.com).
This year’s SEMANTiCS is starting on September 12th with a full day of exciting and interesting satellite events. In 6 parallel tracks scientific and industrial workshops and tutorials are scheduled to provide a forum for groups of researchers and practitioners to discuss and learn about hot topics in Semantic Web research.
How to find users and feedback for your vocabulary or ontology?
The Vocabulary Carnival is a unique opportunity for vocabulary publishers to showcase and share their work in form of a poster and a short presentation, meet the growing community of vocabulary publishers and users to build useful semantic, technical and social links. You can join the Carnival Minute Madness on the 13th of September.
Submit your vocabulary to the VOCARNIVAL here: http://2016.semantics.cc/vocarnival/submission
More info: http://2016.semantics.cc/vocarnival
How to submit to ELDC?
The European Linked Data Contest awards prizes to stories, products, projects or persons presenting novel and innovative projects, products and industry implementations involving linked data. The ELDC is more than yet another competition. We envisage to build a directory of the best European projects in the domain of Linked Data and the Semantic Web. This year the ELDC is awarded in the categories Linked Enterprise Data and Linked Open Data, with €1.500,- for each of the winners. Submission deadline is August 31, 2016.
Submit your product, story or project here: http://2016.semantics.cc/eldc
7th DBpedia Community Meeting in Leipzig 2016
Co-located with SEMANTiCS, the next DBpedia meeting will be held at Leipzig on September 15th. Experts will speak about topics such as Wikidata: bringing structured data to Wikipedia with 16.000 volunteers. The 7th edition of this event covers a DBpedia showcase session, breakout sessions and a DBpedia Association meeting where we will discuss new strategies and which direction is important for DBpedia. If you like to become part of the DBpedia community and present your ideas, please submit your proposal or check our meeting website: http://wiki.dbpedia.org/meetings/Leipzig2016
We would be delighted to welcome new sponsors for SEMANTiCS 2016. You will find a number of sponsorship packages with an indication of benefits and prices here: http://semantics.cc/sponsorship-packages.
Special offer: You can buy a special SEMANTiCS industry ticket for €400 which includes a poster presentation at our marketplace. So take the opportunity to increase the visibility of your company, organisation or project among an international and high impact community. If you are interested, please contact us via email to firstname.lastname@example.org.
Things, not Strings
Entity-centric views on enterprise information and all kinds of data sources provide means to get a more meaningful picture about all sorts of business objects. This method of information processing is as relevant to customers, citizens, or patients as it is to knowledge workers like lawyers, doctors, or researchers. People actually do not search for documents, but rather for facts and other chunks of information to bundle them up to provide answers to concrete questions.
Strings, or names for things are not the same as the things they refer to. Still, those two aspects of an entity get mixed up regularly to nurture the Babylonian language confusion. Any search term can refer to different things, therefore also Google has rolled out its own knowledge graph to help organizing information on the web at a large scale.
Semantic graphs can build the backbone of any information architecture, not only on the web. They can enable entity-centric views also on enterprise information and data. Such graphs of things contain information about business objects (such as products, suppliers, employees, locations, research topics, …), their different names, and relations to each other. Information about entities can be found in structured (relational databases), semi-structured (XML), and unstructured (text) data objects. Nevertheless, people are not interested in containers but in entities themselves, so they need to be extracted and organized in a reasonable way.
Machines and algorithms make use of semantic graphs to retrieve not only simply the objects themselves but also the relations that can be found between the business objects, even if they are not explicitly stated. As a result, ‘knowledge lenses’ are delivered that help users to better understand the underlying meaning of business objects when put into a specific context.
Personalization of information
The ability to take a view on entities or business objects in different ways when put into various contexts is key for many knowledge workers. For example, drugs have regulatory aspects, a therapeutical character, and some other meaning to product managers or sales people. One can benefit quickly when only confronted with those aspects of an entity that are really relevant in a given situation. This rather personalized information processing has heavy demand for a semantic layer on top of the data layer, especially when information is stored in various forms and when scattered around different repositories.
Understanding and modelling the meaning of content assets and of interest profiles of users are based on the very same methodology. In both cases, semantic graphs are used, and also the linking of various types of business objects works the same way.
Recommender engines based on semantic graphs can link similar contents or documents that are related to each other in a highly precise manner. The same algorithms help to link users to content assets or products. This approach is the basis for ‘push-services’ that try to ‘understand’ users’ needs in a highly sophisticated way.
‘Not only MetaData’ Architecture
Together with the data and content layer and its corresponding metadata, this approach unfolds into a four-layered information architecture as depicted here.
Following the NoSQL paradigm, which is about ‘Not only SQL’, one could call this content architecture ‘Not only Metadata’, thus ‘NoMeDa’ architecture. It stresses the importance of the semantic layer on top of all kinds of data. Semantics is no longer buried in data silos but rather linked to the metadata of the underlying data assets. Therefore it helps to ‘harmonize’ different metadata schemes and various vocabularies. It makes the semantics of metadata, and of data in general, explicitly available. While metadata most often is stored per data source, and therefore not linked to each other, the semantic layer is no longer embedded in databases. It reflects the common sense of a certain domain and through its graph-like structure it can serve directly to fulfill several complex tasks in information management:
- Knowledge discovery, search and analytics
- Information and data linking
- Recommendation and personalization of information
- Data visualization
Graph-based Data Modelling
Graph-based semantic models resemble the way how human beings tend to build their own models of the world. Any person, not only subject matter experts, organize information by at least the following six principles:
- Draw a distinction between all kinds of things: ‘This thing is not that thing’
- Give things names: ‘This thing is my dog Goofy’ (some might call it Dippy Dawg, but it’s still the same thing)
- Categorize things: ‘This thing is a dog but not a cat’
- Create general facts and relate categories to each other: ‘Dogs don’t like cats’
- Create specific facts and relate things to each other: ‘Goofy is a friend of Donald’, ‘Donald is the uncle of Huey, Dewey, and Louie’, etc.
- Use various languages for this; e.g. the above mentioned fact in German is ‘Donald ist der Onkel von Tick, Trick und Track’ (remember: the thing called ‘Huey’ is the same thing as the thing called ‘Tick’ – it’s just that the name or label for this thing that is different in different languages).
These fundamental principles for the organization of information are well reflected by semantic knowledge graphs. The same information could be stored as XML, or in a relational database, but it’s more efficient to use graph databases instead for the following reasons:
- The way people think fits well with information that is modelled and stored when using graphs; little or no translation is necessary.
- Graphs serve as a universal meta-language to link information from structured and unstructured data.
- Graphs open up doors to a better aligned data management throughout larger organizations.
- Graph-based semantic models can also be understood by subject matter experts, who are actually the experts in a certain domain.
- The search capabilities provided by graphs let you find out unknown linkages or even non-obvious patterns to give you new insights into your data.
- For semantic graph databases, there is a standardized query language called SPARQL that allows you to explore data.
- In contrast to traditional ways to query databases where knowledge about the database schema/content is necessary, SPARQL allows you to ask “tell me what is there”.
Making the semantics of data and metadata explicit is even more powerful when based on standards. A framework for this purpose has evolved over the past 15 years at W3C, the World Wide Web Consortium. Initially designed to be used on the World Wide Web, many enterprises have been adopting this stack of standards for Enterprise Information Management. They now benefit from being able to integrate and link data from internal and external sources with relatively low costs.
At the base of all those standards, the Resource Description Framework (RDF) serves as a ‘lingua franca’ to express all kinds of facts that can involve virtually any kind of category or entity, and also all kinds of relations. RDF can be used to describe the semantics of unstructured text, XML documents, or even relational databases. The Simple Knowledge Organization System (SKOS) is based on RDF. SKOS is widely used to describe taxonomies and other types of controlled vocabularies. SPARQL can be used to traverse and make queries over graphs based on RDF or standard schemes like SKOS.
With SPARQL, far more complex queries can be executed than with most other database query languages. For instance, hierarchies can be traversed and aggregated recursively: a geographical taxonomy can then be used to find all documents containing places in a certain region although the region itself is not mentioned explicitly.
Standards-based semantics also helps to make use of already existing knowledge graphs. Many government organisations have made available high-quality taxonomies and semantic graphs by using semantic web standards. These can be picked up easily to extend them with own data and specific knowledge.
Semantic Knowledge Graphs will grow with your needs!
Standards-based semantics provide yet another advantage: it is becoming increasingly simpler to hire skilled people who have been working with standards like RDF, SKOS or SPARQL before. Even so, experienced knowledge engineers and data scientists are a comparatively rare species. Therefore it’s crucial to grow graphs and modelling skills over time. Starting with SKOS and extending an enterprise knowledge graph over time by introducing more schemes and by mapping to other vocabularies and datasets over time is a well established agile procedure model.
A graph-based semantic layer in enterprises can be expanded step-by-step, just like any other network. Analogous to a street network, start first with the main roads, introduce more and more connecting roads, classify streets, places, and intersections by a more and more distinguished classification system. It all comes down to an evolving semantic graph that will serve more and more as a map of your data, content and knowledge assets.
Semantic Knowledge Graphs and your Content Architecture
It’s a matter of fact that semantics serves as a kind of glue between unstructured and structured information and as a foundation layer for data integration efforts. But even for enterprises dealing mainly with documents and text-based assets, semantic knowledge graphs will do a great job.
Semantic graphs extend the functionality of a traditional search index. They don’t simply annotate documents and store occurrences of terms and phrases, they introduce concept-based indexing in contrast to term based approaches. Remember: semantics helps to identify the things behind the strings. The same applies to concept-based search over content repositories: documents get linked to the semantic layer, and therefore the knowledge graph can be used not only for typical retrieval but to classify, aggregate, filter, and traverse the content of documents.
PoolParty combines Machine Learning with Human Intelligence
Semantic knowledge graphs have the potential to innovate data and information management in any organisation. Besides questions around integrability, it is crucial to develop strategies to create and sustain the semantic layer efficiently.
Looking at the broad spectrum of semantic technologies that can be used for this endeavour, they range from manual to fully automated approaches. The promise to derive high-quality semantic graphs from documents fully automatically has not been fulfilled to date. On the other side, handcrafted semantics is error-prone, incomplete, and too expensive. The best solution often lies in a combination of different approaches. PoolParty combines Machine Learning with Human Intelligence: extensive corpus analysis and corpus learning support taxonomists, knowledge engineers and subject matter experts with the maintenance and quality assurance of semantic knowledge graphs and controlled vocabularies. As a result, enterprise knowledge graphs are more complete, up to date, and consistently used.“An Enterprise without a Semantic Layer is like a Country without a Map.
PoolParty Academy offers three E-Learning tracks that enable customers, partners and individual professionals to learn Semantic Web technologies and PoolParty Semantic Suite in particular.
David Silver gave one of the best tutorials I’ve seen on his group’s recent work in “deep” reinforcement learning. I learned about a few new techniques, including the benefits of asychrononous updates in distributed Q-learning https://arxiv.org/abs/1602.01783, which was presented in more detail at the main conference. The new domains being explored were exciting, as were the improvements made on the computational side. I would love to seen more pointers to some of the related work from the tutorial, particularly given there was such an exciting mix of new techniques and old staples (e.g. experience replay http://www.dtic.mil/dtic/tr/fulltext/u2/a261434.pdf ), but the talk was so information packed it would have been difficult.
Pieter Abbeel gave an outstanding talk in the Abstraction in RL workshop http://rlabstraction2016.wix.com/icml#!schedule/bx34m, and (I heard) another excellent one during the deep learning workshop. It was rumored that Aviv Tamar gave an exciting talk (I believe on this http://arxiv.org/abs/1602.02867) , but I was forced to miss it to see Rong Ge’s https://users.cs.duke.edu/~rongge/ outstanding talk on a new-ish geometric tool for understanding non-convex optimization, the strict saddle. I first read about the approach here http://arxiv.org/abs/1503.02101, but at ICML he and other authors have demonstrated a remarkable number of problems that have this property that enables efficient optimization via an stochastic gradient descent (and other) procedures.This was a theme of ICML— an incredible amount of good material, so much that I barely saw the posters at all because there was nearly always a talk I wanted to see!
Rocky Duan surveyed some benchmark RL continuous control problems http://jmlr.org/proceedings/papers/v48/duan16.pdf An interesting theme of the conference— and came up in conversation with John Schulman and Yann LeCun– was really old methods working well. In fact, this group demonstrated that variants of the natural/covariant policy gradient proposed originally by Sham Kakade (with a derivation here: http://repository.cmu.edu/cgi/viewcontent.cgi?article=1080&context=robotics) are largely at the state-of-the-art on many benchmark problems. There are some clever tricks necessary for large policy classes like neural networks (like using a partial-least squares-style truncated conjugate gradient to solve for the change in policy in the usual F \delta = \nabla one solves in the natural gradient procedure) that dramatically improve performance (https://arxiv.org/abs/1502.05477). I had begun to view these methods as doing little better (or worse) then black-box search, so it’s exciting to see them make a comeback.
Chelsea Finn http://people.eecs.berkeley.edu/~cbfinn/ gave an outstanding talk on this work https://arxiv.org/abs/1603.00448. She and co-authors (Sergey Levine and Pieter) effectively came up with a technique that lets one apply Maximum Entropy Inverse Optimal Control without the double-loop procedure and using policy gradient techniques. Jonathan Ho described a related algorithm http://jmlr.org/proceedings/papers/v48/ho16.pdf that also appeared to mix policy gradient and an optimization over cost functions. Both are definitely on my reading list, and I want to understand the trade-offs of the techniques.
Both presentations were informative, and both made the interesting connection to Generative Adversarial Nets (GANS) http://arxiv.org/abs/1406.2661 . These were also a theme of the conference in both talks and during discussions. A very cool idea getting more traction, and being embraced by the neural net pioneers.
David Belanger https://people.cs.umass.edu/~belanger/belanger_spen_icml.pdf gave a interesting talk on using backprop to optimize a structured output relative to a a learned cost function. I left thinking the technique was closely related to inverse optimal control methods and the GANs, and wanting understand how implicit differentiation wasn’t being used to optimize the energy function parameters.
Speaking of neural net pioneers— there was lots of good talks during both the main conference and workshops on what’s new — and what’s old https://sites.google.com/site/nnb2tf/— in neural network architectures and algorithms.
I was intrigued by http://jmlr.org/proceedings/papers/v48/balduzzi16.pdf and particularly by the well written blog post it mentions http://colah.github.io/posts/2015-09-NN-Types-FP/ by Christopher Olah. The notion that we need language tools to structure the design of learning programs (e.g. http://www.umiacs.umd.edu/~hal/docs/daume14lts.pdf) and have tools to reason about them seems to be gaining currency. After reading these, I began to view some of the recent work of Wen, Arun, Byron, and myself (including at http://jmlr.org/proceedings/papers/v48/sun16.pdf ICML) in this light— generative RNNs “should” have a well defined hidden state whose “type” is effectively (moments of) future observations. I wonder now if there is a larger lesson here in the design of learning programs.
Nando de Freitas and colleagues approach of separating value and advantage function predictions in one network http://jmlr.org/proceedings/papers/v48/wangf16.pdf was quite interesting and had a lot of buzz. Ian Osband gave an amazing talk on another topic that previously made me despair: exploration in RL http://jmlr.org/proceedings/papers/v48/osband16.pdf. This is one of few approaches that combines the ability to function approximation with rigorous exploration guarantees/sample complexity in the tabular case (and amazingly *better* sample complexity then previous papers that work only in the tabular case). Super cool and also very high on my reading list.
Boaz Barak http://www.boazbarak.org/ gave a truly inspired talk that mixed a kind of coherent computationally-bounded Bayesian-ism (Slogan: ”Compute like a frequentist, think like a Bayesian.”) with demonstrating a lower bound for SoS procedures. Well outside of my expertise, but delivered in a way that made you feel like you understood all of it.
Honglak Lee gave an exciting talk on the benefits of semi-supervision in CNNs http://web.eecs.umich.edu/~honglak/icml2016-CNNdec.pdf. The authors demonstrated that a remarkable amount of information needed to reproduce an input image was preserved quite deep in CNNs, and further that encouraging the ability to reconstruct could significantly enhance discriminative performance on real benchmarks.
The problem with this ICML is that I think it would take literally weeks of reading/watching talks to really absorb the high quality work that was presented. I’m *very* grateful to the organizing committee http://icml.cc/2016/?page_id=39 for making it so valuable.
Reinforcement learning is much discussed these days with successes like AlphaGo. Wouldn’t it be great if Reinforcement Learning algorithms could easily be used to solve all reinforcement learning problems? But there is a well-known problem: It’s very easy to create natural RL problems for which all standard RL algorithms (epsilon-greedy Q-learning, SARSA, etc…) fail catastrophically. That’s a serious limitation which both inspires research and which I suspect many people need to learn the hard way.
Removing the credit assignment problem from reinforcement learning yields the Contextual Bandit setting which we know is generically solvable in the same manner as common supervised learning problems. I know of about a half-dozen real-world successful contextual bandit applications typically requiring the cooperation of engineers and deeply knowledgeable data scientists.
Can we make this dramatically easier? We need a system that explores over appropriate choices with logging of features, actions, probabilities of actions, and outcomes. These must then be fed into an appropriate learning algorithm which trains a policy and then deploys the policy at the point of decision. Naturally, this is what we’ve done and now it can be used by anyone. This drops the barrier to use down to: “Do you have permissions? And do you have a reasonable idea of what a good feature is?”
A key foundational idea is Multiworld Testing: the capability to evaluate large numbers of policies mapping features to action in a manner exponentially more efficient than standard A/B testing. This is used pervasively in the Contextual Bandit literature and you can see it in action for the system we’ve made at Microsoft Research. The key design principles are:
- Contextual Bandits. Many people have tried to create online learning system that do not take into account the biasing effects of decisions. These fail near-universally. For example they might be very good at predicting what was shown (and hence clicked on) rather that what should be shown to generate the most interest.
- Data Lifecycle support. This system supports the entire process of data collection, joining, learning, and deployment. Doing this eliminates many stupid-but-killer bugs that I’ve seen in practice.
- Modularity. The system decomposes into pieces: exploration library, client library, online learner, join server, etc… because I’ve seen to many cases where the pieces are useful but the system is not.
- Reproducibility. Everything is logged in a fashion which makes online behavior offline reproducible. Consequently, the system is debuggable and hence improvable.
The system we’ve created is open source with system components in mwt-ds and the core learning algorithms in Vowpal Wabbit. If you use everything it enables a fully automatic causally sound learning loop for contextual control of a small number of actions. This is strongly scalable, for example a version of this is in use for personalized news on MSN. It can be either low-latency (with a client side library) or cross platform (with a JSON REST web interface). Advanced exploration algorithms are available to enable better exploration strategies than simple epsilon-greedy baselines. The system autodeploys into a chosen Azure account with a baseline cost of about $0.20/hour. The autodeployment takes a few minutes after which you can test or use the system as desired.
This system is open source and there are many ways for people to help if they are interested. For example, support for the client-side library in more languages, support of other learning algorithms & systems, better documentation, etc… are all obviously useful.
More and more Linked Data applications seem to emerge in the business world and software companies make it part of their business plan to integrate Graph Data in their data stories or in their features.
MarkLogic is opening a new wave to how enterprise databases should be used to push over the limits of closed, rigid structures to integrate more data. Neo4j explains how you can enrich existing data and follow new connections and leads for investigations of the Panama Papers.
No wonder the communities in different locations gather to share, exchange and network around topics like Linked Data. In London, a new conference is emerging exactly for this purpose: Connected Data London. The conference sets the stage for industry leaders and early adopters as well as researchers to present their use cases and stories. You can hear talks from multiple domains about how they put Linked Data to a good use: space exploration, financial crime, bioinformatics, publishing and more.
The conference will close with an interesting panel discussion about “How to build a Connected Data capability in your organization.” You can hear from the specialists how this task is approached. And immediately after acquiring the know-how you will need a easy-to-use and easy-to-integrate software to help with your Knowledge Model creation and maintenance as well as Text Mining and Concept Annotating.
In our dedicated slot we present how a Connected Data Application is born from a Knowledge Model and which are the steps to get there.
Connected Data London – London, 12th July, Holiday Inn Mayfair