Semantic Web Company https://semantic-web.com Semantic Web Company Wed, 10 Apr 2019 11:14:06 +0000 en-US hourly 1 CrowdSourcing of Large Knowledge Graphs https://semantic-web.com/2018/09/27/crowdsourcing-large-knowledge-graphs/ https://semantic-web.com/2018/09/27/crowdsourcing-large-knowledge-graphs/#respond Thu, 27 Sep 2018 10:14:26 +0000 https://semantic-web.com/?p=6196 Introduction Knowledge Graphs (KGs) are currently on the rise. In their latest Hype Cycle for Artificial Intelligence (2018), Gartner highlighted: “The rising role of content and context for delivering insights with AI technologies, as well as recent knowledge graph offerings for AI applications have pulled knowledge graphs to the surface.” We can roughly divide KGs ...

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Introduction

Knowledge Graphs (KGs) are currently on the rise. In their latest Hype Cycle for Artificial Intelligence (2018), Gartner highlighted:

“The rising role of content and context for delivering insights with AI technologies, as well as recent knowledge graph offerings for AI applications have pulled knowledge graphs to the surface.”

We can roughly divide KGs into 2 categories:

  1. Expert KGs such as Mesh, FIBO, etc.
  2. General-purpose KGs such as DBpedia, Wikidata, etc.

One of the differences between the two categories is that the expert KGs contain strict knowledge accepted in the expert community of their respective domain. On the contrary, for general purposes, KGs need a vast amount of common sense knowledge that is provided by non-expert users that is not necessarily strictly validated.

In this blog post, we introduce a novel crowdsourcing approach to extend general-purpose knowledge graphs based on a human-in-the-loop model. Using automatic reasoning mechanisms inspired by belief-revision, our approach incorporates views of different users, extracts the largest subgraph without contradictions and integrates this subgraph into the KG. Users can provide their updates in an intuitive way without requiring expertise in the knowledge already contained in the graph.

We have implemented and deployed the approach at PROFIT platform. PROFIT is a public platform for promoting financial awareness, hence everyone is welcome to contribute to the PROFIT financial knowledge graph that currently contains more than 11.000 concepts in 26 languages.

Related Work

One of the most important parts of the KGs is the hierarchy of classes of the domain of discourse. The most popular approach in the area of crowdsourcing (OWL) class hierarchies relies on exemplifying concepts (Exemplifying approach).

CrowdSourcing of Large Knowledge Graphs

Example. “B-52” is a “Shooter” and a “Mixed drink”, so is “Fireball”. “Sea Breeze” is a “Mixed drink”, but not a “Shooter”, hence “Shooter” is more specific than “Mixed drink”.

The advantage of the exemplifying approach is that any discussion about the particular structure of the resulting hierarchy can always be resolved by presenting new examples or discussing the old ones. It is adopted in several crowdsourcing frameworks, for example, CASCADE and CuriousCat.

However, this process is indirect in the sense that users operate on individuals, but get information about classes as the result. This aspect might be confusing for the non-expert users. Moreover, it might be easier to operate on classes directly rather than presenting a number of individuals. To overcome these issues we designed a process of “direct” crowdsourcing, where users operate on classes and obtain a hierarchy of classes as the outcome.

From Direct Beliefs to Knowledge

The challenges of crowdsourcing the taxonomies directly are the following:

  1. How to guarantee the absence of internal contradictions? For example, A <= B <= C <= A.
  2. How to take the different non-compliant inputs into account?
  3. How to integrate the crowdsourced knowledge?

We tackle these challenges in Belief Revision manner. In the following figure, we outline the method.

CrowdSourcing of Large Knowledge Graphs 1
We have 4 steps in our process:

  1. [Collect (box 1)] The user provides their beliefs / updates. The user chooses freely which entities he operates on: no prepared questions or other constraints.
  2. [Analyze and Provide Feedback (box 2)] The user’s update is analyzed against the existing knowledge graph. The users receive feedback about any internal inconsistencies inside their update in real time.
  3. [Vote (box 3)] The users vote on triples suggested by other users. Users either vote explicitly on a dedicated page or implicitly in case their update overlaps / confirms an update from a different user.
  4. [Integrate (box 4)] The threshold for accepting an update is computed dynamically when the update is submitted and depends on how well the update complies with the existing knowledge. When an update gets the numbers of upvotes equal to this threshold, the knowledge is integrated into the existing knowledge graph.

Conclusion

We introduce a novel crowdsourcing approach. The main features are:

  1. The users work directly with the hierarchy of classes, not any other entities.
  2. No need to have the information about the existing knowledge to provide new input. Hence, suitable for large KGs.
  3. The power of reasoning mechanisms of the semantic web is used to analyze user’s inputs, estimate its quality, avoid contradictions. The tool is also able to provide this feedback to educate the user.

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Knowledge Graphs – Connecting the Dots in an Increasingly Complex World https://semantic-web.com/2018/08/23/knowledge-graphs-connecting-dots-increasingly-complex-world/ Thu, 23 Aug 2018 07:31:09 +0000 https://semantic-web.com/?p=6165 Knowledge graphs are essential for any information architecture built upon semantics and AI. The Linked Data Life Cycle provides guideline for data governance within the semantic web framework.

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Bye Bye Silos!

Those who stay on their islands will fall back. This statement is valid on nearly any level of our increasingly complex society, ranging from whole cultural areas down to single individuals. The drawbacks of isolated departmental thinking becomes even more obvious when looking on the competencies and skill sets that become more and more relevant in our data- and knowledge-driven working environment: they should be interdisciplinary, multilingual, and should be based on a systemic / integrative attitude and on an agile mindset.

Knowledge Graphs - Connecting the Dots in an Increasingly Complex World 1

When looking at our data itself: it’s kept in silos, and therefore it is a highly time-consuming task for every knowledge worker to identify the right dots and information pieces, to connect them, to make sense of them, and finally to communicate and interpret them the right way. A shift to data-centric execution instead of document-based communication can be seen in many industries.

Knowledge Graphs (KG) have become increasingly important to support decision and process augmentation based on linked data. In this article we explore how enterprises can develop their own KGs along the whole Linked Data Life Cycle.

Graphs are on the Way

Some vendors of machine learning and AI technologies have aroused great hopes to fix the problem of disconnected and low-quality data automatically. It’s not perfectly true that machines are able to learn from any kind of data, especially from unstructured information, to come to a level that could substitute subject matter experts. The truth: algorithms like deep learning work only well when a lot of data (more than even large corporations usually have) of the same kind is available, and even then, only rather simple cognitive processes like ‘classification’ can be automated.

AI technologies are currently focused on solutions that automate processes. By that, other types of AI applications are well forgotten: decision and process augmentation, which refer to systems supporting knowledge workers by connecting some, but not all of the dots automatically. Applications like these are increasingly based on graph technologies, as they can map and support complex knowledge domains and their heterogeneous data structures in a more agile manner. In mid-2018, Gartner has identified Knowledge Graphs as new key technologies in their Hype Cycle for Artificial Intelligence and in their Hype Cycle for Emerging Technologies.

What is a Knowledge Graph?

It’s all about things, not strings: A Knowledge Graph represents a knowledge domain. It connects things of different types in a systematic way. Knowledge graphs encode knowledge arranged in a network of nodes and links rather than tables of rows and columns. By that, people and machines can benefit from a dynamically growing semantic network of facts about things and can use it for data integration, knowledge discovery, and in-depth analyses.

Graphs are all around: Facebook, Microsoft, Google, all of them operate their own graphs as part of their infrastructure. Google introduced in May 2012 its own version and interpretation of a Knowledge Graph, and since then the notion of ‘knowledge graph’ got more and more popular but linked to the Silicon Valley company. On the surface, information from the Knowledge Graph is used to augment search results and to enhance its AI when answering direct spoken questions in Google Assistant and Google Home voice queries. Behind the scenes and in return, Google uses its KG to improve its machine learning.

But Google’s KG (GKG) has a big disadvantage: it is quite limited how users and software agents can interact with it and its API returns only individual matching entities, rather than graphs of interconnected entities. Even Google itself recommends that, if you need the latter, data dumps from Wikidata should be used instead. But Wikidata is only one out of currently over 1,200 sources, which are highly structured and interconnected and most frequently available for download and reuse as standards-based knowledge bases. This ‘graph of graphs’ is also known as the ‘Semantic Web’. One might argue that this data still cannot easily be integrated in enterprise information systems due to a lack of quality control or missing license information etc.

Nevertheless, there are several reasons why organizations should explore this data management approach, to find out whether a ‘corporate semantic web’ reflecting their own specific knowledge domains would make sense or not.

Build your own Knowledge Graph

As we all know, many roads lead to Rome. Some of them are more exhaustive but more solid and sustainable, some of them are less explored but at the end more efficient, and in many cases the best way can only be found when already on the move.

In any case, the most frequently used approaches to develop knowledge graphs are: knowledge graphs can be curated like Cyc, edited by the crowd like Wikidata, extracted from large-scale, semi-structured knowledge bases such as Wikipedia, like DBpedia and YAGO, or they can be created by information extraction methods for unstructured or semi-structured information, which lead to knowledge graphs like Knowledge Vault.

The later approach sounds most promising since it better scales through a fully automated methodology, not only during the initial creation phase, but also for the continuous extension and improvement. A most fundamental problem with automatic knowledge extraction is the fact that and it cannot distinguish an unreliable source from an unreliable extractor. When learning from Google’s Knowledge Vault, we can assume the following:

  • Approaches primarily focused on statistics-based text extraction can be very noisy
  • Better results can be achieved when extractors combine information obtained via analysis of text, tabular data, page structure, and human annotations with prior knowledge derived from existing knowledge repositories
  • Extracted entity types and predicates should come from a fixed ontology
  • Knowledge graphs should separate facts about the world from their lexical to make it a structured repository of knowledge that is language independent
  • Supervised machine learning methods for fusing distinct information sources are most promising
  • SKOS as a W3C standard serves as a solid starting point to create an Enterprise KG

A systematic view on how Knowledge Graphs can be created, maintained, extended and used along the whole Linked Data Life Cycle has been provided by Semantic Web Company just recently in the course of the latest release of its PoolParty Semantic Suite:

Knowledge Graphs - Connecting the Dots in an Increasingly Complex World

Gartner states in its Hype Cycle for Artificial Intelligence, 2018: “The rising role of content and context for delivering insights with AI technologies, as well as recent knowledge graph offerings for AI applications have pulled knowledge graphs to the surface.”

Watch a Video Tutorial about Knowledge Graphs

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How Semantic Technologies Can Help Smart Cities Succeed https://semantic-web.com/2018/06/12/how-semantic-technologies-can-help-smart-cities-succeed/ https://semantic-web.com/2018/06/12/how-semantic-technologies-can-help-smart-cities-succeed/#respond Tue, 12 Jun 2018 07:45:46 +0000 https://semantic-web.com/?p=6117 With the UN predicting that more than 70 percent of the world’s population will live in urban areas by 2050, the development of sustainable smart cities is a rising need. Cities are now capable of collecting and analyzing enormous amounts of data to automate processes, improve service quality, and to make better decisions. This opens ...

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With the UN predicting that more than 70 percent of the world’s population will live in urban areas by 2050, the development of sustainable smart cities is a rising need. Cities are now capable of collecting and analyzing enormous amounts of data to automate processes, improve service quality, and to make better decisions. This opens up several possibilities and at the same time challenges. How to transform this data into action?

In this blog post, we will introduce you to the importance of digital infrastructure to public administrations at all levels. You will gain the semantic technologies’ perspective to solve current and upcoming challenges for data management. Two use cases from Australian public organizations will inspire you to think of semantics and artificial intelligence as a suitable solution to succeed as Smart City.

Register for the upcoming webinar on the 19th of June at 3 PM Central European Time and learn from the experts.

Digital Infrastructure as a Commodity

When discussing digitization, people commonly think about sensors, broadband networks, and data centers as being the infrastructure. Access to technology is a key parameter for the dynamic and municipality’s sustainable development. However, IT hardware makes only half of the plot of the story.

A community’s competence of providing access to comprehensive information repositories as a commodity is increasingly enabling enhancing societal living conditions. Data are driving lifestyle, business and administration, so information access is crucial for enterprises, small businesses, citizens, and the municipalities themselves.

To achieve this, context and sense-making of data have to be added to digital infrastructure. Reflecting this to traditional patterns, digital roads need junctions, road signs, and semaphores to function, just like their physical counterparts.

The existing use of social networks and the increasing use of intelligent agents will as well raise the question of how to co-exist in a hybrid world. Human-machine interfaces have to be developed that fulfill security, productivity, but also transparency, privacy and sociability needs of society.

The Semantic Web Approach to Deliver Actionable Knowledge

There are several challenges smart cities face, but from a data perspective, the main is how to break up data silos and how to connect data sources spread across agencies, departments, and third-party providers to create actionable knowledge.

Semantic web technologies solve these two challenges with a standards-based approach that has been widely implemented throughout the World Wide Web and in several enterprise use cases.

A semantic layer on top of your content provides endless possibilities to develop smart city applications within a robust information architecture. Integration harmonizes data and metadata as a Knowledge Graph, which makes content from disparate systems easily accessible. This facilitates nouvelle approaches to quality assurance and trust of information.

A semantic information architecture provides along quality, also context and meaning to your data based on controlled vocabularies. In fact, the context extracted from your content and the meaning obtained from words is what makes semantic applications smart and actionable.

Within this framework, it is reasonable to assume that in a smart city, most city functions could benefit from data that gives them the potential to be more efficient, responsive, and effective.

How to Connect People and Data using Semantics and Artificial Intelligence (AI)

The abundance of digital information together with today’s algorithms and powerful computers took AI to the next level. Artificial Intelligence can reveal new insights from your data, something that was not possible before.

Every AI strategy relies on data quality to provide accurate outputs. Semantic technologies support any AI initiative with a robust information architecture that increases data quality along the entire data lifecycle.

This synergetic relation is what we call Semantic AI. A semantic knowledge graph is used at the heart of a semantic enhanced AI architecture, which provides means for a more automated data quality management.

Semantic AI is the perfect companion when smart cities want to connect people and data. We will demonstrate this with one application scenario and two related use cases.

Smart governance for Smart Cities

Government information is subject to digitization thanks to the internet, the development of digital technology and modern lifestyle that demands more public services available online. Digital Government Data is driving economic growth, competitiveness, innovation, job creation and societal progress in general.

According to SmartCity.Press, e-governance and the involvement of the public in the decision-making process is the most important aspect of smart governance. The European Smart City Model rank and benchmark European medium-sized cities under these three aspects to define how well they do in smart governance:

  • Political awareness
  • Public and social services
  • Efficient and transparent administration

So far we know that making digital data useable for public and social services in an efficient and transparent manner improves city’s smart governance. But how to promote the re-use of digital public data to strengthen democracy and public welfare?

We will demonstrate how semantic technologies in general and, Semantic AI in particular, helped two Australian public organizations to provide better services based on their digital assets.

ANDS – Australian National Data Service

The research sector in Australia has over one hundred research organizations, government agencies, and cultural institutions. To success in the discovery, linking, understanding, and reuse of their research data, ANDS agreed on controlled vocabularies reflected in a semantic knowledge graph. Australian research organizations have now free access to the vocabularies, can create an own machine-readable vocabulary and reuse them inside their community. At the same time, ANDS created a web portal to find data for research that is publicly accessible online.

Healthdirect Australia

Healthdirect is a free service supported by the government of Australia. It provides free health advice based on content from over 140 specialized information providers. Built upon a publicly available thesaurus, they enhanced their semantic knowledge graph by extracting entities from all available content repositories. The final result is a web portal for end-users. Now citizens of a vast territory like Australia don’t need to drive far to get medical services. With the smart symptom checker, most often search queries receive a professional and trustworthy answer.

Conclusions

Only when we follow the approach of integration and the use of a semantic layer to glue together all the different types and models – thereby linking heterogeneous information and data from several sources to solve the data variety problem – are we able to develop interoperable and sustainable information models.

The presented initiatives and applications are based on a mature and forward-looking approach, based on open standards. Grown up to productivity stage, these examples still only give us a small glimpse o what will be possible in the future.

Such model can not only be used inside one city or municipality – but it should also be used to interlink and exchange data and information between cities as well as between provinces, regions, countries and societal digitalization transformation.

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Integrating your Drupal CMS with an Out-of-the-Box Semantic Technology Suite https://semantic-web.com/2018/04/05/poolparty-drupal-integration/ https://semantic-web.com/2018/04/05/poolparty-drupal-integration/#respond Thu, 05 Apr 2018 07:52:27 +0000 https://semantic-web.com/?p=5957 Drupal is one of the favourite enterprise content management systems. Especially government and non-governmental organizations embrace this open source platform to build advanced digital experiences. Over the last years, we have been developing several PoolParty semantic technology features and modules that integrate natively into Drupal. In this blog post, I introduce you to the semantic technology capabilities that ...

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Drupal is one of the favourite enterprise content management systems. Especially government and non-governmental organizations embrace this open source platform to build advanced digital experiences.

Over the last years, we have been developing several PoolParty semantic technology features and modules that integrate natively into Drupal. In this blog post, I introduce you to the semantic technology capabilities that will enhance your digital products and services. Learn all about the PoolParty features for Drupal and how to get started.

How to manage your content with semantic technologies

Essential for a successful website and important criteria to choose one CMS over another is their power to achieve a well-planned information architecture. As our Swedish partner stresses: “Proper information management should be the first step in every digitalization project.”

And this is precisely where semantic technologies can bring the most. They are a long-term and future-proof solution to improve both your website’s front- and back-end with powerful information and knowledge management capabilities.

In the front-end, you will be able to offer personalized and highly dynamic content to your users. By annotating your content semantically, you are connecting concepts and thus automatically adding context and meaning to it. In the back-end, you will support content managers with superior collaborative workflows and findability of all internal and external resources for further reuse.

success story handwritten on blackboard

Learn from PoolParty’s Customer Success Story

A Look Inside Semantic Metadata Management

Our customer is a think tank and knowledge hub for climate change technologies and also highly involved in building a worldwide community that benefits from synergies. They were looking for a solution to underpin several information management challenges:

  • Harvest relevant data and information around the world
  • Integrate data from different sources in several formats
  • Link such data along climate technology domain
  • Provide comprehensive information in easy to use manner
  • Develop useful services for all stakeholders on top
  • Act in a dynamic and multi-stakeholder environment

The solution was to annotate their content semantically and to improve search functionalities. Drupal taxonomy editor tool could be an option but is quite rudimentary and will not allow you to manage your metadata semantically in a proper way.

To achieve consistent semantic metadata, you need to develop a knowledge graph and embed it in your Drupal CMS.

A knowledge graph is a central knowledge management model worked throughout the whole organization (or a part of an organization) with the special participation of subject matter experts.

It is an agreement upon a common vocabulary that designates all relevant domains and topics inside the organization. Even more, it allows the organization to use knowledge in several languages, to define synonyms, relations, and hierarchies.

The following graph explains how our customer improved content annotation and consequently their search portal with PoolParty Semantic Suite.

Entity Extraction based on Knowledge Graphs

Entity Extraction based on Knowledge Graphs

First of all, our customer built a knowledge graph. You can create your knowledge graph with the PoolParty feature for Drupal named PoolParty Taxonomy Manager. The Taxonomy Manager is very user-friendly and can be used by several domain experts in a very intuitive way.

By having a knowledge graph in place, you can make use of the next future for Drupal. Supported by the PoolParty Extractor – which accesses the knowledge graph to annotate the content – PoolParty PowerTagging module for Drupal find the proper tags and add it automatically to your content.

PowerTagging considers the concepts you defined in your taxonomy to tag your content and goes way beyond by considering synonyms, frequency, and even context.

As a next step, your content professional can check the tags and adjust them if necessary. Furthermore, the knowledge graph will learn from your new content and suggest extensions for the thesaurus.

Finally, our customer applied PoolParty GraphSearch (a front-end application for search and analytics operations) to build their search portal that offers a faceted semantic search and a similarity algorithm.

For more in detail information, please check out this presentation.

How semantic technologies shape a better customer experience

Our customer annotated approx. 15.000 documents with PoolParty. As a result, they offer a semantic search portal that let users drill down facets defined in the knowledge graph such as content type, regions, and technology (look on the right-hand side of the image below).

Faceted search improves navigation friendliness and increases customer satisfaction, as they can find what they are looking for in few steps. The application suggests similar content (more like this) and offers an autocomplete search field (see the image below. By typing “clima” on the search field I get several related suggestions).

They also developed an internal collaboration environment to manage internal assistance requests and help the day to day work of the organization’s team.

Search Portal based on PoolParty Semantic Suite

Search Portal based on PoolParty Semantic Suite

Conclusions

Metadata management, also known as content annotation or just tagging, is crucial for creating added value from your content assets. If you are acquainted with content management systems, you will recognize the limitations of their tagging tools.

PoolParty Semantic Technology Suite widely supports out-of-the-box solutions for third-party systems like Drupal. You can take advantage of several application scenarios like semantic search, content recommendation, and smart glossary.

Semantic technologies have been in the market for enterprise solutions for the last 15 years and are a proven and standards-based technology. Companies benefit from semantic technologies in the fields of information management and governance, content management, semantic search, business intelligence, analytics, data integration, artificial intelligence and cognitive computing.

Are you ready to initiate your semantics journey for better customer experiences? If so, check out our demo application.

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Data wrangling with OpenRefine, PoolParty and SPARQL — Enabling multi-language thesaurus in PoolParty https://semantic-web.com/2018/03/16/data-wrangling-openrefine-poolparty-sparql-enabling-multi-language-thesaurus-poolparty/ https://semantic-web.com/2018/03/16/data-wrangling-openrefine-poolparty-sparql-enabling-multi-language-thesaurus-poolparty/#respond Fri, 16 Mar 2018 10:05:03 +0000 https://semantic-web.com/?p=5913 In our recent endeavor to import in PoolParty the Google Product taxonomy in different languages, we encountered some challenges that needed to be addressed. The first challenge was that the Google Product taxonomy is in Excel (XLS) format, and for each language there is a separate file. The second challenge is on how to align ...

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In our recent endeavor to import in PoolParty the Google Product taxonomy in different languages, we encountered some challenges that needed to be addressed. The first challenge was that the Google Product taxonomy is in Excel (XLS) format, and for each language there is a separate file. The second challenge is on how to align and merge the data rows coming from different languages, i.e., how can we be sure that two entities in different languages (respectively different files) mean the very same thing. We did this exercise for two languages (English and German), but inductively the same methodology can be applied for arbitrary number of languages (here are links for Italian and French to name a few).

Google Product Taxonomy

Google Product Taxonomy is a taxonomy used by Google in categorizing products, for the purpose of ensuring that a certain advertisement is shown with the right search results. The taxonomy in Excel format (see the figure below) is organized in columns, where each column based on the order corresponds to a depth level in the taxonomy, so the first column corresponds to the highest level in the taxonomy — in SKOS that corresponds to a concept scheme; the second column corresponds to the second-highest level in the taxonomy — in SKOS that is a top concept; and the other columns represent concepts and their respective narrower concepts and so it goes on. As PoolParty supports XLS import, in order to import it, we had to slightly adjust the Google Product Taxonomy XLS file. For this we used OpenRefine (formerly known as Google Refine), a powerful data wrangling tool.

Data wrangling with OpenRefine

The Excel data from Google Product Taxonomy was almost perfect to be imported in PoolParty, with a single caveat, the duplicates — as can be seen in the following figure — have to be removed. One can see that whenever a new level in the taxonomy is introduced using a new column data, the previous column data records are duplicated.

Data wrangling with OpenRefine -- Enabling multi-language thesaurus in PoolParty

Duplicates in Google Product Taxonomy

In PoolParty one needs to remove such recurring cell values, but keep only the top-most value for each different concept. Also, one has to rename the columns such that the first column is called “scheme”, the second “concept”, likewise the third “concept” and so on. Thus, it has to look like the following figure (note: OpenRefine internally renames columns with the same name in order to distinguish them visually, thus introduced concept2, concept3,…).

Data wrangling with OpenRefine -- Enabling multi-language thesaurus in PoolParty 1

Duplicates removed with OpenRefine

One can see that all duplicates under “Animals & Pet Supplies” are removed, same it is done with the column “Live Animals” and “Pet Supplies” and so on.

This transformation was done using OpenRefine’s “blank down” feature, by just going through each column and selecting Edit Cells -> Blank down. As result, all the duplicates are removed as in the previous figure, and data is ready to be imported in PoolParty. The same procedure is done for the German version as well.

PoolParty Import/Export

As mentioned earlier, PoolParty supports the feature to import XLS data. It also supports export to RDF feature that allows one to choose from a range of export options with the ultimate aim of providing you a means to avoid data lock-in. Export to XLS is also possible. Once the data are imported they are internally stored in RDF format. Using RDF format is much easier to do data integration, where each resource is assigned an unique URI. For both English and German the corresponding XLS files are imported to PoolParty. Import Assistant in PoolParty gives us a green light, suggesting that all the SKOS constraints are satisfied. Once we have import them, for each language, we export data as RDF, so that we can do the merging.

Import RDF data to a triplestore

Both RDF data belonging to the English and German version of the Google Product Taxonomy, were imported to a dedicated triple store. Now, the question is how to align the RDF data consisting of statements in different languages? One, for instance, could think of doing a “string matching” by using machine translation beforehand for non-English languages, but as we know the precision is not always 100% correct. Luckily for us, in each of the XLS files, there is a “notation” column, such that each row has a corresponding “identifier”. This is translated to skos:notation when imported to PoolParty. Now we are sure which row in English corresponds to the German based on the notation, this is because they coincide.

Data wrangling with OpenRefine, PoolParty and SPARQL -- Enabling multi-language thesaurus in PoolParty 1

Notation columns for both English and German, serve us as identifiers.

In the case of RDF, we align the data on skos:notation identifiers, and for this we use a SPARQL query.

Data merging using SPARQL

Merging RDF data based on skos:notation is done relatively easy, using the following SPARQL query:

PREFIX skos: <http://www.w3.org/2004/02/skos/core#>

insert { graph <https://nextrelease.poolparty.biz/GoogleProductTaxonomy11-2017/thesaurus> {?s2 skos:prefLabel ?label1 . }}
where {
?s1 skos:notation ?o1 . ?s1 skos:prefLabel ?label1 .
?s2 skos:notation ?o2 . ?s2 skos:prefLabel ?label2 .
FILTER (?o1 = ?o2 && !sameTerm(?s1, ?s2))
}

The query checks for all different subjects that have the same notation, and for each result, inserts the corresponding label. Now, in the graph we have concepts that have skos:prefLabel for both English and German.

Import final (graph) data to PoolParty

The resulting RDF graph data was imported to PoolParty, where now for each concept we have both labels in English and German. In the following figure, one can see this under Preferred Label. Note that in SKOS one can have multiple preferred labels, as long as they are in different languages.

Data wrangling with OpenRefine -- Enabling multi-language thesaurus in PoolParty 2

Google Product Taxonomy, both in English and German imported in PoolParty.

Conclusion

We could have done this exercise without using OpenRefine, but then we would have done a manual work on the 5426 rows, going through each column and removing the duplicates. For large files, the approach using OpenRefine is indispensable and too many times saved us a lot of precious time. Same can be said for SPARQL, we automated the merge of the data by using a simple SPARQL query instead of doing that work manually. As conclusion, we can claim that data wrangling with OpenRefine, PoolParty and SPARQL proved to be a huge time saver, just imagine if you have to do this exercise for n different languages.

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Streamline your Software and Data Engineering Process with Semantic Technologies https://semantic-web.com/2018/01/29/streamline-software-data-engineering-process-semantic-technologies/ https://semantic-web.com/2018/01/29/streamline-software-data-engineering-process-semantic-technologies/#respond Mon, 29 Jan 2018 11:49:43 +0000 https://semantic-web.com/?p=5732 With technology being number one concern for most businesses, there is an increasing need for better lifecycle management of software and data engineering processes. Quality management is a critical component, especially nowadays with the explosion of big data sources and existing tools that tend to break at scale. In the case of semantic knowledge bases, ...

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With technology being number one concern for most businesses, there is an increasing need for better lifecycle management of software and data engineering processes. Quality management is a critical component, especially nowadays with the explosion of big data sources and existing tools that tend to break at scale.

In the case of semantic knowledge bases, data quality comes hand in hand with a consistent knowledge graph. Managing data inconsistencies is a data engineering task supported by software solutions.

With this in mind, we wanted to align data management and software development in our flagship product PoolParty semantic suite. Therefore, we pursue a holistic approach from where both PoolParty users and our software developers will benefit from new features to facilitate their workflow and increase the quality of outcomes.

As a result, we developed the Smart Development Assistant (SDA) that supports software and data engineering processes in PoolParty in a very intuitive way. This process was part of the ALIGNED research project, a collaboration between the Semantic Web Company and prestigious universities, information companies, and academic curators.

At the end of the project, we were able to take our software to the next level by increasing the productivity of our development team and hence improving client satisfaction.

In this post, I am going to break down how we developed the Smart Development Assistant and how semantic technologies supported us in our use case.

What was the challenge?

Semantic technologies are largely based on the Resource Description Framework (RDF), which allows connecting structured and unstructured data from disparate systems.

Semantic web applications like PoolParty Semantic Suite enable the import of RDF based vocabularies into smart applications like semantic search and content tagging.

As opposed to relational databases, semantic web databases have no fixed schema. Thus, there is no way to guarantee consistency of the imported datasets.

When importing an RDF dataset into PoolParty we have to ensure that it satisfies constraints for the application to work correctly. These restrictions correspond to our data model which is based on SKOS (a simple knowledge model for expressing controlled vocabularies).

For example, a typical issue is when the preferred label (a lexical label that represents the meaning of a concept) and the alternative label (acronyms, abbreviations, spelling variants, etc.) of a concept are the same. Without a quality check, this inconsistency will remain and affect the quality of outputs.

The Smart Development Assistant does an RDF Validation of imported data and reports inconsistencies.

Example of automatically detected data inconsistencies in PoolParty

What is the solution?

To overcome challenges as mentioned above and mitigate subsequent problems, we developed the Smart Development Assistant built on top of semantic technologies.

We defined a set of 16 constraints based on SKOS, SHACL shapes, and SPARQL queries which allow us to identify data inconsistencies caused by application errors or introduced by a user while importing data into PoolParty.

The Smart Development Assistant automatically executes an RDF validation and offers repair strategies for data curation that the user can immediately implement. After solving detected violations, the user can store the new data into the PoolParty project.

Automatic offering of repair strategies in PoolParty

Automatic offering of repair strategies in PoolParty

By ensuring data consistency, we can avoid many problems; still, some errors will persist. In such a case, the user depends on the software development team to solve bug issues.

With the Smart Development Assistant, we also allow the user to create a bug ticket automatically without leaving the semantic platform.

The development team will directly receive a notification to solve the ticket. This way, we are aligning data and software engineering processes with a simple tool that improves the user experience with PoolParty and helps developers to work more efficiently.

Integrated Issue Reporting in PoolParty

Integrated Issue Reporting in PoolParty

We also integrated the Smart Development Assistant with PoolParty GraphSearch. GraphSearch is a front-end application with search, recommendation and analytics functionalities.

The development team can analyze the incoming tickets in a user-friendly interface. They can search by issue type or assignee, or a mixture of both, just to name an example.

A similarity computation detects duplicate bugs and finds associated stories and requirements. The team can also access statistical charts and histograms for analysis purposes.

For more details watch the video.

Which PoolParty components did we use for developing the Smart Development Assistant?

I will now introduce you to the PoolParty semantic suite capabilities and the technology components we used for the Smart Development Assistant.

PoolParty Thesaurus Server

The PoolParty Thesaurus Server supports web-based taxonomy and ontology management, which are the cornerstone of any application based on semantic technologies. PoolParty Thesaurus Server allows users to create and edit controlled vocabularies based on the SKOS scheme and organizes it in a tree-like structure.

To develop the Smart Development Assistant, we imported the ALIGNED generic metamodel (an outcome of the research project we participated in) to the thesaurus server and extended it to fit our use case, resulting in the PoolParty design intent ontology. With this ontology, we were able to integrate datasets generated through requirements specification and the issues raised during their implementation.

PoolParty GraphSearch

With PoolParty GraphSearch organizations can connect content and data repositories to improve search over a variety of business objects and analyze the data on a granular level. You can enhance GraphSearch with recommendation algorithms providing similarity-based recommendations or a matchmaking algorithm.

Our development team is using the GraphSearch application of the Smart Development Assistant on a daily basis. It assists them in managing software development artifacts: detecting duplicate bugs, finding stories associated with a specific issue, visualizing statistics on time to issue resolution and keeping track on the number of bugs over the time.

Read the white paper for more insights on PoolParty GraphSearch.

PoolParty Unified Views

PoolParty UnifiedViews provides a framework to develop, execute, monitor, debug, schedule, and share RDF data processing tasks. Data processing tasks are modeled as pipelines via a graphical interface and can consist of several Data Processing Units (DPUs).

We created a UnifiedViews pipeline for harvesting data from two data repositories we use for software development (Confluence and Jira in our case) and transform it into RDF based on the PoolParty design intent ontology. Afterwards, we annotated the data using the PoolParty Knowledge Graph which helped on calculating similarity scores on issues and requirements.

Extraction, Annotation and Loading Pipeline in UnfiedViews for the Smart Development Assistant

Extraction, Annotation and Loading Pipeline in UnifiedViews for the Smart Development Assistant

Conclusion

Semantic technologies offer an array of solutions for entity-centric information architecture.

By considering features our partners and customers requested in several times, we took our product to the next level and increased the productivity of our development team.

Using PoolParty ontology management tool and integrated components such as GraphSearch and UnifiedViews, we delivered a cutting-edge solution for typical data and software engineering challenges in PoolParty Semantic Suite.

Our research and development team is looking forward to furthering developments supported by research funds and new partners.

Are you willing to partner with us to develop smart applications based on semantic technologies?

If so, don’t hesitate to contact us.

About the ALIGNED Project

Since 2004 the Semantic Web Company invests in research and innovation projects that have a direct impact on the development of our flagship product PoolParty Semantic Suite. This year we successfully finished the ALIGNED Project after three years of collaboration between industry and academia and €4 million of funding from the European Commission’s Horizon 2020 program. Several partners joined forces to develop new ways to build and maintain IT systems that use big data on the web.

The Smart Development Assistant was the PoolParty use case within the ALIGNED project. But there were many other use cases in a wide range of areas that showed how the latest semantic technologies could help create smarter legal information systems, provide better management of health data and help construct high-quality archaeological and historical datasets. In all of these areas people are struggling to harness the available data, and in all of these areas, ALIGNED’s semantic and model-driven technologies were able to help.

 

 

 

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Information Management Survey 2018: Knowledge Engineering at the Core of Cognitive Applications https://semantic-web.com/2018/01/16/information-management-survey-2018-knowledge-engineering-core-cognitive-applications/ https://semantic-web.com/2018/01/16/information-management-survey-2018-knowledge-engineering-core-cognitive-applications/#respond Tue, 16 Jan 2018 15:56:59 +0000 https://semantic-web.com/?p=5663 The Semantic Web Company, Mekon and Enterprise Knowledge conducted an Information Management Survey for practitioners that provides new insights into the current status of this highly diverse technology field. 187 data and content professionals participated worldwide. Let us share the three key findings in this article and encourage you to download the full report.  Information ...

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The Semantic Web Company, Mekon and Enterprise Knowledge conducted an Information Management Survey for practitioners that provides new insights into the current status of this highly diverse technology field. 187 data and content professionals participated worldwide. Let us share the three key findings in this article and encourage you to download the full report

Information Management is a very heterogeneous and interdisciplinary domain 

Knowledge engineering is an integral part of the development process of cognitive applications. 67% of information professionals agree that the demand for their expertise is rising. At the same time, every second knowledge engineer thinks that organizations don’t understand their role and potential contribution. A better alignment of the business and technology side is needed to fulfil the high expectations in regards to smart applications.

Digital maturity of an organization depends on the availability of enterprise knowledge engineering solutions 

The survey report also highlights how information management is embedded in organizations and which tools are used to build and maintain cognitive applications. The maturity degree of an organization is definitely reflected by its usage of professional semantic metadata management solutions and the inclusion of complementary technologies.

Importance of technology trends depend on professional role 

Depending on the professional role, technologies are of different relevance. Machine Learning is for all data and content professionals at the forefront of technological innovations. However, it needs to be differentiated who is working with it and who has an opinion about it. The Top Management declares Cognitive Computing as leading technology trend. For them, it’s not about a sub-set of technologies, but a robust platform that enables companies to build cognitive applications.

Download Information Management Survey 2018 

 

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PoolParty GraphSearch brings knowledge search to a new level https://semantic-web.com/2018/01/08/poolparty-graphsearch-brings-knowledge-search-new-level/ https://semantic-web.com/2018/01/08/poolparty-graphsearch-brings-knowledge-search-new-level/#respond Mon, 08 Jan 2018 08:24:15 +0000 https://semantic-web.com/?p=5604 There is a strong connection between information storage and retrieval, namely how information storage is implemented under the hood directly impacts the retrieval or search. The knowledge management techniques we adopt have an immense impact on our daily productivity. For that reason, a lot of thought is given by us when we store information, asking ...

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There is a strong connection between information storage and retrieval, namely how information storage is implemented under the hood directly impacts the retrieval or search.

The knowledge management techniques we adopt have an immense impact on our daily productivity. For that reason, a lot of thought is given by us when we store information, asking questions such as which tools we should use for the very same purpose.

In this blog post, we will see the reasons behind why PoolParty GraphSearch is a very powerful semantic search tool, but before dwelling into that we go back to an old-school method of knowledge management called “Zettelkasten”.

Zettelkasten

Niklas Luhmann developed his own system of knowledge management back in the time called “Zettelkasten” (“slip notes” in English). His method allowed him to be highly productive when working with literature as a scientist. The system is mostly known to librarians, but can also be used as a standalone system for storing information, ideas, book excerpts and so on.

Using this system, as shown in the following figure, in principle one can store handwritten slip notes in “index card” format by giving an Identifier, body text describing the slip note, as well as related slip notes by referencing them with their corresponding Identifier. After that, one decides in which category she should put the slip note. In this way, one can look over all slip notes of Category A, and while looking into that information she can jump to other slip notes of Category B or C. This is a powerful way to search/browse for information and how one can come up with relevant slip notes by just starting somewhere.

PoolParty GraphSearch brings knowledge search to a new level 3

Zettelkasten technique

Imagine you have an index card categorized as “Da Vinci”, “Renaissance” and you have the <body text>, plus indices (pointers) to related index cards such as “related events”, or “museums”. By looking at categories “Da Vinci”, “Renaissance” this system is able to retrieve you the “related events” and bring you to a new piece of information that you never thought of previously. From the perspective of search — in this system one can either “branch” further via categories, or “chase” using pointers to related index cards. By combining “branch” and “chase” one can find information that on first look appeared disconnected, but turned out to be a very relevant information.

Graph data in RDF

From the previous figure one can see that the structure of the information in Zettelkasten can be visualized as a graph. Thus, one starts with a node and then follows edges until the search criteria according to her is satisfied, or alternatively she decides to stop.

The most prominent way of storing the graph data is in RDF, while querying them using SPARQL. The graph data is stored in a triple format consisted of subject, predicate (or property) and object; each one of them having an URI identifier. Having URIs in place, using RDF it is much easier to integrate and link data from different sources. On other hand, SPARQL is very expressive language – as expressive as SQL – and one can use it to query and traverse data in the graph. While SPARQL is a great query and manipulation language for graphs, is not that user-friendly to a broad of users. For that reason, different Semantic Web applications are created with that in mind so that they generate SPARQL queries under the hood, initially having an original query in a different format such as natural language or facets.

Faceted search is widely used today (for instance, see Amazon) as a means to drill-down search criteria by allowing users to select the relevant boxes as search criteria in a very intuitive way. Nowadays, Semantic Web applications mimic the same functionality on top of RDF graph data, where SPARQL queries are generated on the background while users select different combination of facets.

PoolParty GraphSearch (PPGS)

PoolParty GraphSearch is a semantic search application, an analytical tool that allows users to search for information using facets.  Search is done via facets of concepts and properties, derived from a PoolParty thesaurus, that drill-down information satisfying the criteria.

In PoolParty GraphSearch, such information is a-priori annotated using PoolParty Extractor using concepts and properties from the designated thesaurus. The workflow of annotating is typically done using UnifiedViews DPUs (Data Processing Unit), and in the end the triples are stored in a dedicated triple store; where then are fed to GraphSearch.

In PPGS one not only can find information based on facets, but also based on content similarity, and the system allows to plug in any kind of other similarity algorithm and thus build any custom recommender engine (for instance see Wine and Cheese recommender). On top of that, in PPGS one can also visualize different statistics based on actual data using pie charts or histograms.

PoolParty GraphSearch brings knowledge search to a new level 4

Wine and Cheese recommender implemented in PoolParty GraphSearch

Connection between PPGS and Zettelkasten

As mentioned earlier the Zettelkasten method can be conceptualized as a graph, and this allows us to make a direct connection with PPGS.

Zettelkasten technique can be implemented in PoolParty GraphSearch, where:

  1. categorized search is done via facets, i.e., dereferencable concepts <http://dbpedia.org/page/Leonardo_da_Vinci>, <http://dbpedia.org/page/Category:Renaissance>.
  2. regarding related index cards, instead of pointers we have typed links, i.e. dereferencable properties such as <http://dbpedia.org/ontology/museum>
  3. for related index cards, we can also use the similarity feature where we get similarity results based on the <content>.

Clearly, in PoolParty GraphSearch 2) and 3) bring Zettelekasten technique to a new level, while 1) makes the approach in general more robust, i.e., representing data using “things” and not “strings”.

Conclusion

Lately, in order to honour Niklas Luhmann, there was an initiative to convert all his Zettelkasten in XML (link only in German). If we would have such Zettelkasten in XML, by converting them to RDF (for instance using UnifiedViews), then ultimately we would see the fundamental advantage of PoolParty GraphSearch.

 

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Why Semantic Technologies Are Steering Cognitive Applications https://semantic-web.com/2017/09/19/semantic-technologies-steering-cognitive-applications/ https://semantic-web.com/2017/09/19/semantic-technologies-steering-cognitive-applications/#respond Tue, 19 Sep 2017 12:03:17 +0000 https://semantic-web.com/?p=5494 Despite the rising popularity of data-driven technologies, studies show that nowadays less than 10% of data is used effectively by organizations. Over 40% of companies are struggling to find talent to implement Big Data solutions. However, by 2020 company’s success will largely depend on their ability to create digitally enhanced customer experience solutions. Cognitive applications ...

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Despite the rising popularity of data-driven technologies, studies show that nowadays less than 10% of data is used effectively by organizations. Over 40% of companies are struggling to find talent to implement Big Data solutions. However, by 2020 company’s success will largely depend on their ability to create digitally enhanced customer experience solutions. Cognitive applications are revolutionizing the faculty of enterprises to grow and compete. Solutions like chatbots for customer advice and assistance in retail, knowledge discovery tools for medical diagnostics and treatment, predictive analytics solutions for industrial equipment or recommendation systems for fraud detection and prevention in financial services are becoming increasingly more productive. Nevertheless, cognitive applications have challenges that semantic technologies are helping to overcome. Working with semantic technologies is a qualitatively driven way to enhance smart applications.  

The limits of cognitive applications

Cognitive applications are being applied to a wide variety of uses and across various industries. Based on statistical and rule-based methods, they are excellent to process a large volume of information. But many companies are battling with the imprecise results this technology delivers. Complex algorithms to simulate how the human brain works lead data scientists to a bottleneck for taking cognitive computing to the next level.

How semantic technologies come into play

Semantic technologies enable companies to uncover untapped business potential and to point the way to efficiency gains. Semantic technologies have knowledge graphs at the core of their solution approach. Knowledge graphs organize information using interrelated concepts like a human brain would do. In other words, resemble the human brain’s architecture allowing domain experts and business users to perform data modeling.

How does it work

Cognitive systems can be built on statistical models or knowledge bases – or on a hybrid of these. Semantic Web Company recommends the last option.  The use of knowledge graphs together with statistical models provide the end user with the option to independently modify the functioning of smart applications. Therefore, it comes more natural and user-friendly for human users and at the same time allows dynamic data management with precise results that naturally grow and learn over the time.  

If you want to find out more download a free IDC White Paper about how to build cognitive computing applications with semantic technologies at www.poolparty.ai

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A Standard to build Knowledge Graphs: 12 Facts about SKOS https://semantic-web.com/2017/08/21/standard-build-knowledge-graphs-12-facts-skos/ https://semantic-web.com/2017/08/21/standard-build-knowledge-graphs-12-facts-skos/#respond Mon, 21 Aug 2017 10:17:51 +0000 https://semantic-web.com/?p=5412 These days, many organisations have begun to develop their own knowledge graphs. One reason might be to build a solid basis for various machine learning and cognitive computing efforts. For many of those, it remains still unclear where to start. SKOS offers a simple way to start and opens many doors to extend a knowledge ...

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These days, many organisations have begun to develop their own knowledge graphs. One reason might be to build a solid basis for various machine learning and cognitive computing efforts. For many of those, it remains still unclear where to start. SKOS offers a simple way to start and opens many doors to extend a knowledge graph over time.

A Standard to build Knowledge Graphs: 12 Facts about SKOS

1) Standardised: The Simple Knowledge Organization System (SKOS) is a standards-based ontology, which was published by the World Wide Web Consortium (W3C) in 2009. Learn more: SKOS – A Guide for Information Professionals

2) Future-proof: SKOS is part of a larger set of open standards, which is also known as the Semantic Web. The usage of open standards for data and knowledge models eliminates proprietary vendor lock-inLearn more: Semantic Web at W3C

3) Wide range of applications: SKOS is primarily used to build and to make controlled vocabularies like taxonomies, thesauri or business vocabularies available as a service. This builds the basis for a wide range of applications, starting from semantic search and text mining, ranging to data integration and data analytics. Learn more:Semantic Applications

4) Graph-based: SKOS concepts can be related and linked to other concepts and instances of ontologies. By these means, SKOS constitutes the nucleus of a decentralised enterprise-wide knowledge graph. Learn more: Introducing a Graph-based Semantic Layer

5) Taxonomy/ontology overlay: Without violating restrictions, any node in a knowledge graph can be part of the taxonomical (SKOS) and ontological structure (e.g. FIBO, FOAF, or schema.org) at the same timeLearn more: Anatomy of an Ontology

6) Cost-efficient and incremental approach: Any SKOS-based taxonomy, thesaurus, or controlled vocabulary can be extended and enriched by additional ontologies step-by-step, thus various views on the same node can be created when needed. SKOS-based vocabularies can be used as a starting point for a cost-efficient development of more extensive semantic knowledge graphs. Learn more: Extending Taxonomies With Ontologies

7) Actionable content: SKOS models, no matter if linked to more expressive ontologies or not, can be queried with SPARQL, or validated by the use of SHACL, a recently issued standard for describing and validating RDF graphs. By that means, knowledge models become actionable and can help to find answers in unstructured content, trigger alerts or to make better decisions. Learn more: Structured Content Meets Taxonomy

8) Things, not strings: With SKOS, solely term-based taxonomies get obsolete. Taxonomical knowledge becomes accessible, its semantics becomes explicit. Any business object represented in a knowledge graph receives a unique address and can then easily be integrated in a bot, service, or application. Terms and strings are no longer used to build ambiguous metadata, instead, a semantic layer on top of all content and data assets works like a multi-dimensional index. Learn more: Different shades of metadata

9) Widely adopted: Hundreds of SKOS vocabularies are available on the web. Large international bodies like the EU, UN, or The World Bank make use of SKOS to make their knowledge available to external and internal stakeholders as well. Many Fortune 500 companies have already adopted SKOS for internal use. Learn more: The Basel Register of Thesauri, Ontologies & Classifications (BARTOC)

10) Mandatory: SKOS, RDF and other standards can, for instance, be required in EU public procurementLearn more: Commission Implementing Decision (EU) 2017/1358

11) No black box: Organisations seeking for strategies to keep control over their cognitive applications and algorithms need to involve their own subject matter experts. SKOS is relatively easy to learn and can produce massive input to make machine learning tasks more precise. Learn more: IDC Paper – How Semantic Technologies Steer Cognitive Applications

12) Easy to master: Learn more about SKOS and the Semantic Web in less than half a day at PoolParty Academy

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