A knowledge graph is also known as a semantic network and represents a network of real-world entities. In other words represent “reality” as a combination of objects, events, situations, or concepts taking into account the relationship between them. This information is usually stored in a graph database and visualized as a graph structure, prompting the term knowledge “graph.”. The figure below can give you a visual idea of how these entities interlink among each other.

Intuitively, we can note that knowledge graphs take advantages of triples: subject-verb-qualifier in order to infer relationships among the data.

The knowledge graphs is at the core of the semantic web vision and standards like RDF and query languages like SPARQL are maintained by the w3c. A set of pointers for more information is available in this paper that I wrote specifically for interoperability in industry 4.0 and internet of things. However sections 2.3. (of the paper) give an overview of the semantic stack.

Existing tools and technologies that use knowledge graph

A core set of technologies that are currently in productions and used companies like Google, Facebook, Apple includes the following:

• Wikidata: Wikidata is a collaboratively edited multilingual knowledge graph hosted by the Wikimedia Foundation. It is a free and open knowledge base that can be read and edited by both humans and machines.
• schema.org is an organization that publishes documentations and guidelines to using structured data mark-up on web-pages. Its main objective is to standardize HTML tags to be used by webmasters for creating rich results about a certain topic of interest.
• Google Knowledge Graph is a knowledge base used by Google and its services to enhance its search engine’s results with information gathered from a variety of sources.

These technologies are strictly focusing on the data instead and the video below share a practical approach that focus on the data and how to combine them. Note that it does not consider any AI approach but focus on how to combine existing available data and their relationships.

However, it contains very nice examples and give an intuitive idea on how Wikidata, Schema.org and Knowledge graph works.

Research on Knowledge Graph and Artificial Intelligence

Now that we have an idea of what are the basic techniques for representing information we need to understand how we can use machine learning techniques for enriching these information and the expressiveness of the models. What follows is a set of seminars that discuss the knowledge graph from a more theoretical point of view and consider open research questions. This is the first part:

Below you can find the second part. It also contains very nice real life case studies. Including how Siri works!

If you are curious and would like to know more about the topic I encourage you in browsing the YouTube channel for more videos.

On #Knowledge Graph and #ArtificialIntelligence : how to combine data and models for better information. #BigData #DataScience
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Faced with an ever-increasing information flood in a complex and interconnected world, individual knowledge workers are in dire need for support in structuring their personal information space and maintaining fruitful communication and exchange within social networks across organizational boundaries. NEPOMUK develops technical support and stipulates and mobilizes community efforts to realize the Social Semantic Desktop.

This paradigm comprises a set of technical and methodological solutions for:

• supporting the knowledge life cycle via personal knowledge articulation support, embedding in personal work processes, and sharing, exchange and alignment across social networks
• cross-media and cross-application linking and browsing of information items based on standard semantic web data structures, together with un-intrusive metadata support
• knowledge communication within social networks and distributed search and storage to build, maintain, and employ inter-workspace relations in large scale distributed scenarios.

Links:

• Nepomuk in Wikipedia
• EU website of Nepomuk

Some relevant publications includes:

• This is work in progress, just look at the full list of publication here

If this is not what you are looking for, at this link you can see the full list of projects

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Abstract: Industry 4.0 demands a dynamic optimization of production lines. They are formed by sets of heterogeneous devices that cooperate towards a shared goal. The Internet of Things can serve as a technology enabler for implementing such a vision. Nevertheless, the domain is struggling in finding a shared understanding of the concepts for describing a device. This aspect plays a fundamental role in enabling an “intelligent interoperability” among sensor and actuators that will constitute a dynamic Industry 4.0 production line. In this paper, we summarize the efforts of academics and practitioners toward describing devices in order to enable dynamic reconfiguration by machines or humans. We also propose a set of concepts for describing devices, and we analyze how present initiatives are covering these aspects

Recommendations for advancing the state-of-the-art of the (intelligent) interoperability of IoT towards solving use cases from Industry 4.0 include:

• IoT can serve as enabler technology for Industry 4.0 especially if proper “intelligent interoperability” is achieved.
• Consider a deeper collaboration among practitioners and academics for developing a shared set of concepts.
• Consider structured knowledge around devices that does not only focus on sensors and includes actuators, as well.
• Schema.org may be a promising technology that could overcome the intrinsic complexity of the semantic web; however, in the case of IoT, it is still in its initial phases.

Full article available at the following link:

DOI: https://doi.org/10.3390/fi11020036

Your help in sharing the idea is very welcome!

#Interoperability of the Time of #Industry40 and the #InternetOfThings . Consider 'intelligent interoperability' as the next step forward
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Cite this paper as:

Lelli F. Interoperability of the Time of Industry 4.0 and the Internet of Things. Future Internet. 2019; 11(2):36.

• Full list of Articles
• Full list of Journal Publications

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