Six Core Aspects of Semantic AI

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  1. Hybrid approach: Semantic AI is the combination of methods derived from symbolic AI and statistical AI. Virtuously playing the AI piano means that for a given use case various stakeholders, not only data scientists, but also process owners or subject matter experts, choose from available methods and tools, and collaboratively develop workflows that are most likely a good fit to tackle the underlying problem. For example, one can combine entity extraction based on machine learning with text mining methods based on semantic knowledge graphs and related reasoning capabilities to achieve the optimal results.
  2. Data Quality: Semantically enriched data serves as a basis for better data quality and provides more options for feature extraction. This results in higher precision of prediction & classification calculated by machine learning algorithms. Example: PoolParty Semantic Classifier
  3. Data as a Service: Linked data based on W3C Semantic Web standards can serve as an enterprise-wide data platform and helps to provide training data for machine learning in a more cost-efficient way. Instead of generating data sets per application or use case, high-quality data can be extracted from a knowledge graph or semantic data lake. Through this standards-based approach, also internal data and external data can be linked with little effort and can be used as a rich data set for any machine learning task.
    See also: The Knowledge Graph as the Default Data Model for Machine Learning
  4. Structured data meets text: Most machine learning algorithms work well either with text or with structured data, but those two types of data are rarely combined to serve as a whole. Semantic data models bridge the gaps between most used data formats such as XML, relational data, CSV or also unstructured text when using NLP and text mining methods. This allows us to link data across heterogeneous data sources to provide data objects as training data sets which are composed of information from structured data and text at the same time.
    See also: Leveraging Taxonomy Management with Machine Learning
  5. No black-box: In sharp contrast to AI technologies that ‘work like magic’, where only a few experts really understand the underlying techniques, Semantic AI seeks to provide an infrastructure to overcome information asymmetries between the developers of AI systems and other stakeholders, including consumers and policymakers. Semantic AI ultimately leads to AI governance that works on three layers: technically, ethically, and on the social and legal layer.
    See also: A Layered Model for AI Governance and Explainable Artificial Intelligence
  6. Towards self optimizing machines: Semantic AI is the next-generation Artificial Intelligence. Machine learning can help to extend knowledge graphs (e.g., through ‘corpus-based ontology learning’ or through graph mapping based on ‘spreading activation’) and, in return, knowledge graphs can help to improve ML algorithms (e.g., through ‘distant supervision’). This integrated approach ultimately leads to systems that work like self optimizing machines after an initial setup phase, while being transparent to the underlying knowledge models. Graph Convolutional Networks (in progress) promise new insights.
    See also: How do we capture structure in relational data? and Snorkel: A System for Fast Training Data Creation
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Recap SEMANTiCS 2017 (Amsterdam)

This was SEMANTICS 2017: Reaching out to new industries, vendors, and establishing good relations to neighbouring communities. These were the reasons why to bring SEMANTiCS conference series to Amsterdam.

The feedback and the pure figures show the success of that effort. Exactly 370 attendees joined the SEMANTiCS Conference in Amsterdam. This growth by 33% shows that SEMANTiCS meets a growing demand for such gatherings in industry and research.

The 13th edition of SEMANTiCS combined researchers, professionals, and practitioners from across the globe – from a total of 28 different countries. Most visitors and participants came from the Netherlands, Germany, Austria, UK and Belgium.

https://2017.semantics.cc/was-semantics-2017

Linked Data – The Next 5 Years: From Hype to Action

Linked Data and the Semantic Web have been around for quite a while and have been hyped again and again. In the meantime, a large number of enterprises and even whole industries have adopted semantic web technologies for several purposes (for example, visit Allotrope Foundation). “Gartner’s Hype Cycle 2015 for Advanced Analytics and Data Science” has put Linked Data into the trough of disillusionment, which is another clear indicator to be ready for takeoff.

Gartner 2015 Hype Cycle for Advanced Analytics and Data Science.png

The pace of semantic web technology adoption may vary from industry to industry, but in average it has increased even more than expected. Just in 2012, Gartner has predicted that the Semantic Web won’t reach the plateau of productivity within the next 10 years, only three years later it seems like it will be there in 5 to 10 years.

Gartner 2012 Hype Cycle for Big Data.png

Linked Data Hype or not, it has entered the adoption phase. In the next 5 years we finally can see to which degree enterprises will use semantic web technologies for data analytics, data integration, and knowledge discovery.

What are the main obstacles that are frequently mentioned by potential users? Which best practices for implementing linked data on a larger scale have already been developed? What are the ‘low-hanging fruits’, and how could a concrete action plan look like? Will the often predicted interlinking of an open semantic web and corporate semantic webs take place? Which other technology (of the above mentioned hype cycles) might play a crucial role as an enabler for enterprise linked data? Which other (mega-)trends will influence the pace of linked data adoption, and which related organisational challenges should be expected?

Please visit Andreas Blumauer’s talk ‘Linked Data – The Next 5 Years: From Hype to Action’ at SEMANTiCS 2016 in Leipzig to get some valuable impulses for your Linked Data project!

 

I‐SEMANTICS 2010 Conference Program

This year´s I-Semantics offers a lot of great talks around linked data, semantic search and other hot topics around the big question “how to make the web and applications a little bit smarter once again”. And like every year: It´s the place to go (at least in Central Europe) to meet other people interested and working in the area of the semantic web. Social events at I-Semantics are really cool 🙂

Download Conference Programme

Photo by Leobard