Can Knowledge Graphs help Data Mining Algorithms?

March 01, 2019

SEMANTiCS 2019 Poster and Demo Chair Mehwish Alam is a Post-Doctoral Researcher at Consiglio Nazionale delle Ricerche (CNR), Rome, Italy. The focus of her research is to use/develop Data Mining, Machine Learning/Deep Learning techniques for Semantic Web and Text processing. In this Interview, Mehwish talks about the combination of AI, Knowledge Graphs and NLP.

Looking into Data Mining, Machine Learning and Deep Learning methods for Text processing: What are the most exciting technology trends? Which breakthroughs do you see coming up on the horizon? Which ones in academia? Which ones for businesses?

A combination of AI, Knowledge Graphs and NLP can lead to many different exciting applications or probably many new methods. Currently, Machine Learning and Deep Learning methods are very widely used in text processing as well as in Knowledge Graphs. However, a very exciting emerging aspect is that the semantics embedded in the Knowledge Graphs and the information in textual resources can really benefit from each other from research point of view. However, these research ideas should be capable of practical applications from industrial point of view.

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With the catchphrase “explainable AI” an integrated view on Semantics and Machine Learning currently gets a lot of attention. How could a conceptual approach to Semantics and Data Mining look like?

Data Mining focuses on discovering new patterns in a data set using several methods which include machine learning methods. There can be many ways Data Mining algorithms can take advantage of Knowledge Graphs and the other way round. These methods can also help in generating Knowledge Graphs or Knowledge Graphs can be used to interpret the patterns generated by these methods. It can be used to find representations of Knowledge Graphs to feed to Neural Networks. Current methods for generating Knowledge Graph Embeddings follow these kinds of approaches. The hot topics regarding a combination of these technologies include metaphor processing, question answering, sentiment analysis, natural language understanding and many more. Multilinguality is one of the most interesting aspects of text processing which applies to all of these research areas.

At SEMANTiCS 2019 you will be chairing the Poster and Demo Track. What are your expectations in this respect? What do you especially look forward to about the submissions?

According to my experience with the SEMANTiCs conference, I am very convinced that this conference is a very important platform for cultivating the exchange of ideas between industry and academia. Usually there is a gap between research and industrial applications. As a chair of Poster & Demo Track, I am looking forward to a middle ground which gives an intuition of how the introduced methodologies can translate into a working system targeting the issues faced in industrial applications. I would suggest the researchers to submit their ideas to SEMANTiCs conference. This can be an excellent platform for engaging into a fruitful discussion with the experts in the  field. However, in many cases a non-expert opinion may also give you a different perspective on your research problem.

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The annual SEMANTiCS conference is the meeting place for professionals who make semantic computing work, and understand its benefits and know its limitations. Every year, SEMANTiCS attracts information managers, IT-architects, software engineers, and researchers, from organisations ranging from NPOs, universities, public administrations to the largest companies in the world.