Prof. Amit Sheth gave a talk on Knowledge Graphs and their central role in big data processing: Past, Present, and Future on 6 January, followed by interaction/discussions with KCIS faculty, students and a Q&A session.
Early use of knowledge graphs, before the start of this century, related to building a knowledge graph manually or semi-automatically and applying them for semantic applications, such as search, browsing, personalization, and advertisement. Taalee/Semagix Semantic Search in 2000 had a KG that covered many domains and supported search with an equivalent of today’s infobox. Along with the growth of big data, machine learning became the preferred technique for searching, analyzing and deriving insights from such data. Prof. Amit Sheth and his research group observed the complementary nature of bottom-up (machine learning-driven) and top-down (semantic, knowledge graph and planning based) techniques. Recently they have seen growing efforts involving the shallow use of a knowledge graph to improve the semantic and conceptual processing of data. The future promises deeper and congruent incorporation or integration of knowledge graphs in the learning techniques (which they call knowledge-infused learning), where knowledge graphs combining statistical AI (bottom-up) and symbolic AI learning techniques (top-down) play a critical role in hybrid and integrated intelligent systems. Throughout his talk, Prof. Sheth provided real-world examples, products, and applications where the knowledge graph played a pivotal role.
In an informal Town Hall – guided discussion with Q&A on Research Career and Higher Education choices for ICT students Prof Sheth discussed topics that may be of interest to 3rd and 4th year students:
- What are the exciting/hot job areas for Computer Science students? How do you classify different types of jobs/careers? Pros and cons for working first before higher studies versus going for graduate education immediately. Why some decide to do MS abroad after working for 2-3 years? When is higher education worth it? What should you do if you want to pursue a research career? What are the pros and cons of pursuing a research career?
- What are the options/trade offs in different countries (quality of education, costs, job prospects): India, US, UK, Germany, Singapore, Australia, Canada
- How to choose the right university? How do you assess ranking/quality and value for money/outcome? Ranking: university vs department vs lab/center and faculty: which are more important and why? Where/how can you get reliable ranking and quality information?
- Insights on how are applications by Indian students evaluated by admissions committees in USA
- Components of applications: GPA, TOEFL, GRE, internships/projects, reference letters, personal statement (SOP). What is a good schedule?
- Should you use agents? How to improve chances for admission in better universities and departments?
- How to write to a professor? When should you write a professor?
- MS vs PhD: how can you evaluate your choices? how do you know what is right for you? When is doing a PhD worth the time? Do all PhD students get fully funded?
- How do you get the most out of MS education? what are pros and cons: courses only, projects, or thesis?
- What kinds of funds/scholarships are available (hourly, tuition waiver, GRA, GTA)? How can you assess your chances of getting funding during your post-graduate studies? How can you improve the chances of getting funding? Which universities are more likely to fund MS students? How about internships before joining graduate studies?
- Should you take a loan?
- What are salaries for different jobs after MS and PhD in CS and some engineering fields?
- While topics/areas of CS are hot? CS vs CyberSecurity vs Data Science vs AI, etc.
Prof. Amit Sheth is an educator, researcher, and entrepreneur. Prior to his joining the University of South Carolina as the founding director of the university-wide AI Institute, he was the LexisNexis Ohio Eminent Scholar and executive director of Ohio Center of Excellence in Knowledge-enabled Computing. He is a Fellow of IEEE, AAAI, and AAAS. He is among the highly cited computer scientists worldwide (h-index 104, >44,000 citations, listed among the top 100 in the world in 2018). He has founded three companies by licensing his university research outcomes, including the first Semantic Web company in 1999 that pioneered technology similar to what is found today in Google Semantic Search and Knowledge Graph.