This is my personal space to share my thoughts and ideas on AI, Data and Productivity
I am a driven AI and Machine Learning leader focused on turning complex technology into useful products. As CTO of , I lead technology strategy across applied AI, data platforms, knowledge graphs, and product engineering, helping teams move from promising ideas to reliable systems that create measurable value.
My research background sits at the intersection of Knowledge Graphs, Semantic Web, Information Retrieval, and data quality. I hold a PhD in Semantic Web and Information Retrieval, and my publications explore Linked Data, recommender systems, dataset quality, and ways to make knowledge easier for both humans and machines to use.
Before Mav9, I was one of the founding engineers at , where I helped build and scale engineering, AI, and data science teams as the company grew into one of the latest tech unicorns. I use this space to write about AI, data, productivity, engineering practice, and the lessons learned from building intelligent products with real users and real constraints.
Knowledge Graphs are a powerful tool for organizing and representing information in a structured way. In this post, we explore the concept of Knowledge Graphs, their applications, and how they are transforming the way we interact with data.
Read Full ArticleHumans share, consume and produce knowledge using natural language; however presenting this knowledge in a machine readable and understandable format can be difficult. This post discusses the Resource Description Framework (RDF) which is the main data modeling block of the Semantic Web
The standardization of Semantic Web technologies and specifications has resulted in a staggering volume of data being published. In this post, I propose an objective assessment framework for Linked Data quality after having surveyed the landscape of Linked Data quality tools to discover that they only cover a subset of the proposed quality indicators