Research

In general, I am interested in Machine Learning and Natural Language Processing, and how they enable applications in Semantic Web, Search or Information Retrieval, Recommendation, Personalization, and any data mining task in general. A few representative examples within these include entity extraction, relation extraction, entity linkage or named entity disambiguation, ontology creation, taxonomy construction and enrichment, recommending items/products/users, ranking items for retrieval, and more.

I have a rich experience on Information Extraction (IE) that helps build structured knowledge bases for Search, Recommendation, QA and any application that needs intelligent reasoning. In doing so, I have developed a variety of extraction techniques for the closed and open IE settings, starting from rule-based methods, weak supervision based methods to graph neural networks and large language models based methods. I also have some experience extracting and aligning information from web tables (relational tables embedded in HTML pages) for knowledge base enrichment.

Besides IE research aimed toward building knowledge bases, I have rich experience mining knowledge graphs specifically for the task of fact checking. As part of my doctoral work, I designed and developed multiple computational methods that perform fact checking of statements that can be expressed in the form of (subject, predicate, object) triplets, e.g., (Olympia, capital_of, Washington). In doing so, I leveraged large public, collaboratively-edited knowledge bases such as DBpedia, Freebase, YAGO and Wikidata, typically published as part of the Linked Open Data (LOD). I list below a few techniques that resulted from this work.

You can also find implementations of a few related techniques (Path Ranking Algorithm, PredPath) in the same GitHub repo.


Tutorials:

Publications: See my CV for a select set of papers and Google Scholar and DBLP for a complete list.

Thesis:

Posters: