Recent papers
Please look at Google Scholar to see the list of my most recept up-to-date papers, and be sure to check out https://techatbloomberg.com/ai as well!
Category: Conference Papers
Peer-reviewed papers, published in conference proceedings.
News Article Retrieval in Context for Event-centric Narrative Creation
Writers such as journalists often use automatic tools to find relevant content to include in their narratives. In this paper, we focus on supporting writers in the news domain to develop event-centric narratives. Given an incomplete narrative that specifies a main event and a context, we aim to retrieve news…
Contextualizing Trending Entities in News Stories
Trends are those keywords, phrases, or names that are mentioned most often on social media or in news in a particular timeframe.They are an effective way for human news readers to both discover and stay focused on the most relevant information of the day. In this work, we consider trends…
Evaluating the Calibration of Knowledge Graph Embeddings for Trustworthy Link Prediction
Little is known about the trustworthiness of predictions made by knowledge graph embedding (KGE) models. In this paper we take initial steps toward this direction by investigating the calibration of KGE models, or the extent to which they output confidence scores that reflect the expected correctness of predicted knowledge graph…
CrossBERT: A Triplet Neural Architecture for Ranking Entity Properties
Task-based Virtual Personal Assistants (VPAs) such as the Google Assistant, Alexa, and Siri are increasingly being adopted for a wide variety of tasks. These tasks are grounded in real-world entities and actions (e.g., book a hotel, organise a conference, or requesting funds). In this work we tackle the task of…
Novel Entity Discovery from Web Tables
When working with any sort of knowledge base (KB) one has to make sure it is as complete and also as up-to-date as possible. Both tasks are non-trivial as they require recall-oriented efforts to determine which entities and relationships are missing from the KB. As such they require a significant…
Identifying Notable News Stories
The volume of news content has increased significantly in recent years and systems to process and deliver this information in an automated fashion at scale are becoming increasingly prevalent. One critical component that is required in such systems is a method to automatically determine how notable a certain news story…
Related Entity Finding on Highly-heterogeneous Knowledge Graphs
In this paper, we study the problem of domain-specific related entity finding on highly-heterogeneous knowledge graphs where the task is to find related entities with respect to a query entity. As we are operating in the context of knowledge graphs, our solutions will need to be able to deal with…
Weakly-supervised Contextualization of Knowledge Graph Facts
Knowledge graphs (KGs) model facts about the world; they consist of nodes (entities such as companies and people) that are connected by edges (relations such as founderOf ). Facts encoded in KGs are frequently used by search applications to augment result pages. When presenting a KG fact to the user,…
The Second Workshop on Knowledge Graphs and Semantics for Text Retrieval, Analysis, and Understanding (KG4IR)
Semantic technologies such as controlled vocabularies, thesauri, and knowledge graphs have been used throughout the history of information retrieval for a variety of tasks. Recent advances in knowledge acquisition, alignment, and utilization have given rise to a body of new approaches for utilizing knowledge graphs in text retrieval tasks and…