10 citations · 32 across the 10 of their papers we have counts for
14 papers
Identifying and Consolidating Knowledge Engineering Requirements
Bradley P. Allen, Filip Ilievski, Saurav Joshi
Knowledge engineering is the process of creating and maintaining knowledge-producing systems. Throughout the history of computer science and AI, knowledge engineering workflows hav…
A Study of Slang Representation Methods
Aravinda Kolla, Filip Ilievski, Hông-Ân Sandlin +1
Considering the large amount of content created online by the minute, slang-aware automatic tools are critically needed to promote social good, and assist policymakers and moderato…
Analyzing Race and Country of Citizenship Bias in Wikidata
Zaina Shaik, Filip Ilievski, Fred Morstatter
As an open and collaborative knowledge graph created by users and bots, it is possible that the knowledge in Wikidata is biased in regards to multiple factors such as gender, race,…
User-friendly Comparison of Similarity Algorithms on Wikidata
Filip Ilievski, Pedro Szekely, Gleb Satyukov +1
While the similarity between two concept words has been evaluated and studied for decades, much less attention has been devoted to algorithms that can compute the similarity of nod…
A Study of the Quality of Wikidata
Kartik Shenoy, Filip Ilievski, Daniel Garijo +2
Wikidata has been increasingly adopted by many communities for a wide variety of applications, which demand high-quality knowledge to deliver successful results. In this paper, we…
Do Language Models Perform Generalizable Commonsense Inference?
Peifeng Wang, Filip Ilievski, Muhao Chen +1
Inspired by evidence that pretrained language models (LMs) encode commonsense knowledge, recent work has applied LMs to automatically populate commonsense knowledge graphs (CKGs).…
Representing Numbers in NLP: a Survey and a Vision
Avijit Thawani, Jay Pujara, Pedro A. Szekely +1
NLP systems rarely give special consideration to numbers found in text. This starkly contrasts with the consensus in neuroscience that, in the brain, numbers are represented differ…
Dimensions of Commonsense Knowledge
Filip Ilievski, Alessandro Oltramari, Kaixin Ma +3
Commonsense knowledge is essential for many AI applications, including those in natural language processing, visual processing, and planning. Consequently, many sources that includ…
Knowledge-driven Data Construction for Zero-shot Evaluation in Commonsense Question Answering
Kaixin Ma, Filip Ilievski, Jonathan Francis +3
Recent developments in pre-trained neural language modeling have led to leaps in accuracy on commonsense question-answering benchmarks. However, there is increasing concern that mo…
CSKG: The CommonSense Knowledge Graph
Filip Ilievski, Pedro Szekely, Bin Zhang
Sources of commonsense knowledge support applications in natural language understanding, computer vision, and knowledge graphs. Given their complementarity, their integration is de…
Commonsense Knowledge in Wikidata
Filip Ilievski, Pedro Szekely, Daniel Schwabe
Wikidata and Wikipedia have been proven useful for reason-ing in natural language applications, like question answering or entitylinking. Yet, no existing work has studied the pote…
Consolidating Commonsense Knowledge
Filip Ilievski, Pedro Szekely, Jingwei Cheng +2
Commonsense reasoning is an important aspect of building robust AI systems and is receiving significant attention in the natural language understanding, computer vision, and knowle…
KGTK: A Toolkit for Large Knowledge Graph Manipulation and Analysis
Filip Ilievski, Daniel Garijo, Hans Chalupsky +8
Knowledge graphs (KGs) have become the preferred technology for representing, sharing and adding knowledge to modern AI applications. While KGs have become a mainstream technology,…
The Profiling Machine: Active Generalization over Knowledge
Filip Ilievski, Eduard Hovy, Qizhe Xie +1
The human mind is a powerful multifunctional knowledge storage and management system that performs generalization, type inference, anomaly detection, stereotyping, and other tasks.…