6 citations · 12 across the 11 of their papers we have counts for
11 papers
Monitoring Neural Training with Topology: A Footprint-Predictable Collapse Index
Alexander Kalinowski
Representational collapse, where embeddings become anisotropic and lose multi-scale structure, can erode downstream performance long before performance metrics react. We propose an…
Calibrated Persistent Homology Tests for High-dimensional Collapse Detection
Alexander Kalinowski
We study detection of collapse in high-dimensional point clouds, where mass concentrates near a lower-dimensional set relative to a non-collapsed geometry. We propose persistent ho…
Enhancing Semantic Interoperability Across Materials Science With HIVE4MAT
Jane Greenberg, Kio Polson, Scott McClellan +3
HIVE4MAT is a linked data interactive application for navigating ontologies of value to materials science. HIVE enables automatic indexing of textual resources with standardized te…
Is the Lecture Engaging for Learning? Lecture Voice Sentiment Analysis for Knowledge Graph-Supported Intelligent Lecturing Assistant (ILA) System
Yuan An, Samarth Kolanupaka, Jacob An +5
This paper introduces an intelligent lecturing assistant (ILA) system that utilizes a knowledge graph to represent course content and optimal pedagogical strategies. The system is…
Knowledge Graph Question Answering for Materials Science (KGQA4MAT): Developing Natural Language Interface for Metal-Organic Frameworks Knowledge Graph (MOF-KG) Using LLM
Yuan An, Jane Greenberg, Alex Kalinowski +6
We present a comprehensive benchmark dataset for Knowledge Graph Question Answering in Materials Science (KGQA4MAT), with a focus on metal-organic frameworks (MOFs). A knowledge gr…
Repurposing Knowledge Graph Embeddings for Triple Representation via Weak Supervision
Alexander Kalinowski, Yuan An
The majority of knowledge graph embedding techniques treat entities and predicates as separate embedding matrices, using aggregation functions to build a representation of the inpu…
Exploring Wasserstein Distance across Concept Embeddings for Ontology Matching
Yuan An, Alex Kalinowski, Jane Greenberg
Measuring the distance between ontological elements is fundamental for ontology matching. String-based distance metrics are notorious for shallow syntactic matching. In this explor…
Building Open Knowledge Graph for Metal-Organic Frameworks (MOF-KG): Challenges and Case Studies
Yuan An, Jane Greenberg, Xintong Zhao +9
Metal-Organic Frameworks (MOFs) are a class of modular, porous crystalline materials that have great potential to revolutionize applications such as gas storage, molecular separati…
Clustering and Network Analysis for the Embedding Spaces of Sentences and Sub-Sentences
Yuan An, Alexander Kalinowski, Jane Greenberg
Sentence embedding methods offer a powerful approach for working with short textual constructs or sequences of words. By representing sentences as dense numerical vectors, many nat…
A Survey of Embedding Space Alignment Methods for Language and Knowledge Graphs
Alexander Kalinowski, Yuan An
Neural embedding approaches have become a staple in the fields of computer vision, natural language processing, and more recently, graph analytics. Given the pervasive nature of th…
A Comparative Study on Structural and Semantic Properties of Sentence Embeddings
Alexander Kalinowski, Yuan An
Sentence embeddings encode natural language sentences as low-dimensional dense vectors. A great deal of effort has been put into using sentence embeddings to improve several import…