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Nitisha Jain

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3 papers

2 author rows

NAI Journal 2025 Journal Article

Towards Interpretable Embeddings: Aligning Representations With Semantic Aspects

Nitisha Jain
Antoine Domingues
Adwait Baokar
Albert Meroño Peñuela
Elena Simperl

Knowledge graph embedding models (KGEMs) project entities and relations from knowledge graphs (KGs) into dense vector spaces, enabling tasks such as link prediction and recommendation systems. However, these embeddings typically suffer from a lack of interpretability and struggle to represent entity similarities in a way that is meaningful to humans. To address these challenges, we introduce InterpretE, a neuro-symbolic approach that generates interpretable vector spaces aligned with human-understandable entity aspects. By explicitly linking entity representations to their desired semantic aspects, InterpretE not only improves interpretability but also enhances the clustering of similar entities based on these aspects. Our experiments demonstrate that InterpretE effectively produces embeddings that are interpretable and improve the evaluation of semantic similarities, making it a valuable tool in explainable AI research by supporting transparent decision-making. By offering insights into how embeddings represent entities, InterpretE enables KGEMs to be used for semantic tasks in a more trustworthy and reliable manner.

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NeSy Conference 2024 Conference Paper

Bringing Back Semantics to Knowledge Graph Embeddings: An Interpretability Approach

Antoine Domingues
Nitisha Jain
Albert Meroño-Peñuela
Elena Simperl

Abstract Knowledge Graph Embeddings Models project entities and relations from Knowledge Graphs into a vector space. Despite their widespread application, concerns persist about the ability of these models to capture entity similarity effectively. To address this, we introduce InterpretE, a novel neuro-symbolic approach to derive interpretable vector spaces with human-understandable dimensions in terms of the features of the entities. We demonstrate the efficacy of InterpretE in encapsulating desired semantic features, presenting evaluations both in the vector space as well as in terms of semantic similarity measurements.

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NeurIPS Conference 2024 Conference Paper

Croissant: A Metadata Format for ML-Ready Datasets

Mubashara Akhtar
Omar Benjelloun
Costanza Conforti
Luca Foschini
Pieter Gijsbers
Joan Giner-Miguelez
Sujata Goswami
Nitisha Jain

Data is a critical resource for machine learning (ML), yet working with data remains a key friction point. This paper introduces Croissant, a metadata format for datasets that creates a shared representation across ML tools, frameworks, and platforms. Croissant makes datasets more discoverable, portable, and interoperable, thereby addressing significant challenges in ML data management. Croissant is already supported by several popular dataset repositories, spanning hundreds of thousands of datasets, enabling easy loading into the most commonly-used ML frameworks, regardless of where the data is stored. Our initial evaluation by human raters shows that Croissant metadata is readable, understandable, complete, yet concise.

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