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Raghava Mutharaju

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6 papers

2 author rows

NAI Journal 2025 Journal Article

Benchmarking Neurosymbolic Description Logic Reasoners: Existing Challenges and a Way Forward

Gunjan Singh
Riccardo Tommasini
Sumit Bhatia
Raghava Mutharaju

Recently, there has been significant progress in the development of robust and highly scalable neurosymbolic description logic reasoners. However, the field faces challenges arising from diverse design strategies and evaluation methods. We address the latter challenge by emphasizing the critical requirement for a comprehensive benchmark framework tailored to the unique evaluation needs of neurosymbolic description logic reasoners. In this paper, we address barriers that must be overcome to facilitate the effective evaluation of these reasoners and outline a potential methodology for designing the benchmark framework. This work contributes toward a more systematic and principled evaluation framework for neurosymbolic reasoning, highlighting the broader role of benchmarks in advancing the field.

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IJCAI Conference 2025 Conference Paper

Moral Compass: A Data-Driven Benchmark for Ethical Cognition in AI

Aisha Aijaz
Arnav Batra
Aryaan Bazaz
Srinath Srinivasa
Raghava Mutharaju
Manohar Kumar

We propose the Moral Compass benchmark, a point of reference for incorporating ethical cognition in AI. It has four key contributions. A Moral Decision Dataset (MDD) that captures cases with ethical ambiguity, along with parameters that aid moral decision-making. It is created using a methodology that leverages the use of Large Language Models (LLMs) and seed data from real-world sources which are processed, summarized, and augmented. We also introduce a Moral Decision Knowledge Graph (MDKG) that is created using feature mappings of the relational dataset MDD to facilitate efficient querying. To demonstrate the validity and robustness of this dataset, we introduce an Ethics Scoring Algorithm (ESA) that makes use of the parameters defined in the dataset to calculate ethical scores for isolated actions. Furthermore, ESA is extended by the novel concept of context-sensitive thresholding (CST) to discretize grey areas to resolve ethical dilemmas with explainable results. This work aims to facilitate ethical cognition in AI systems that are deployed in various important sections of society through a clear methodology, modular development, and broad applicability.

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AAAI Conference 2024 Conference Paper

Revisiting Document-Level Relation Extraction with Context-Guided Link Prediction

Monika Jain
Raghava Mutharaju
Ramakanth Kavuluru
Kuldeep Singh

Document-level relation extraction (DocRE) poses the challenge of identifying relationships between entities within a document. Existing approaches rely on logical reasoning or contextual cues from entities. This paper reframes document-level RE as link prediction over a Knowledge Graph (KG) with distinct benefits: 1) Our approach amalgamates entity context and document-derived logical reasoning, enhancing link prediction quality. 2) Predicted links between entities offer interpretability, elucidating employed reasoning. We evaluate our approach on benchmark datasets - DocRED, ReDocRED, and DWIE. The results indicate that our proposed method outperforms the state-of-the-art models and suggests that incorporating context-based Knowledge Graph link prediction techniques can enhance the performance of document-level relation extraction models.

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AAAI Conference 2021 Short Paper

Neuro-Symbolic Techniques for Description Logic Reasoning (Student Abstract)

Gunjan Singh
Sutapa Mondal
Sumit Bhatia
Raghava Mutharaju

With the goal to find scalable reasoning approaches, neurosymbolic techniques have gained significant attention. However, the existing approaches do not take into account the inference capabilities of ontology languages that are based on expressive description logic (such as OWL 2). To fill this gap, we propose two approaches: an ontology-based embedding model for theories in EL++ description logic and a reinforcement learning-based solution for efficient tableau-based reasoning on description logic. We describe promising initial results of our efforts towards these directions and lay down the direction for future work.

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KER Journal 2018 Journal Article

A survey of large-scale reasoning on the Web of data

Grigoris Antoniou
Sotiris Batsakis
Raghava Mutharaju
Jeff Z. Pan
Guilin Qi
Ilias Tachmazidis
Jacopo Urbani
Zhangquan Zhou

Abstract As more and more data is being generated by sensor networks, social media and organizations, the Web interlinking this wealth of information becomes more complex. This is particularly true for the so-called Web of Data, in which data is semantically enriched and interlinked using ontologies. In this large and uncoordinated environment, reasoning can be used to check the consistency of the data and of associated ontologies, or to infer logical consequences which, in turn, can be used to obtain new insights from the data. However, reasoning approaches need to be scalable in order to enable reasoning over the entire Web of Data. To address this problem, several high-performance reasoning systems, which mainly implement distributed or parallel algorithms, have been proposed in the last few years. These systems differ significantly; for instance in terms of reasoning expressivity, computational properties such as completeness, or reasoning objectives. In order to provide a first complete overview of the field, this paper reports a systematic review of such scalable reasoning approaches over various ontological languages, reporting details about the methods and over the conducted experiments. We highlight the shortcomings of these approaches and discuss some of the open problems related to performing scalable reasoning.

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ECAI Conference 2012 Conference Paper

Reasoning with Fuzzy-EL+ Ontologies Using MapReduce

Zhangquan Zhou
Guilin Qi
Chang Liu 0021
Pascal Hitzler
Raghava Mutharaju

Fuzzy extension of Description Logics (DLs) allows the formal representation and handling of fuzzy knowledge. In this paper, we consider fuzzy-EL+, which is a fuzzy extension of EL+. We first present revised completion rules for fuzzy-EL+that can be handled by MapReduce programs. We then propose an algorithm for scale reasoning with fuzzy-EL+ontologies based on MapReduce.

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