• Proposal of Analog2KG , a pipeline for turning textual analogies into knowledge graphs • Knowledge-graph version of 2 long-text analogy datasets, RattermannKG and WhartonKG • Modification of information extraction methods for maintaining analogical structure • Introduction of an LLM-free discovery methodology for higher-order relationships • Comparison to 3 LLM-enabled information extraction algorithms Analogical reasoning is an increasingly popular, lightweight solution to enable large language model (LLM)-level reasoning without computational complexity. Still, it has yet to be adopted due to its reliance on strictly hand-formatted data. Therefore, we propose Analogy2KG (“Analogy to Knowledge Graph’’), as an automatic pipeline that transforms text into a KG format via a fine-tuned version of information extraction (IE) algorithms for long-text analogies. The need to verify that the complex underlying analogical structure of the data is maintained was done via paired samples tests in the creation and validation of this pipeline. Graph density was used to evaluate the structural quality of the resulting KGs. Lastly, causal relationships were optionally detected using a novel, question-and-answer-based method. Analogy2KG was validated on the Rattermann and Wharton long-text datasets, which suggested that the proposed methodology maintains analogical structure when transforming from text to KGs. The resulting RattermannKG and WhartonKG datasets were introduced to the literature, which is the first instance of a the conversion of long-text analogy dataset into a KG format in the literature. Finally, Analogy2KG had superior performance among three LLM-enabled information extraction algorithms: ChatIE, Code4UIE, and InstructUIE for maintaining analogical structure, despite operating without the need for an LLM backend and a pre-defined relation extractor list; thus, making it an ideal lightweight solution.

• Inclusive language generation using Retrieval-Augmented Generation and expert-driven reasoning. • Chain-of-Thought based Mixture of Experts to ensure fairness, neutrality and coherence. • Model-agnostic design with validated bias mitigation across six social dimensions. Developing intelligent inclusive language generation systems that promote inclusivity and mitigate harmful or exclusive terms is a key challenge in advancing Equity, Diversity and Inclusion (EDI) principles. Using inclusive language in communication helps create a respectful, bias free and mutual understanding between the peers, which is also essential for organizations to promote safe and equitable workspaces. Inclusive language involves neutrality, tone sensitivity and fairness across diverse contexts, enabling meaningful engagement without marginalization. Considering this, we propose INCLUDE, an inclusive language generator designed for promoting respectful and equitable communication in workplaces. We curated a non-inclusive vs inclusive pair dataset including real-world workplace, advertisements and HR policy discourse with annotated inclusive rewrites. The proposed framework employs three experts dedicated to inclusiveness, bias and stereotype free and contextual relevance to facilitate the learning of diverse semantics. To optimize these experts, we propose a self-calibration mechanism using meta-prompting guided by a novel multi-dimensional reward function. Extensive evaluations, including metrics LLM based assessments and human in the loop analysis shows that proposed model effectively counters non-inclusive language into contextually appropriate, inclusive and accessible responses while maintaining the original intent.