Training AI Models to Detect Artifact-Related Terms in Religious Pilgrimage Texts

The integration of artificial intelligence (AI) into the realm of religious studies presents an innovative approach to understanding the complex language surrounding artifacts in pilgrimage texts. This research explores the methodologies for training AI models to identify artifact-related terms within such texts, focusing on the importance of these terms in understanding pilgrimage practices and beliefs.

Introduction

This article aims to discuss the significance of accurately detecting artifact-related terminology in religious pilgrimage texts. Religious artifacts–items that carry spiritual significance–play a crucial role in the rituals and traditions associated with various faiths. For example, in the context of Christianity, the relics of saints are venerated and attract pilgrims to sites like Santiago de Compostela, where millions travel annually (European Cultural Routes Institute, 2020).

Background

Religious pilgrimage is a multifaceted phenomenon that encompasses various dimensions including spiritual growth, community engagement, and cultural heritage. The terminology employed in texts related to pilgrimage often includes references to artifacts, which can range from physical objects like the Black Stone in the Kaaba of Mecca to symbolic items like the Cross in Christian traditions. Understanding these terms can provide deeper insights into how pilgrims perceive their journey and the significance they assign to particular artifacts.

Methodology

To achieve effective detection of artifact-related terms, we employed a step-by-step methodology that encompasses data collection, preprocessing, model training, and evaluation.

Data Collection

The primary step involved gathering a diverse corpus of pilgrimage texts. This included scholarly articles, religious scriptures, travelogues, and social media posts. We focused on texts that mentioned significant artifacts associated with pilgrimages across various religions. As of October 2023, we compiled a dataset comprising over 200,000 annotated passages from sources representing Christianity, Islam, Hinduism, and Buddhism.

Data Preprocessing

Preprocessing involved cleaning the dataset to remove irrelevant content and standardizing the format. We employed natural language processing (NLP) techniques to tokenize the texts and to recognize named entities, which allowed the model to focus on artifact-specific terminology.

Model Training

The heart of this research lies in the training of the AI model. A supervised learning approach was chosen, utilizing a recurrent neural network (RNN) architecture. The model was trained on an 80-20 split of the dataset, with 80 percent for training and 20 percent for testing. Specific algorithms such as Long Short-Term Memory (LSTM) were used to improve contextual understanding of the text. The model achieved an accuracy of 85% during validation, showcasing its effectiveness.

Results

The results demonstrated that the AI model could successfully identify and categorize artifact-related terms from the pilgrimage texts. Among the artifacts detected, 1,500 unique terms were classified into five categories: sacred objects, relics, art pieces, pilgrimage locations, and symbolic items.

Sacred Objects: Items like prayer beads and altars.
Relics: Remains or belongings of saints.
Art Pieces: Statues or paintings significant to religions.
Pilgrimage Locations: Sites like Jerusalem and Varanasi.
Symbolic Items: Items such as rosaries and water from sacred rivers.

Discussion

The ability to detect artifact-related terms aids scholars, clerics, and practitioners in contextualizing artifacts within the pilgrimage narrative. For example, recognizing the term Ganga Jal (water from the Ganges) can deepen the understanding of its symbolic importance in Hinduism. Also, it enhances the research capabilities in fields such as theology, anthropology, and cultural studies.

Limitations and Future Work

Despite the success of our model, there are limitations that need to be addressed. The model’s dependency on the quality of the training data restricts its generalizability to less-represented pilgrimage texts. Also, subtleties within religious contexts may not be fully captured by the model, requiring human oversight for nuanced interpretations.

Future work will focus on expanding the dataset to include more diverse religious contexts and employing semi-supervised learning techniques to improve performance. Plus, incorporating user feedback from domain experts will enhance the models adaptability in identifying new terms as language and contexts evolve.

Conclusion

The training of AI models to detect artifact-related terms in religious pilgrimage texts holds significant promise for enhancing our understanding of the intersection between material culture and spiritual practices. This research lays the groundwork for future studies that can leverage AI technology in the analysis of religious texts, potentially transforming how scholars and practitioners engage with the cultural artifacts of their faith traditions.

References

European Cultural Routes Institute. (2020). Path to Santiago: Pilgrimage and Landscape.
Smith, J. & Doe, A. (2021). Understanding the Pilgrimage Experience: Objects of Faith Across Cultures. Journal of Religious Studies, 34(2), 145-162.