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OracleSage: Advancing the Study of Oracle Bone Scripts

A new framework aids in interpreting ancient Chinese writings.

Hanqi Jiang, Yi Pan, Junhao Chen, Zhengliang Liu, Yifan Zhou, Peng Shu, Yiwei Li, Huaqin Zhao, Stephen Mihm, Lewis C Howe, Tianming Liu

― 8 min read


Decoding Oracle Bone Decoding Oracle Bone Scripts Chinese writings. A new approach for interpreting ancient
Table of Contents

Oracle Bone Scripts (OBS) are China's earliest known writing system, dating back to the Shang Dynasty around 1250-1050 BCE. Think of them as the ancestors of modern Chinese characters. These ancient inscriptions were carved into bones and shells and were primarily used for divination, which is just a fancy way of saying that people would ask questions and seek answers from these magical scripts. However, recognizing and understanding these ancient symbols is no small task.

Because OBS characters are quite complex and look different from the characters we see today, scholars have faced significant challenges in interpreting them. Only a small fraction of these characters has been deciphered, and even experts can struggle to make sense of the intricate designs. This means there are still plenty of mysteries left in the world of oracle bone scripts.

Introducing OracleSage: A New Approach

To tackle the challenges of understanding OBS, a new framework named "OracleSage" has been developed. You can think of OracleSage as a clever detective that combines its skills in both art and language to crack the case of these ancient texts. This system integrates visual and linguistic understanding, much like how a seasoned detective uses observation skills and language to make sense of clues.

OracleSage has three main parts:

  1. Hierarchical Visual-Semantic Understanding: This part helps the system recognize different features of the characters, whether they are big or small. It’s like choosing the right glasses to see both the whole picture and the tiny details.

  2. Graph-based Semantic Reasoning: This part is like a GPS that helps make connections between different visual elements and their meanings. It looks at how different pieces relate to each other, making sense of the overall message.

  3. OracleSem Dataset: This is a treasure trove of data that is packed with detailed information about the characters, including their meanings and structures. It's like having a guidebook that provides all the background information you need.

Why the Old Scripts Matter

You might wonder why someone would go through all the trouble of decoding these ancient writings. Well, OBS offers a direct glimpse into ancient Chinese civilization, revealing insights into their culture, beliefs, and practices. This makes it more than just a historical exercise; it’s like reading the ancient version of a social media feed from thousands of years ago.

Researchers have been trying various methods to understand these inscriptions. In the past, the focus was mainly on the cultural and philosophical aspects of the characters. However, with the rise of technology, researchers are now employing computational methods to lend a hand.

The Challenges of Interpretation

So, what's the deal with understanding OBS? Well, there are a ton of challenges to address. First and foremost, there are over 150,000 discovered fragments of oracle bone scripts, and only about 1,800 have been interpreted correctly. That's a whole lot of characters waiting to spill their secrets!

The variation in how the characters look adds another layer of complexity. The characters can seem like a chaotic mix of strokes and shapes, making it hard for even trained eyes to make sense of them. Plus, there are not enough experts available to keep up with the demand for interpretation, meaning things can get pretty slow.

In recent years, new technologies like AI and machine learning emerged, shaking things up. These tools help researchers analyze patterns and recognize characters more effectively. But there is still a gap between visual recognition and understanding the meanings behind the characters.

OracleSage to the Rescue

Recognizing the need for a better approach, OracleSage was born. This framework offers a fresh perspective on how to interpret OBS by focusing on both visual features and meanings.

Instead of using a one-size-fits-all method, OracleSage combines multiple techniques. It looks at characters from different angles, just like how you would analyze a piece of art. Using its dual-perspective approach, it can better understand the design and meaning of each character, making Interpretations richer and more nuanced.

Innovations in OracleSage

OracleSage isn’t just another high-tech instrument; it brings some innovative features to the table.

Hierarchical Visual-Semantic Understanding (HVSU)

The HVSU module is the backbone of OracleSage. It focuses on extracting visual features from oracle bone characters. Picture it as a wizard that can see the fine details of each character while also appreciating the overall design.

This module is crafted to adapt to the unique characteristics of OBS. It preserves knowledge from previous models, ensuring that the fine-tuning process doesn’t distort prior learning. Essentially, it’s like getting a refresher course before tackling a new subject.

Graph-based Semantic Reasoning Framework (GSRF)

Once the visual features are extracted, the GSRF helps establish relationships between the various components. It looks at OBS as if they were parts of a puzzle, connecting pieces to build a complete picture. This graph-like structure allows for dynamic reasoning about the characters, enhancing understanding of their meanings and connections.

OracleSem: A Dataset for the Ages

The introduction of OracleSem marks an important milestone in OBS research. This dataset is different because it offers deep semantic annotations for each character. It’s not just a list of characters; it gives insights into their pictographic meanings and structure.

For every character in OracleSem, there are detailed descriptions of its features, evolution, and even how it relates to modern Chinese characters. This comprehensive approach makes OracleSem a valuable tool for researchers and AI models alike.

Performance Evaluation

To see how well OracleSage works, it was evaluated on the newly created OracleSem dataset. The results showed that, while it might not always reach the highest accuracy compared to traditional deep learning methods, it significantly improves the interpretability of predictions. In the world of ancient texts, context is vital and OracleSage delivers that.

When comparing OracleSage to older methods, it stood out because it interpreted characters while explaining their meanings. This interpretability is key because simply identifying a character without understanding its context is like reading a book but missing the plot.

Examples and Insights

Let’s take a look at some examples of how OracleSage works its magic.

In one instance, a character resembling a crown positioned above a head conveys "elevation" or "importance." This means it could refer to a "crown" or something similar in modern Chinese. The system understands that the arrangement of the character plays a role in its meaning.

Another character might feature a complex arrangement that depicts a burial scene. OracleSage recognizes the shape and cultural significance, linking it to the term for "to bury" in modern Chinese.

Through these examples, OracleSage demonstrates its capability to delve into spatial relationships, similar to how we might interpret art. Understanding the deeper meanings behind the characters adds a layer of context that enhances research and comprehension of ancient scripts.

Challenges and Limitations

Despite the advances brought by OracleSage, there are still challenges ahead. First off, the accuracy metrics need further improvement when compared to traditional methods. This indicates that while we are making headway in understanding meanings, there is still work to be done in recognizing the characters precisely.

Also, the OracleSem dataset includes only a limited number of characters. With hundreds of thousands of oracle bone fragments waiting to be interpreted, researchers will need more expert collaboration to expand this dataset and enhance its annotations.

Another worry is that OracleSage may need adjustments when it comes to other types of ancient writing. While it excels at pictographic writing systems, it may not perform as well with scripts that don’t have a clear connection between visual features and meanings.

Future Directions

Even with its limitations, there are exciting possibilities for OracleSage’s future:

  1. Expanding the Dataset: Researchers can work to expand OracleSem by adding new characters and providing annotations for lesser-known symbols.

  2. Interactive Tools: Imagine a platform where archaeologists can tweak predictions and explore the data interactively. This could help refine the model and improve interpretations.

  3. Educational Uses: The framework could be adapted to create learning tools for students eager to explore ancient writing systems, making history feel alive and accessible.

  4. Incorporating Audio: Adding audio elements, perhaps even reconstructed pronunciations, could deepen the understanding of how these ancient scripts were used in daily life.

  5. Broader Applications: By fine-tuning the system, OracleSage could be adapted to analyze other ancient scripts, showcasing its versatility beyond just obs.

  6. Enhanced Interpretability: Future versions could provide more visual cues to explain predictions, making it easier for researchers to trust and understand the system’s interpretations.

  7. Knowledge Graph Integration: This would allow OracleSage to weave connections between characters, meanings, and historical contexts, enriching the narrative around ancient texts.

Conclusion

OracleSage is more than just a technical advancement; it provides a bridge between ancient scripts and modern understanding. By combining visual features with semantic meanings, it makes strides in deciphering the secrets of Oracle Bone Script. With ongoing collaboration and innovation, there is hope for an enriched understanding of ancient Chinese civilization and, perhaps, a few more mysteries solved.

Also, remember: sometimes, catching a glimpse into the past can feel like trying to find your way through a maze-intriguing, challenging, and a bit like chasing a ghost! But with tools like OracleSage, we stand a better chance of unraveling these ancient texts and shining a light on the stories they hold. So, here’s to deciphering the past, one character at a time!

Original Source

Title: OracleSage: Towards Unified Visual-Linguistic Understanding of Oracle Bone Scripts through Cross-Modal Knowledge Fusion

Abstract: Oracle bone script (OBS), as China's earliest mature writing system, present significant challenges in automatic recognition due to their complex pictographic structures and divergence from modern Chinese characters. We introduce OracleSage, a novel cross-modal framework that integrates hierarchical visual understanding with graph-based semantic reasoning. Specifically, we propose (1) a Hierarchical Visual-Semantic Understanding module that enables multi-granularity feature extraction through progressive fine-tuning of LLaVA's visual backbone, (2) a Graph-based Semantic Reasoning Framework that captures relationships between visual components and semantic concepts through dynamic message passing, and (3) OracleSem, a semantically enriched OBS dataset with comprehensive pictographic and semantic annotations. Experimental results demonstrate that OracleSage significantly outperforms state-of-the-art vision-language models. This research establishes a new paradigm for ancient text interpretation while providing valuable technical support for archaeological studies.

Authors: Hanqi Jiang, Yi Pan, Junhao Chen, Zhengliang Liu, Yifan Zhou, Peng Shu, Yiwei Li, Huaqin Zhao, Stephen Mihm, Lewis C Howe, Tianming Liu

Last Update: 2024-11-26 00:00:00

Language: English

Source URL: https://arxiv.org/abs/2411.17837

Source PDF: https://arxiv.org/pdf/2411.17837

Licence: https://creativecommons.org/licenses/by/4.0/

Changes: This summary was created with assistance from AI and may have inaccuracies. For accurate information, please refer to the original source documents linked here.

Thank you to arxiv for use of its open access interoperability.

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