Bridging Text and Context: Ontology Development for Policy Document Analysis
Authors: Shikha Soneji, Jonathan Dodge


Reviewer paper summaries: The paper addresses the complexity of privacy policies and terms of service, highlighting how their intricacy and legal jargon make them difficult for users to understand. The authors propose developing a hierarchical ontological structure to simplify interpretation, automated analysis, and compliance verification of these documents. By using 245 examples from the ToS;DR website, the work aims to enhance transparency and user comprehension of privacy policies, aiding companies in adhering to privacy regulations.

The paper outlines a clear and well-structured methodology for data collection, classification, and ontology schema definition. The methodology includes the following steps:
1. Data Collection: Using the ToS;DR API and BeautifulSoup to extract information and generate a human-readable CSV dataset.
2. Data Classification: Labeling each case with broad categories referenced from the OPP-115 corpus using GPT to automate categorization.
3. Ontology Schema Definition: Defining the ontology schema based on hierarchical and associative relationships.
4. Ontology Construction: Using GPT-4 Turbo to find category value associations, ensuring an optimal fit based on predefined instructions.

The developed ontology provides a hierarchical structure that improves the understanding and management of policy documents, simplifies the automated analysis of unseen policy documents, and ensures regulatory compliance
Summary of strengths: - Improved document comprehension through a structured and simplified representation.
- Uniform and precise labeling of sections within policy documents.
- An adaptable framework applicable to various websites and types of legal documents.
Summary of weaknesses: - Complexity: Developing a comprehensive ontology that accurately represents the various components of terms of service agreements and privacy policies is challenging and time-consuming.
- Scalability: Continuous maintenance and refinement are necessary to ensure the ontology can scale to accommodate new regulations and changes.
- Interoperability: Integrating the ontology with existing systems and tools for privacy policy analysis requires careful consideration of compatibility and data standards.
Comments, suggestions and typos: While the paper is well-structured and provides a detailed methodology for ontology development, it does not explicitly mention whether manual validations by legal experts were conducted. 

The interpretation of privacy policies and terms of service requires in-depth legal knowledge, and the absence of expert validation could risk inaccuracies or misinterpretations. For example, ToS sections are often entitled with different labels, which should be considered "alternative lexical entries” of a standard, most common, category. Although the use of AI models like GPT-4 Turbo can automate and accelerate the classification process, understanding privacy policies and terms of service necessitates comprehensive legal knowledge. 

Therefore, integrating a phase for manual review by legal professionals into the ontology development process would be beneficial. This review could ensure that the categorizations are accurately aligned with existing legal frameworks and that the ontological relationships correctly reflect the legal implications of various document clauses. Tests should be implemented to verify that the ontology complies with privacy laws and regulations across different jurisdictions.

How do you manage multilingualism? Do you exclusively incorporate contents in English?

The final goal of the ontology, being supporting automatic ToS analysis or direct consultation by humans, should be better clarified.
Overall assessment scores: 2