Neo4j Live: Building a Semantics-Based Recommender System for ESG Documents

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Technology

Sustainability

Knowledge Management

Semantic Web

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Building a Semantics-Based Recommendation System for ESG Documents

ESG stands for environmental, social, and governance.
ESG documents are part of global initiatives to promote sustainability and green practices.
Semantic-based recommendation systems are the focus of this session.
The session will cover the importance of graphs in various industries.
The guest, ASD Crickle, is from Semantic Web Company and works as a data and knowledge engineer.
The session will discuss the relevance and traction of the ESG topic in the data and policy landscape.

Challenges in working with ESG documents

The variety of ESG documents from different industries poses a challenge in understanding the specific requirements for each company.
Understanding and analyzing the vast amount of data related to ESG, such as CO2 emissions and production factors, is difficult.
Knowledge graphs and semantics can help organize and analyze the data more efficiently.
Using relational databases to extract and analyze ESG data can be time-consuming compared to using knowledge graphs.

Building a Semantics-Based Recommendation System for ESG Documents

Exploring the Semantic Web and Property Graph approaches for modeling knowledge graphs.
Overview of Pool Party and Neo4j, and how they can be combined for optimal results.
Introduction to the use case of ESG documents and the integration process.
Details on data analysis, semantic search, and recommendations.
Live demo showcasing the integration and its capabilities.

Features and Functionality of Pool Party Knowledge Graph Management System

Pool Party is a Knowledge Management and Knowledge Graph management system.
It allows users to draw and manage their graph using a honeycomb structure.
Users can perform taxonomy and ontology management to enhance their data.
The system offers basic graph data management capabilities.
The extractor functionality enables users to use the taxonomy to find annotations and classifications within documents.
Pool Party supports natural language processing to perform text analysis using the knowledge in the background.
Users can work with metadata, infer knowledge, and utilize the taxonomy structure for detailed document analysis.
The system enables the creation of semantic search and recommender systems using the knowledge graph.
Pool Party allows for the integration of heterogenous data from various systems through data ingestion, mapping, and linking capabilities.

Advanced Semantic AI Applications and Pool Party Semantic Tagging

Advanced semantic AI applications can be built by going through the taxonomy ontology process.
Pool Party is a tool that enables semantic tagging and provides a semantic graph to find knowledge within documents or text.
The integration also includes the use of NEo4j, a market-leading graph database with high performance and scalability.
NEo4j's property graph modeling is different from semantic modeling, but the Neo Semantics add-on bridges the gap by allowing RDF data to be transformed into the format needed by NEo4j.
Neo Semantics also offers mapping, basic inference, and model validation capabilities using Shacker.
The integration and management of NEo4j and other systems is easy, making it convenient for developers.
The speaker also mentions a series called "Going Meta" that discusses various topics related to taxonomies, semantics, and bridging the gap between the RDF and graph worlds.

Integration Architecture and Use Case for Semantics in ESG Reporting

The integration architecture includes a PoolParty modeling server for taxonomy management and tagging, and a Neo4j server for storing documents and annotations.
The Neo4j server uses Neo Semantics to bring RDF data from PoolParty into Neo4j and also publishes the taxonomy and ontology.
The PoolParty recommender server queries the Neo4j server to display results for search applications, analytics, and recommendations.
The use case focuses on ESG reporting, which involves responsible business strategies and reporting on environmental, social, and governance factors.
ESG reporting requires finding relevant information and understanding how it relates to the report.
The integration architecture and tools aim to help ESG managers find relevant documents, explain their relevance, and identify relevant sustainable development goals (SDGs).
The demo showcases an Enterprise recommender for insights on which SDGs are relevant to the report and the company's focus.

Using Semantic Layer to Analyze ESG Data

ESG data is analyzed using a knowledge model and annotated documents.
The data is stored in a Neo4j database and combined with information from PoolParty.
The semantic layer allows for graph traversal to understand document relationships and their connection to ESG.
Screenshots from the Neo4j database show the semantic context around documents and their related concepts.
Document similarity can be inferred by analyzing shared concepts and taxonomy relationships.
The semantic layer also helps determine how documents relate to specific ESG goals or SGs.
The process of curating ESG datasets often involves manual curation and downloading reports.
The Sustain Graph project by the University of Athens focuses on creating a Knowledge Graph using United Nations sustainability goals.

Multilingual Support and Precision in Meaningful Recommendations in ESG Reporting

Multilingual support is natively supported by semantic web technology.
Building a taxonomy involves assigning labels in different languages to concepts.
Translation services can also be used to ensure accuracy in different languages.
PoolParty extractor can automatically annotate text in different languages.
The unique identifier (URI) of a concept can be used to query by language.
The search interface in PoolParty NE Forj provides short text annotations and highlights relevant tags.
Recommendations for specific Sustainable Development Goals (SDGs) can be inferred from the taxonomy.
Similar documents in a similar context can also be identified.

Using Semantics-based Recommender as a Sensitivity Labeler

Splitting up documents into smaller sections provides more context and insights.
Using filters and search functionalities can help narrow down results.
Similar documents can be explored to gain more information.
For using semantics-based recommender as a sensitivity labeler, it is important to ensure that enough context is provided.
To overcome the issue of "not enough context," consider the following advice: - Analyze the specific requirements and objectives of the sensitivity labeling task. - Incorporate additional features or metadata that can provide more context, such as document metadata, author information, or other relevant data. - Fine-tune the recommender model to prioritize sensitivity-related topics or keywords. - Experiment with different document segmentation strategies to find the most informative sections. - Consider incorporating user feedback or annotations to improve the sensitivity labeling accuracy. - Continuously evaluate and refine the sensitivity labeling process based on user feedback and specific use cases.

Importance of Taxonomy and Fine-Tuning for Quality Results

The quality of the taxonomy is crucial for obtaining good metadata and achieving high-quality results.
Having knowledgeable workers who understand the field being worked on is valuable for improving the quality of results.
Fine-tuning parameters, such as sensitivity labeling, can enhance the results and may require manual annotation.
Manual effort, such as manual annotation, can improve the quality of machine learning results.
Preparation and data cleaning are essential to overcome data mess and improve the outcome.
Working with synonyms and acronyms is important in establishing a proper knowledge system.
The initial attempt may not be perfect, but it provides valuable insights and allows for continuous improvement.
Integration and implementation of the system may take time and effort.
Indexing, such as label indexing, can be utilized to enable efficient search functionality.

Advantages of the Pool Party and Neo4j Integration

The integration allows for the use of natural language processing capabilities of Pool Party in Neo4j.
The semantic layer of taxonomies and ontologies from Pool Party can be easily integrated into Neo4j.
The integration enables the exploration of graph patterns and the identification of similarities and recommendations.
The property graph structure allows for the direct incorporation of extractor results and filtering of results based on similarity.
The use of properties on edges in Neo4j simplifies the implementation of relevancy and weighting.
The integration is easily adaptable to different use cases, with the ability to modify documents, taxonomies, graph patterns, and weights as needed.

Recommendations for Optimizing the Knowledge Base Process in a Company with Existing Docs and Information Sources

Start with the taxonomy and ontology to assess what already exists and what can be done with it.
If there is no existing taxonomy, create one using different strategies.
Test the extractor with the documents to see how well it works and if the data fits the taxonomy.
Iterate and make adjustments to the taxonomy and queries as needed.
Push in more data and continue refining the process.
For this project, an existing ESG taxonomy and ontology developed by the ESG group within the company were used.
Some documents worked better than others and there were iterations to find the best dataset.
Integration with LMS and chatbots is possible, but no specific examples were shared.

Pool Party Meets Chat GPT: Combining Knowledge Graph and Language Models for Better Answers

JGPT is a common language model but often provides incorrect answers and lacks context.
Combining knowledge graphs and language models can provide more accurate and contextual answers.
The demo showcases how the integration works, providing clearer answers with more context.
The blue concepts mentioned in the demo are part of the company's taxonomy and can be further explored.
The demo also includes a feature for playful exploration, but it's important to rely on knowledge and context for accurate answers.
Using company-specific knowledge instead of public data can lead to more relevant and reliable answers.

Summary of User Feedback and Closing Remarks

Users emphasized the importance of connecting learning management systems (LMS) with their own data and knowledge to retain context and information.
The chat GPT version used in the demo was mentioned to be based on GPT-3.
Appreciation was expressed for the overview, demo, and ad hoc demonstration provided.
Integration with PoolParty Neova J was highlighted as a valuable feature, allowing users to leverage existing data and documents effectively.
The audience was encouraged to explore the possibilities and benefits of connecting different tools and expanding their information footprint.

Integration of Pool Party and Neo4j for ESG Data Analysis and Recommendations

ESG stands for environmental, social, and governance.
ESG documents are part of global initiatives to promote sustainability and green practices.
Semantic-based recommendation systems are the focus of this session.
ASD Crickle from Semantic Web Company will discuss the relevance and traction of the ESG topic in the data and policy landscape.
Understanding and analyzing the vast amount of ESG data is difficult.
Knowledge graphs and semantics can help organize and analyze ESG data more efficiently.
Pool Party is a Knowledge Management and Knowledge Graph management system.
Pool Party allows users to draw and manage their graph using a honeycomb structure.
Pool Party supports natural language processing and offers basic graph data management capabilities.
The system enables the creation of semantic search and recommender systems using the knowledge graph.
Pool Party allows for the integration of heterogenous data from various systems.
Neo4j is a market-leading graph database with high performance and scalability.
The Neo Semantics add-on bridges the gap between semantic and property graph modeling.
The integration architecture includes a PoolParty modeling server for taxonomy management and tagging, and a Neo4j server for storing documents and annotations.
The PoolParty recommender server queries the Neo4j server to display results for search applications, analytics, and recommendations.
The use case focuses on ESG reporting and finding relevant information.
The integration architecture and tools aim to help ESG managers find relevant documents, explain their relevance, and identify relevant sustainable development goals (SDGs).
The Sustain Graph project by the University of Athens focuses on creating a Knowledge Graph using United Nations sustainability goals.

Recommendations for Sensitivity Labeling and Integration with PoolParty Neova J

Analyze specific requirements and objectives of the sensitivity labeling task.
Incorporate additional features or metadata for more context.
Fine-tune the recommender model to prioritize sensitivity-related topics.
Experiment with different document segmentation strategies.
Consider user feedback or annotations to improve labeling accuracy.
Continuously evaluate and refine the sensitivity labeling process.
Ensure a high-quality taxonomy for good metadata and results.
Have knowledgeable workers to improve result quality.
Fine-tune parameters and consider manual annotation for better ML results.
Prepare and clean data to improve outcomes.
Use synonyms and acronyms for a proper knowledge system.
Initial attempts may not be perfect, but provide insights for improvement.
Integration and implementation of the system may take time and effort.
Utilize indexing for efficient search functionality.
Integrate Pool Party's semantic layer into Neo4j for graph exploration.
Incorporate extractor results and filter based on similarity.
Use properties on edges in Neo4j for relevancy and weighting.
Adapt the integration to different use cases as needed.
Start with taxonomy and ontology assessment.
Create a taxonomy if none exists.
Test the extractor with documents and iterate as needed.
Use an existing ESG taxonomy and ontology for this project.
Connect with LMS and chatbots for possible integration.
Combine knowledge graphs and language models for accurate answers.