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Creating Knowledge Graphs from Raw Text: A Semi-Automatic Approach Using NLP and Wikidata, (from page 20290911.)

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Summary

This article discusses the process of constructing knowledge graphs from raw texts using a semi-automatic NLP pipeline. Knowledge graphs visualize relationships between entities and concepts within a specific domain, aiding in tasks like supply chain management. The author highlights the complexities of building knowledge graphs, which require programming, linguistics, and domain knowledge, particularly when starting with unstructured data. Despite the availability of various tools, a comprehensive automatic pipeline is still lacking. The author presents their own pipeline, which enhances knowledge graph creation by linking named entities from English and Japanese texts to Wikidata, thereby enriching the graph with additional entities and relationships.

Signals

name description change 10-year driving-force relevancy
Emergence of Semi-Automatic NLP Pipelines Growing trend of semi-automatic tools for creating knowledge graphs from raw text. Shift from fully manual to semi-automated knowledge graph creation processes. Widespread adoption of semi-automatic pipelines, enabling rapid knowledge graph construction across industries. The need for faster and more efficient data processing in various sectors. 4
Integration of Multilingual Capabilities in NLP NLP pipelines that support both English and Japanese text extraction. Transition from single-language NLP tools to multilingual capabilities. NLP tools will seamlessly handle multiple languages, broadening accessibility and usability. Globalization and the demand for cross-language data processing. 4
No-Code Tools for Knowledge Graph Navigation Rise of no-code tools simplifying the navigation of complex knowledge graphs. Move towards user-friendly interfaces that democratize access to knowledge graphs. Widespread use of no-code tools will empower non-technical users to create and navigate knowledge graphs. Desire for accessibility and ease of use in data management and visualization. 5
Domain-Specific NLP Tools Development Development of NLP tools tailored to specific domains and vocabularies. Emergence of specialized tools that cater to unique industry needs. Increased precision and efficiency in knowledge graph creation across various sectors. The need for tailored solutions to handle diverse data types and industry-specific challenges. 4
Challenges in Automatic Knowledge Graph Construction Persistent challenges in creating fully automatic knowledge graph pipelines. Ongoing struggle to achieve fully automated knowledge graph construction. Continued reliance on hybrid models combining human and machine efforts for knowledge graph creation. Complexity of natural language and the uniqueness of domain-specific knowledge. 4

Concerns

name description relevancy
Dependency on NLP Tools Over-reliance on NLP tools can create vulnerabilities if these systems fail or yield inconsistent results, especially in critical applications like supply chain management. 4
Complexity of Knowledge Graph Construction The inherent complexity in constructing knowledge graphs can lead to incorrect interpretations of data, causing potential mismanagement of information. 4
Variability of Outputs from Raw Texts Different NLP approaches can yield varying knowledge graphs from the same raw text, resulting in inconsistent data interpretations and applications. 3
Lack of End-to-End Solutions The absence of a comprehensive, automatic pipeline creates challenges for enterprises, impacting efficiency and reliability in utilizing knowledge graphs. 5
Language and Domain Limitations Specific vocabularies and ontologies for different domains may lead to challenges in knowledge graph applicability across diverse fields. 3

Behaviors

name description relevancy
Semi-Automatic Knowledge Graph Creation Utilizing a semi-automatic NLP pipeline to generate knowledge graphs from raw texts in multiple languages. 4
Integration of No-Code Tools Leveraging no-code tools like Gemini Data to simplify navigation and construction of knowledge graphs for non-technical users. 4
Domain-Specific NLP Tools Usage Employing specialized NLP tools tailored to specific vocabularies and ontologies of different domains. 5
Augmented Knowledge Graphs Enhancing traditional knowledge graphs by linking entities to external databases like Wikidata for richer context. 5
Multi-Language Processing Creating knowledge graphs from raw texts in different languages, such as English and Japanese, expanding accessibility and usability. 3
Dynamic Relationship Extraction Extracting and modeling relationships dynamically based on the context and content of the raw texts. 4

Technologies

name description relevancy
Knowledge Graphs Graphs that capture relationships between entities, concepts, and facts in a specific domain, enhancing data visualization and analysis. 4
Natural Language Processing (NLP) A field of AI that enables machines to understand and process human language, essential for tasks like entity recognition and relationship extraction. 5
No-Code Tools Platforms allowing users to create applications without traditional programming, making technology more accessible for knowledge graph creation. 3
Chatbots AI programs that simulate conversation, facilitating user interaction with knowledge graphs and data retrieval. 4
Domain-Specific NLP Tools NLP tools tailored for specific industries or contexts, improving the accuracy and relevance of text processing tasks. 4
Semi-Automatic NLP Pipeline A hybrid approach combining automation and human input to generate knowledge graphs from raw texts, improving efficiency and accuracy. 5
Zero-Shot Prompting A technique in AI that allows models to make predictions or decisions without prior examples, enhancing flexibility in NLP tasks. 4

Issues

name description relevancy
Knowledge Graph Complexity The challenge of navigating and constructing knowledge graphs, especially from raw texts, highlights the need for simplified tools and methodologies. 4
Domain-Specific NLP Tools The necessity for tailored NLP tools for different domains emphasizes the limitations of generic solutions in knowledge graph creation. 5
Semi-Automatic Knowledge Graph Generation The emergence of semi-automatic pipelines for knowledge graph generation indicates a shift towards more user-friendly and efficient methods. 4
Integration of Multilingual Data The ability to generate knowledge graphs from multiple languages (e.g., English and Japanese) points to the importance of multilingual capabilities in knowledge extraction. 3
No-Code Tools for Data Processing The rise of no-code tools for creating knowledge graphs suggests a growing trend towards accessibility in data processing for non-technical users. 4
Ontology Modeling Challenges The complexity of ontology modeling in knowledge graphs reflects ongoing difficulties in standardizing knowledge representation across domains. 4
Natural Language Processing Advancements The ongoing development in NLP techniques, such as named entity recognition and relationship extraction, is crucial for future knowledge graph applications. 5