XuLei

Extended Information#

How Tana's Supertags help you organize your digital life

🧑‍💻 User#

The AI features and user experience design of the note-taking tool Tana can be summarized as follows:

1. Intelligent content generation and filling
   Through preset prompt templates, AI can automatically fill in node metadata. For example, when entering an economics concept, the AI generates related terms in the "Related Concepts" field to assist in building the knowledge graph. Additionally, AI supports text-to-image generation (based on the DALL-E model), allowing users to generate matching illustrations by inputting descriptions, enhancing the visual expression of notes.

2. AI-assisted structured management
   Tana's Supertags are the core scenario for AI implementation. AI can automatically add tags to nodes based on content and dynamically adjust classifications. For instance, when users record life logs, AI can automatically categorize fragmented events as work, life, or entertainment based on timestamps and behavior patterns. This dynamic structuring capability far exceeds traditional tagging systems.

3. Command buttons and workflow automation
   Users can execute complex operations with one click through custom command nodes. For example, after configuring a "Meeting Summary" command, AI automatically extracts key points from the minutes, generates a task list, and assigns responsibilities. This "buttonized" interaction lowers the threshold for using AI features.

4. Supertag: Intelligent integration of unstructured information
   Supertag is Tana's signature feature, based on an object-oriented programming model, which can quickly transform scattered text, voice, or meeting notes into structured data. For example, when a user inputs "Customer feedback needs to optimize the login page," AI recognizes "Customer feedback" as a tag and associates the task with the "Product Optimization" project while generating priority suggestions.

I hope to design a next-generation library resource description AI metadata editor based on large models. Please refer to the features and interactions of the Tana notes above to help me design AI-related functions and interactions for the metadata editor.

Function Design Sketch#

First Round of Thoughts#

Okay, I now need to design a large model-based library resource description AI metadata editor, referencing Tana notes' AI features and user experience. First, I need to carefully understand the functional points of Tana provided by the user, and then think about how to apply these concepts to the library metadata editing scenario.

First, the user mentioned that Tana has four main features: intelligent content generation and filling, AI-assisted structured management, command buttons and workflow automation, and Supertag's integration of unstructured information. I need to analyze the applicability and transformation of these features in the context of library metadata editing one by one.

First is intelligent content generation and filling. Tana uses preset prompt templates to generate metadata, such as related concepts and text-to-image generation. Library metadata typically includes title, author, subject terms, classification numbers, abstracts, etc. AI can automatically extract this information from resource content, such as extracting abstracts from book PDFs, generating subject terms, or automatically recommending classification numbers based on content. Additionally, text-to-image generation may not be directly applicable in library resources, but it can be considered to generate book covers or related illustrations, such as generating cover sketches based on novel content or creating attractive illustrations for children's books. This may help catalogers quickly generate visual elements or store them as part of the metadata.

Next is AI-assisted structured management. Tana's Supertags can dynamically classify content. In library systems, resource classification and indexing are key. AI can automatically add appropriate subject tags or classification numbers based on resource content, such as automatically identifying whether a book belongs to "Computer Science" or "Literature," and dynamically adjusting the classification structure. For example, when a book involves multiple disciplines, AI can recognize and assign it to the corresponding multiple classifications, and even dynamically update tags based on the library's classification system. Additionally, it may also need to handle multilingual resources, automatically translating and generating multilingual metadata, which requires the large model's multilingual processing capabilities.

The third point is command buttons and workflow automation. Tana allows users to customize command nodes, such as generating meeting summaries with one click. In the library editor, similar one-click operations can be designed, such as a "Generate Complete Metadata" button that automatically fills in all possible fields, or a "Validate Metadata" button that checks whether existing metadata meets standards (such as MARC, RDA, etc.) and automatically corrects errors. There could also be specific commands like "Generate Subject Term Suggestions" or "Export to Specific Format," simplifying the cataloger's workflow.

The fourth point is the intelligent integration of unstructured information by Supertag. Library resource descriptions often need to handle unstructured data, such as user-submitted reviews, book content abstracts, or handwritten notes. AI can transform this information into structured metadata. For example, if a user inputs a book review, AI extracts keywords as subject terms, identifies related works mentioned by the author, and links them to existing bibliographic records. Alternatively, it can automatically convert resource demands from meeting notes into procurement suggestions and integrate them into the procurement system.

Now I need to translate these ideas into specific functions and interaction designs. I may need to consider the following aspects:

1. Automatic metadata extraction and filling: Utilize large models to analyze resource content (such as text, images, audio) and automatically generate title, author, abstract, subject terms, classification numbers, etc. For example, after uploading a PDF, AI automatically reads the content, extracts relevant information, and fills it into the editor form.
2. Dynamic classification and tag management: Automatically suggest or assign classification numbers (such as Chinese Library Classification, Dewey Decimal Classification), subject terms (such as LCSH), based on resource content and existing classification systems, and allow dynamic adjustments. AI can identify inconsistencies in classifications and suggest corrections.
3. Workflow automation commands: Preset buttons like "Auto Fill," "Validate and Fix," "Export as MARC21," etc., allow users to execute complex operations with one click, reducing manual steps. For example, after clicking "Validate," AI checks whether required fields are complete, whether formats are correct, and whether associated resources exist.
4. Unstructured data conversion: Allow users to input free text or voice, and AI parses it to generate structured metadata. For instance, a cataloger dictating a description of a book's content, AI transforms it into an abstract and extracts subject terms and classification numbers.
5. Intelligent recommendations and associations: Based on existing metadata and resource content, recommend related resources, authors, or subjects to help build a knowledge graph. For example, when editing a book on machine learning, AI recommends related conference papers, datasets, or online course resources and automatically establishes associations.
6. Multilingual support: Automatically translate metadata fields and generate multilingual versions to meet the needs of international libraries. For example, automatically translating the abstract of a Chinese book into English and filling it into the corresponding field.
7. Collaboration and version control: AI assists in collaborative editing among multiple users, automatically detecting conflicts, suggesting merging strategies, or recording modification history. For example, when two catalogers modify the same field simultaneously, AI analyzes the differences and provides solutions.

In terms of interaction design, similar visual elements to Tana's Supertag and command nodes can be designed. For example, using a tagging system to allow catalogers to quickly view and adjust AI-generated metadata, or dragging unstructured content to a designated area for AI to process automatically.

It may also be necessary to consider catalogers' work habits, compatibility with existing metadata standards (such as MARC, BIBFRAME, Dublin Core), and how to combine AI-generated content with manual review to ensure accuracy. For instance, AI-generated fields may need to be highlighted for catalogers to confirm or modify, while recording AI's suggestions and manual modifications for subsequent model training.

Additionally, in terms of user experience, it is essential to ensure that AI features are easily accessible and do not interfere with existing workflows. For example, displaying AI suggestions in a sidebar of the metadata editing interface or triggering commands via keyboard shortcuts. At the same time, providing a feedback mechanism allows users to rate AI's suggestions to optimize model performance.

Finally, potential issues need to be addressed, such as the accuracy of AI-generated content, the depth of understanding of professional classification systems, the accuracy of multilingual processing, and how to handle copyright and sensitive content (such as copyright issues that may arise from automatically generating covers). It may be necessary to integrate domain knowledge bases or authoritative data sources to enhance the reliability of AI suggestions, such as integrating the library's existing classification systems or subject term lists to ensure that generated metadata meets standards.

Next-Generation Library Resource Description AI Metadata Editor Based on Large Models: Function and Interaction Design Proposal#

Core Design Philosophy: Deeply integrate large models into the library metadata editing process, fundamentally enhancing cataloging efficiency, metadata quality, and user exploration experience. Abandon the mechanical repetitive labor of traditional metadata editors and build a new system with self-learning, adaptive, and proactive intelligence, allowing catalogers to focus on higher-level knowledge organization and semantic association work.

I. Intelligent Content Deep Parsing and Proactive Metadata Construction (Corresponding to Tana: Intelligent Content Generation and Filling)

Core Function: Go beyond simple field filling to achieve multi-modal, in-depth understanding of resource content and proactively construct complete, high-quality metadata records based on this understanding.

1. Multi-modal Content Understanding and Semantic Mining:

• Input Forms: The editor supports various resource input methods, including but not limited to:
  • Text: Full-text documents (PDF, TXT, DOCX, etc.), abstracts, tables of contents, prefaces, book reviews, etc.
  • Images: Book covers, illustrations, image materials.
  • Audio/Video: Lecture recordings, interview videos, multimedia resources.
  • URLs: Links to online resources.
• Intelligent Parsing: The large model deeply parses the input content, not only performing keyword extraction but also conducting semantic understanding, thematic analysis, and knowledge extraction.
  • Text Understanding: Identifying the theme, argument, research methods, key concepts, target audience of the text, and understanding the contextual background and implicit information.
  • Image Understanding: Recognizing image content, style, emotion, and understanding the relevance of images to text. For example, identifying characters and scenes on a book cover and associating them with the book's theme.
  • Audio/Video Understanding: Performing speech-to-text (ASR) and semantically analyzing the transcribed text to extract key information.
• Knowledge Graph Association: The system has built-in or can connect to existing library knowledge graphs/subject term lists/classification systems, deeply associating parsed concepts, themes, and entities with nodes in the knowledge graph rather than simple keyword matching.
  • Concept Association: After identifying an economics concept, not only generating the "Related Concepts" field but also deeply mining the concept's superordinate concepts, subordinate concepts, synonymous concepts, and related disciplines, constructing a more complete knowledge network.
  • Entity Recognition and Linking: Recognizing entities such as authors, institutions, and locations, and linking them to authoritative knowledge bases (such as VIAF, Wikidata, Geonames) to obtain richer entity information and achieve standardized control.

2. Proactive Metadata Generation and Filling:

• Intelligent Field Recommendations: Based on content parsing and knowledge graph associations, the system proactively recommends metadata fields that need to be filled and provides high-confidence pre-filled values.
  • Beyond Templates: No longer relying on preset prompt templates, but dynamically generating the best metadata scheme based on resource type, content characteristics, and knowledge domain. For example, for an economics monograph, the system will focus on recommending fields such as "Discipline Classification," "Subject Terms," "Research Methods," and "Target Audience."
• Multiple Scheme Generation and Explainability: For ambiguous or polysemous content, the system can generate multiple metadata filling schemes and explain the basis and confidence of each scheme for catalogers to choose and adjust.
  • Scheme Comparison: For example, in classification number recommendations, the system can state, "Based on content analysis, the probability of recommending classification number A is 80%, classification number B is 70%, classification number C is 60%," and explain the rationale for each classification number.
• Advanced Application of Text-to-Image Generation: Text-to-image generation can not only be used to create illustrations but also assist in the visual expression of metadata. For example, generating thematic cloud maps, concept relationship diagrams based on book themes as part of the metadata, intuitively showcasing the core content and knowledge structure of resources.

User Interaction:

• Visual Prompts: The system highlights AI-recommended metadata fields and pre-filled values in the editor interface and provides confidence scores for easy identification and judgment by catalogers.
• Scheme Selection and Adjustment: Allows catalogers to select, modify, and merge among multiple AI schemes, ensuring flexibility for manual intervention.
• Feedback Mechanism: Catalogers can provide positive/negative feedback on AI's recommendations, allowing the system to continuously learn and optimize the model.
• Explainability Display: Clicking on AI-recommended fields or values allows users to view the reasoning process and basis of AI, enhancing trust and understanding.

II. Dynamic Structuring and Knowledge Organization (Corresponding to Tana: AI-assisted Structured Management, Supertag)

Core Function: Go beyond static tagging systems to build a dynamic, adaptive, and semantically driven structured knowledge organization system that achieves intelligent integration and deep mining of unstructured information.

1. Semantically Driven Dynamic Tagging System (SuperTags 2.0):

• Beyond Keyword Tags: Tags are no longer simple keywords but knowledge units containing semantic information that can represent themes, concepts, entities, relationships, etc.
• Dynamic Tag Generation and Association: AI automatically generates semantic tags based on resource content and dynamically associates them with existing tagging systems.
  • Context Awareness: For example, in life logs, AI not only identifies "Work," "Life," "Entertainment" tags but can also dynamically adjust the semantics and weights of tags based on contextual situations (such as timestamps, locations, people, events) for more refined classification.
• Hierarchical and Relational Tagging: Tags can be organized into hierarchical structures (e.g., subject classification trees) and establish semantic relationships between tags (e.g., superordinate relationships, subordinate relationships, associative relationships), forming a dynamic knowledge network.
• Adaptive Evolution of Tags: As new resources are continuously added and knowledge systems evolve, the tagging system can adaptively adjust, automatically discovering new tags and optimizing the tag structure to maintain the vitality and relevance of the knowledge organization system.

2. Intelligent Integration of Unstructured Information and Structured Presentation:

• Multi-source Heterogeneous Data Fusion: The system can integrate unstructured information from different sources and formats (e.g., book reviews, user comments, meeting notes, research notes) and transform it into structured metadata.
• Intelligent Information Extraction and Relationship Building: AI extracts structured knowledge such as entities, relationships, and events from unstructured information and associates and integrates it with metadata records.
  • User Feedback Analysis: Analyzing user book reviews to extract themes, strengths, and weaknesses as supplementary information for metadata, enhancing the dimensionality of resource evaluation.
  • Meeting Record Analysis: Analyzing library procurement meeting records to extract resource demands, procurement reasons, decision processes, transforming them into procurement suggestions or decision bases.
• Visual Presentation of Structured Knowledge: The system can visually present structured metadata and knowledge networks, for example, through knowledge graphs, thematic networks, tag clouds, helping catalogers and users understand the knowledge structure and relationships of resources more intuitively.

User Interaction:

• Visual Tag Management: Adopting a visual tagging system similar to Tana Supertag, allowing catalogers to intuitively view, edit, and manage tags and explore the relationship network between tags.
• Dynamic Tag Suggestions and Adjustments: The system real-time suggests tags related to the current resource and allows catalogers to add, delete, or modify tags.
• Unstructured Information Drag-and-Drop Processing: Allows catalogers to drag unstructured text, voice, etc. to designated areas, and the system automatically parses and generates structured metadata or tags.
• Knowledge Network Exploration Interface: Provides a knowledge graph browser that allows catalogers and users to interactively explore the knowledge associations between resources and discover potential knowledge links.

III. Workflow Automation and Intelligent Commands (Corresponding to Tana: Command Buttons and Workflow Automation)

Core Function: Encapsulate complex metadata editing tasks into intelligent commands that can be executed with one click, significantly simplifying the cataloging process, improving efficiency, and lowering the threshold for using AI features.

1. Custom Intelligent Command Nodes (Command Node 2.0):

• Preset Command Library: The system provides commonly used metadata editing commands, such as:
  • "Generate Complete Metadata": Automatically fills in all possible metadata fields.
  • "Validate Metadata Standards": Checks whether metadata complies with MARC, RDA, etc., and automatically corrects errors.
  • "Generate Subject Term Suggestions": Generates a list of standardized subject term suggestions based on content analysis.
  • "Export as MARC21 Format": One-click export of metadata records in MARC21 format.
  • "Create Resource Association Records": Automatically identifies related resources referenced in the resource and creates association records.
• Custom Command Editing: Allows advanced users to customize more complex intelligent commands, such as:
  • Combined Commands: Combining multiple preset commands into more complex workflows (e.g., first "Generate Complete Metadata," then "Validate Metadata Standards," and finally "Export as MARC21 Format").
  • Script Commands: Supporting users to write simple scripts (e.g., Python scripts) to achieve more flexible metadata processing logic.
  • AI-driven Commands: Utilizing the large model's natural language understanding and generation capabilities, allowing users to create and execute commands through natural language descriptions (e.g., a user inputs "Help me translate the abstract of this book into English and add it to the metadata record").

2. Workflow Automation and Intelligent Assistant:

• Workflow Orchestration: Users can visually orchestrate metadata editing workflows, linking intelligent command nodes together to achieve automated processes.
• Intelligent Assistant and Task Recommendations: The system intelligently recommends possible intelligent commands or workflows that may need to be executed based on the user's work habits and the type of resource currently being edited.
• Background Automatic Task Execution: For certain time-consuming tasks (e.g., batch metadata processing), they can be set to execute automatically in the background, freeing catalogers from waiting time.

User Interaction:

• Visual Command Node Interface: Adopting a visual interface similar to Tana Command Node, allowing users to drag, connect, and configure command nodes to build workflows.
• One-click Execution and Progress Visualization: Clicking on a command node allows for one-click execution, with real-time display of task execution progress.
• Natural Language Command Input: Supporting users to create commands through natural language input, with the system automatically parsing and executing.
• Intelligent Assistant Panel: Displaying an intelligent assistant panel in the editor sidebar, recommending intelligent commands, workflows, and operational suggestions.

IV. Continuous Learning and Model Optimization (Surpassing Tana)

Core Function: Build a continuous learning and adaptive optimization AI model to ensure the system's performance continuously improves, better meeting the evolving needs of library metadata editing.

1. User Feedback-driven Model Optimization:

• Explicit Feedback: Catalogers can rate, comment, and correct AI's recommendations and command execution results, and the system collects this explicit feedback for model training and optimization.
• Implicit Feedback: The system automatically records catalogers' operational behaviors (e.g., which fields were modified, which commands were used) as implicit feedback for model behavior analysis and improvement.
• Online Learning of the Model: Utilizing the collected user feedback data, the AI model undergoes continuous online training and optimization, enhancing the model's accuracy, generalization, and personalized service capabilities.

2. Integration of Domain Knowledge Bases and Authoritative Data Sources:

• Knowledge Base Connection: The system can connect to the library's existing knowledge graphs, subject term lists, classification systems, authoritative data sources (VIAF, Wikidata), enhancing the AI's understanding and reasoning capabilities with domain knowledge.
• Knowledge Base Updates and Synchronization: Maintaining real-time synchronization with external knowledge bases to ensure that the knowledge used by the system is the latest and most authoritative.
• Knowledge Base-driven Metadata Generation: Utilizing structured knowledge and semantic relationships in the knowledge base to guide the metadata generation process, improving the quality and standardization of metadata.

3. Model Explainability and Transparency:

• Model Decision Explanation: For important AI decisions (e.g., classification number recommendations, subject term generation), the system can explain the reasoning process and basis of the model, enhancing user trust in AI.
• Model Version Control and Auditing: Implementing version control for the AI model, recording the iteration history of the model, and providing model auditing functions to ensure the system's stability and reliability.
• Transparent Algorithm Selection and Configuration: Allowing users to understand and select the AI algorithms used by the system and providing model parameter configuration options to enhance system transparency and customizability.

User Interaction:

• Feedback Collection Interface: Providing a simple and user-friendly feedback collection interface for catalogers to provide explicit feedback.
• Model Optimization Reports: Regularly generating model optimization reports to show users the improvements in model performance and directions for enhancement.
• Knowledge Base Browsing and Management Interface: Providing a knowledge base browsing and management interface for users to view and manage the integrated knowledge resources of the system.
• Model Configuration Interface: Providing a model configuration interface for advanced users to adjust model parameters and algorithm selections.

Summary:

The next-generation library resource description AI metadata editor is not just an upgrade of tools but a paradigm shift in library cataloging work. It liberates catalogers from tedious repetitive labor, allowing them to focus on higher-level knowledge organization, semantic association, and user service. By deeply integrating large models, it constructs an intelligent, dynamic, and adaptive metadata ecosystem, ultimately enhancing the discoverability, accessibility, and utilization of library resources, contributing significantly to the dissemination and innovation of knowledge. This is not merely an accumulation of functions but a profound insight into and forward-looking layout of the future development trends of libraries.

🧑‍💻 User#

The four aspects of the AI metadata editor designed here correspond to the initial summary points I provided about Tana's features. I hope to reorganize these four points according to the business process of resource description for better understanding.

Second Round of Thoughts#

Okay, I need to help the user reorganize the four functional aspects of their previously designed AI metadata editor according to the business process of library resource description to better align with actual workflows. The user previously referenced Tana notes' functional points and now hopes to organize them from a business process perspective, which may facilitate understanding and application.

First, I need to understand the typical process of library resource description. Generally, the library cataloging process includes resource acquisition, content analysis, metadata creation, structured processing, quality control, workflow management, and subsequent maintenance and optimization. Therefore, it may be necessary to map these functional points to these specific steps.

Next, reviewing the four design aspects previously provided by the user:

1. Intelligent content deep parsing and proactive metadata construction
2. Dynamic structuring and knowledge organization
3. Workflow automation and intelligent commands
4. Continuous learning and model optimization

These functions need to be reclassified into different stages of the business process. For example, content parsing belongs to the resource acquisition and content analysis stage, metadata creation involves field filling and validation, structured processing may correspond to dynamic tagging and knowledge graphs, workflow automation belongs to process management, and continuous learning pertains to later optimization and maintenance.

At the same time, the user mentioned Tana's four functional points: intelligent content generation, AI-assisted structured management, command buttons and automation, and Supertag's information integration. It is essential to ensure that the redesigned organization still encompasses these elements while prioritizing the business process.

It may be necessary to detail how each stage of the business process applies AI functions, such as multi-modal content understanding corresponding to resource parsing, intelligent filling and validation corresponding to metadata generation, dynamic tagging and knowledge graphs corresponding to structured processing, automated commands corresponding to workflow stages, and model learning and feedback mechanisms corresponding to the optimization stage.

It is also important to consider the user's actual usage scenarios, such as catalogers' pain points when handling resources: repetitive labor, inconsistent data quality, difficulties in structuring, and cumbersome workflows, ensuring that the redesigned organization can address these issues effectively.

Finally, ensure that the language is clear and the structure is logical, allowing readers to follow the business process step by step to understand how AI functions integrate into it, enhancing overall efficiency and quality.