Automatic Metadata Generation Charles Duncan

Nov 30, 2009Download as PPT, PDF0 likes516 views

Slides by Charles Duncan summarising the findings of the automatic metadata generation use cases project, see http://www.intrallect.com/wiki/index.php/AMG-UC

Findings of the Automatic Metadata Generation Use Cases project Charles Duncan [email_address]

What is metadata? Anything that aids the discovery and discrimination of resources known unknowns unknown unknowns too many results ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ?

Purposes of metadata Discovery (known unknowns) Discrimination (too many results) Recommendation (unknown unknowns)

Closer to reality Just-in-case Metadata Generation Other Information Archives Other Information Arc hives Other Information Archives Metadata Use Information Archive Deposit Discovery Metadata Generation Discrimination Recommendation Metadata Generation Just-in-time

In-case v. In-time Could require unreasonable processing times for a real-time service Allows great flexibility for new applications Just-in-time May create and store metadata which might never be used Efficient if metadata is created once and used many times Just-in-case Against For

Use case - depositor Researcher submits a paper for deposit in a repository. The PDF is analysed and keywords and classification suggested. File type and size are detected. Author and journal names are detected and checked and disambiguated against authoritative source. Page numbers and date of publication extracted. All these metadata fields completed automatically. Depositor puts paper into two “collections”. All references are identified and related to this paper.

Types of automatic metadata gen Automatic recognition and extraction services Key word extraction Automatic classification Basic facts (date, depositor, file type, file size, etc) Authoritative source services Name authority Journal title authority Translation services Conversion between metadata schemas Conversion between languages Metadata quality validation services Harvesting and validating metadata Activity aggregation services Usage (reading lists, library borrowing, search terms) Relationship services User-created collections Automatic term relation mapping (with “strength”)

Types of automatic metadata gen  Relationship services  Activity aggregation services  Metadata quality validation services   Translation services   Authoritative source services   Automatic recognition and extraction services Just-in-time Just-in-case

Reports Synthesis Report (Use Cases) Guidance Report (Tools) Recommendations Report (JISC only) Specialist reports Subject metadata Name metadata Geospatial metadata Factual metadata Bibliographic metadata Usage metadata File format metadata Integrating automatic metadata services http://www.intrallect.com/index.php/intrallect/knowledge_base/research_projects/automatic_metadata_generation_use_cases

The document discusses rethinking metadata to better connect scholarly works and enable transparency. It proposes three key areas: 1) Adding a new "Reviews" content type to link peer review assets like reports and responses. 2) Developing event data standards to aggregate metadata about publications and establish trust. 3) Citing data and software to provide proper credit and facilitate reproducibility. The goal is to improve infrastructure for scholarly discussion by making provenance, context and peer review processes more open and linked over time.

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This document provides an agenda and overview for an introduction to Crossref meeting. The agenda includes sessions on Crossref history and mission, DOIs and metadata, content on multiple sites, text and data mining, and administrative matters. Background information is given on Crossref's founding in 2000 with 12 publishers, current staff and governance structure, over 5500 publisher members representing over 85 million scholarly works, and services used by publishers, libraries, and other organizations. Growth statistics are shown and upcoming initiatives like linked clinical trials and a new website are highlighted.

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- CrossCheck has rebranded to Crossref Similarity Check to provide clearer messaging and reduce confusion. - The service checks documents against over 53 million papers from over 1200 publishers, as well as 105 million items from other sources and over 60 billion web pages. - Over 1200 Crossref publishers and over 100 Brazilian publishers are using the service, with increasing usage in countries like Japan, South Korea, and Turkey. - Publishers are looking to identify issues like poor references, self-plagiarism, unattributed use of others' works, and submitting others' works as their own through the similarity checking service.

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The *nervous system of insects* is a complex network of nerve cells (neurons) and supporting cells that process and transmit information. Here's an overview: Structure 1. *Brain*: The insect brain is a complex structure that processes sensory information, controls behavior, and integrates information. 2. *Ventral nerve cord*: A chain of ganglia (nerve clusters) that runs along the insect's body, controlling movement and sensory processing. 3. *Peripheral nervous system*: Nerves that connect the central nervous system to sensory organs and muscles. Functions 1. *Sensory processing*: Insects can detect and respond to various stimuli, such as light, sound, touch, taste, and smell. 2. *Motor control*: The nervous system controls movement, including walking, flying, and feeding. 3. *Behavioral responThe *nervous system of insects* is a complex network of nerve cells (neurons) and supporting cells that process and transmit information. Here's an overview: Structure 1. *Brain*: The insect brain is a complex structure that processes sensory information, controls behavior, and integrates information. 2. *Ventral nerve cord*: A chain of ganglia (nerve clusters) that runs along the insect's body, controlling movement and sensory processing. 3. *Peripheral nervous system*: Nerves that connect the central nervous system to sensory organs and muscles. Functions 1. *Sensory processing*: Insects can detect and respond to various stimuli, such as light, sound, touch, taste, and smell. 2. *Motor control*: The nervous system controls movement, including walking, flying, and feeding. 3. *Behavioral responses*: Insects can exhibit complex behaviors, such as mating, foraging, and social interactions. Characteristics 1. *Decentralized*: Insect nervous systems have some autonomy in different body parts. 2. *Specialized*: Different parts of the nervous system are specialized for specific functions. 3. *Efficient*: Insect nervous systems are highly efficient, allowing for rapid processing and response to stimuli. The insect nervous system is a remarkable example of evolutionary adaptation, enabling insects to thrive in diverse environments. The insect nervous system is a remarkable example of evolutionary adaptation, enabling insects to thrive

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Automatic Metadata Generation Charles Duncan

1. Findings of the Automatic Metadata Generation Use Cases project Charles Duncan [email_address]

2. Find and Seek Hide and Seek?

3. What is metadata? Anything that aids the discovery and discrimination of resources known unknowns unknown unknowns too many results ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ?

4. Purposes of metadata Discovery (known unknowns) Discrimination (too many results) Recommendation (unknown unknowns)

5. Simplistic View

6. Closer to reality Just-in-case Metadata Generation Other Information Archives Other Information Arc hives Other Information Archives Metadata Use Information Archive Deposit Discovery Metadata Generation Discrimination Recommendation Metadata Generation Just-in-time

7. In-case v. In-time Could require unreasonable processing times for a real-time service Allows great flexibility for new applications Just-in-time May create and store metadata which might never be used Efficient if metadata is created once and used many times Just-in-case Against For

8. Use case - student Student on history course gets reading list from VLE. Selects an article and is offered, additional information about geographic locations and historical characters mention in the article, list of other articles by same author that have the same highly ranked keywords, other articles that commonly appear on reading lists with this article and books borrowed by students of similar profile with matched keywords.

9. Use case - depositor Researcher submits a paper for deposit in a repository. The PDF is analysed and keywords and classification suggested. File type and size are detected. Author and journal names are detected and checked and disambiguated against authoritative source. Page numbers and date of publication extracted. All these metadata fields completed automatically. Depositor puts paper into two “collections”. All references are identified and related to this paper.

10. Types of automatic metadata gen Automatic recognition and extraction services Key word extraction Automatic classification Basic facts (date, depositor, file type, file size, etc) Authoritative source services Name authority Journal title authority Translation services Conversion between metadata schemas Conversion between languages Metadata quality validation services Harvesting and validating metadata Activity aggregation services Usage (reading lists, library borrowing, search terms) Relationship services User-created collections Automatic term relation mapping (with “strength”)

11. Types of automatic metadata gen  Relationship services  Activity aggregation services  Metadata quality validation services   Translation services   Authoritative source services   Automatic recognition and extraction services Just-in-time Just-in-case

12. Reports Synthesis Report (Use Cases) Guidance Report (Tools) Recommendations Report (JISC only) Specialist reports Subject metadata Name metadata Geospatial metadata Factual metadata Bibliographic metadata Usage metadata File format metadata Integrating automatic metadata services http://www.intrallect.com/index.php/intrallect/knowledge_base/research_projects/automatic_metadata_generation_use_cases

Editor's Notes

#3: “ open”

Automatic Metadata Generation Charles Duncan

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