Deeper representation of digital information: From metadata to semantic data
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Deeper Representation of Digital Information: From Metadata to Semantic Data Jian Qin (moderator) School of Information Studies, Syracuse University, Syracuse, NY 13244 Email: [email protected] Elizabeth Liddy (Text-Mining for Automatic Metadata Generation) Center for Natural language Processing, School of Information Studies, Syracuse University, Syracuse, NY 13244. Email: [email protected] Terence R. Smith (A Strongly-Structured Model for Concepts in Digital Learning Environment) Center for Computational Modeling and Systems, Department of Computer Science, University of California Santa Barbara, Santa Barbara, CA 93806. E ma i I : smitht [email protected] Marcia Lei Zeng (A Study of the Interrelationships between Metadata Standards and Content Markup Languages in Representing and Organizing Knowledge in Digitized Collections) School of Library and Information Science, Kent State University, Kent, OH 44242- 0001. Email: mzeng @ kent .edu Metadata plays a critical role in bridging between information resources and search queries. As digital collections grow rapidly, several issues surrounding metadata have begun to emerge and affect the creation of metadata and its capabilities in providing wider and non- txaditional uses of digital information resources. One of the issues is automatic tagging and generation of metadata. For example, creating metadata records that abide by metadata standards is largely manual work in current practice. This process is very time consuming and it is difficult to keep pace with the growth of digital information. Another issue is related to content representation of digital resources. While metadata contains elements for content representation, the level of representation has not achieved much progress compared to that of print materials. The issue of access to deeper content in digital resources has been repeatedly raised, but no consensus has been reached concerning the appropriate level of granularity for such access. Furthermore, there is a need to examine the inter- relationships among the mechanisms used in description at the collection and item levels (as the material embodiment of information) and the ones used in representing the content of a work. Metadata standards and markup language standards developed by various disciplines represent two parallel efforts and rarely cross paths with one another. These issues signal a strong need for metadata to extend into semantic data that represent both conceptual and structural content in resources. This panel brings three speakers to address these issues. All three speakers are current principal or co-principal investigators of NSF National Science, Mathematics, Engineering, and Technology Education Digital Libraries NSDL projects. Liz Liddy of Syracuse University will report the research results from an NSDL project, in which she and her team have developed natural language-based software for automatically tagging text and generating metadata from these tagged documents. Terry Smith of University of California Santa Barbara will present a new approach to deeper representation- a strongly-structured model for concepts used in a DL-based environment designed to support learning in domains of scientific knowledge. Marcia Lei Zeng from Kent State will share the fundamental understanding of the functions and characteristics of metadata elements and markpp language elements, their inter-relationships, and the way they interact when used for describing and indicating information and its embodying documents. Each of these presentations represents a different approach to solving metadata and representation issues. Their findings and insights will provide valuable information for the audience and bring them up-to-date with frontier research in information and content representation. ASIST 2002 Punel 45 1
Transcript of Deeper representation of digital information: From metadata to semantic data
Deeper Representation of Digital Information: From Metadata to Semantic Data
Jian Qin (moderator) School of Information Studies, Syracuse University, Syracuse, NY 13244 Email: [email protected]
Elizabeth Liddy (Text-Mining for Automatic Metadata Generation) Center for Natural language Processing, School of Information Studies, Syracuse University, Syracuse, NY 13244. Email: [email protected]
Terence R. Smith (A Strongly-Structured Model for Concepts in Digital Learning Environment) Center for Computational Modeling and Systems, Department of Computer Science, University of California Santa Barbara, Santa Barbara, CA 93806. E ma i I : smith t r@ cs. ucs b .ed u
Marcia Lei Zeng (A Study of the Interrelationships between Metadata Standards and Content Markup Languages in Representing and Organizing Knowledge in Digitized Collections) School of Library and Information Science, Kent State University, Kent, OH 44242- 0001. Email: mzeng @ kent .edu
Metadata plays a critical role in bridging between information resources and search queries. As digital collections grow rapidly, several issues surrounding metadata have begun to emerge and affect the creation of metadata and its capabilities in providing wider and non- txaditional uses of digital information resources. One of the issues is automatic tagging and generation of metadata. For example, creating metadata records that abide by metadata standards is largely manual work in current practice. This process is very time consuming and it is difficult to keep pace with the growth of digital information. Another issue is related to content representation of digital resources. While metadata contains elements for content representation, the level of representation has not achieved much progress compared to that of print materials. The issue of access to deeper content in digital resources has been repeatedly raised, but no consensus has been reached concerning the appropriate level of granularity for such access. Furthermore, there is a need to examine the inter- relationships among the mechanisms used in description at the collection and item levels (as the material embodiment of information) and the ones used in representing the content of a work. Metadata standards and markup language standards developed by various disciplines represent two parallel efforts and rarely cross paths with one another. These issues signal a strong need for metadata to extend into semantic data that represent both conceptual and structural content in resources.
This panel brings three speakers to address these issues. All three speakers are current principal or co-principal investigators of NSF National Science, Mathematics, Engineering, and Technology Education Digital Libraries NSDL projects. Liz Liddy of Syracuse University will report the research results from an NSDL project, in which she and her team have developed natural language-based software for automatically tagging text and generating metadata from these tagged documents. Terry Smith of University of California Santa Barbara will present a new approach to deeper representation- a strongly-structured model for concepts used in a DL-based environment designed to support learning in domains of scientific knowledge. Marcia Lei Zeng from Kent State will share the fundamental understanding of the functions and characteristics of metadata elements and markpp language elements, their inter-relationships, and the way they interact when used for describing and indicating information and its embodying documents.
Each of these presentations represents a different approach to solving metadata and representation issues. Their findings and insights will provide valuable information for the audience and bring them up-to-date with frontier research in information and content representation.
ASIST 2002 Punel 45 1
