Jane Greenberg

Metadata extraction and harvesting: A comparison of two automatic metadata generation applications

Abstract This research explores the capabilities of two Dublin Core automatic metadata generation applications, Klarity and DC-dot. The top level Web page for each resource, from a sample of 29 resources obtained from National Institute of Environmental Health Sciences (NIEHS), was submitted to both generators. Results indicate that extraction processing algorithms can contribute to useful automatic metadata generation. Results also indicate that harvesting metadata from META tags created by humans can have a positive impact on automatic metadata generation. The study identifies several ways in which automatic metadata generation applications can be improved and highlights several important areas of research. The conclusion is that integrating extraction of harvesting methods will be the best approach to creating optimal metadata, and more research is needed to identify when to apply which method.

Functionalities for automatic metadata generation applications: a survey of metadata experts' opinions

This paper reports on the automatic metadata generation applications (AMeGA) projects metadata expert survey. Automatic metadata generation research is reviewed and the studys methods, key findings and conclusions are presented. Participants anticipate greater accuracy with automatic techniques for technical metadata (e.g., ID, language, and format metadata) compared to metadata requiring intellectual discretion (e.g., subject and description metadata). Support for implementing automatic techniques paralleled anticipated accuracy results. Metadata experts are in favour of using automatic techniques, although they are generally not in favour of eliminating human evaluation or production for the more intellectually demanding metadata. Results are incorporated into Version 1.0 of the Recommended Functionalities for automatic metadata generation applications (Appendix A).

Who is an open source software developer

Profiling a community of Linux developers.

Automatic query expansion via lexical-semantic relationships

Structured thesauri encode equivalent, hierarchical, and associative relationships and have been developed as indexing/retrieval tools. Despite the fact that these tools provide a rich semantic network of vocabulary terms, they are seldom employed for automatic query expansion (QE) activities. This article reports on an experiment that examined whether thesaurus terms, related to query in a specified semantic way (as synonyms and partial-synonyms (SYNs), narrower terms (NTs), related terms (RTs), and broader terms (BTs)), could be identified as having a more positive impact on retrieval effectiveness when added to a query through automatic QE. The research found that automatic QE via SYNs and NTs increased relative recall with a decline in precision that was not statistically significant, and that automatic QE via RTs and BTs increased relative recall with a decline in precision that was statistically significant. Recall-based and a precision-based ranking orders for automatic QE via semantically encoded thesauri terminology were identified. Mapping results found between end-user query terms and the ProQuest® Controlled Vocabulary (1997) (the thesaurus used in this study) are reported, and future research foci related to the investigation are discussed.

Understanding Metadata and Metadata Schemes

SUMMARY Although the development and implementation of metadata schemes over the last decade has been extensive, research examining the sum of these activities is limited. This limitation is likely due to the massive scope of the topic. A framework is needed to study the full extent of, and functionalities supported by, metadata schemes. Metadata schemes developed for information resources are analyzed. To begin, the author presents a review of the definition of metadata, metadata functions, and several metadata typologies. Next, a conceptualization for metadata schemes is presented. The emphasis is on semantic container-like metadata schemes (data structures). The last part of this paper introduces the MODAL (Metadata Objectives and principles, Domains, and Architectural Layout) framework as an approach for studying metadata schemes. The paper concludes with a brief discussion on the value of frameworks for examining metadata schemes, including different types of metadata schemes.

A Metadata Best Practice for a Scientific Data Repository

Digital data repositories ought to support immediate operational needs and long-term project goals. This paper presents the Dryad repositorys metadata best practice balancing of these two needs. The paper reviews background work exploring the meaning of science, characterizing data, and highlighting data curation metadata challenges. The Dryad repository is introduced, and the initiatives metadata best practice and underlying rationales are described. Dryads metadata approach includes two prongs: one addressing the long-term goal to align with the Semantic Web via a metadata application profile; and another addressing the immediate need to make content available in DSpace via an extensible markup language (XML) schema. The conclusion summarizes limitations and advantages of the two prongs underlying Dryads metadata effort.

Open source software development and Lotka's law: bibliometric patterns in programming

This research applies Lotkas Law to metadata on open source software development. Lotkas Law predicts the proportion of authors at different levels of productivity. Open source software development harnesses the creativity of thousands of programmers worldwide, is important to the progress of the Internet and many other computing environments, and yet has not been widely researched. We examine metadata from the Linux Software Map (LSM), which documents many open source projects, and Sourceforge, one of the largest resources for open source developers. Authoring patterns found are comparable to prior studies of Lotkas Law for scientific and scholarly publishing. Lotkas Law was found to be effective in understanding software development productivity patterns, and offer promise in predicting aggregate behavior of open source developers.

Using BM25F for semantic search

Information Retrieval (IR) approaches for semantic web search engines have become very populars in the last years. Popularization of different IR libraries, like Lucene, that allows IR implementations almost out-of-the-box have make easier IR integration in Semantic Web search engines. However, one of the most important features of Semantic Web documents is the structure, since this structure allow us to represent semantic in a machine readable format. In this paper we analyze the specific problems of structured IR and how to adapt weighting schemas for semantic document retrieval.

Theoretical Considerations of Lifecycle Modeling: An Analysis of the Dryad Repository Demonstrating Automatic Metadata Propagation, Inheritance, and Value System Adoption

The Dryad repository is for data supporting published research in the field of evolutionary biology and related disciplines. Dryad development team members seek a theoretical framework to aid communication about metadata issues and plans. This article explores lifecycle modeling as a theoretical framework for understanding metadata in the repository environment. A background discussion reviews the importance of theory, the status of a metadata theory, and lifecycle concepts. An analysis draws examples from the Dryad repository demonstrating automatic propagation, metadata inheritance, and value system adoption, and reports results from a faceted term mapping experiment that included 12 vocabularies and approximately 600 terms. The article also reports selected key findings from a recent survey on the data-sharing attitudes and behaviors of nearly 400 evolutionary biologists. The results confirm the applicability of lifecycle modeling to Dryads metadata infrastructure. The article concludes that lifecycle modeling provides a theoretical framework that can enhance our understanding of metadata, aid communication about the topic of metadata in the repository environment, and potentially help sustain robust repository development.

Augmenting memory for student learning: Designing a context‐aware capture system for biology education

The Memex Metadata for Student Portfolios (M2) project is using mobile technology to augment student memory and improve student learning. We have constructed a student-targeted Context Awareness Framework (CAF) and we are developing a metadata scheme that integrates the CAF with a variety of mobile technologies. In particular, we are exploring the use of Microsoft SenseCams, which capture images and sensory data approximately every 90 seconds and can extend student memory, enabling for an enriched learning experience for undergraduate biology students. We are exploring the use of SenseCams along with other mobile devices (e.g., a GIS and Tablet PC) for biology students conducting scientific field work such as specimen identification. The development of our CAF and metadata scheme also support the development of e-portfolios that can extend student memory and maintain useful records of educational activities. This paper presents research and development activities underlying the M2 project, including research methods, evaluation activities, and next steps.