Library influence on museum information work

Contemporary literature on the divergence of libraries, archives, and museums over the course of the twentieth century credits the rise of distinct professional practices required to handle different physical forms. This paper explores the extent that librarianship influenced museum information practices in a predigital era. Instead of divergence, I find examples where museums adapted library methods to fit their needs instead of developing their own set of professional practices. Because museum professionalization placed an emphasis on discipline-based university training, information work in museums has been incorporated into nonuniversity technical education and on-the-job training programs. That this divergence of information work from academic preparation has fallen along gender lines requires additional attention.published or submitted for publicationOpe

Sustaining Collection Value: Managing Collection/Item Metadata Relationships

Many aspects of managing collection/item metadata relationships are critical to sustaining collection value over time. Metadata at the collection-level not only provides context for finding, understanding, and using the items in the collection, but is often essential to the particular research and scholarly activities the collection is designed to support. Contemporary retrieval systems, which search across collections, usually ignore collection level metadata. Alternative approaches, informed by collection-level information, will require an understanding of the various kinds of relationships that can obtain between collection-level and item-level metadata. This paper outlines the problem and describes a project that is developing a logic-based framework for classifying collection-level/item-level metadata relationships. This framework will support (i) metadata specification developers defining metadata elements, (ii) metadata librarians describing objects, and (iii) system designers implementing systems that help users take advantage of collection-level metadata.Institute for Museum and Libary Services (Grant #LG06070020)published or submitted for publicationis peer reviewe

Premise Selection for Mathematics by Corpus Analysis and Kernel Methods

Smart premise selection is essential when using automated reasoning as a tool for large-theory formal proof development. A good method for premise selection in complex mathematical libraries is the application of machine learning to large corpora of proofs. This work develops learning-based premise selection in two ways. First, a newly available minimal dependency analysis of existing high-level formal mathematical proofs is used to build a large knowledge base of proof dependencies, providing precise data for ATP-based re-verification and for training premise selection algorithms. Second, a new machine learning algorithm for premise selection based on kernel methods is proposed and implemented. To evaluate the impact of both techniques, a benchmark consisting of 2078 large-theory mathematical problems is constructed,extending the older MPTP Challenge benchmark. The combined effect of the techniques results in a 50% improvement on the benchmark over the Vampire/SInE state-of-the-art system for automated reasoning in large theories.Comment: 26 page