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This paper is about searching literature digital libraries to find "related" publications of a given publication. Existing approaches do not take into account publication topics in the relatedness computation, allowing topic diffusion across query output publications. In this paper, we propose a new way to measure "relatedness" by incorporating "contexts" (representing topics) of publications. We utilize existing ontology terms as contexts for publications, i.e., publications are assigned to their relevant contexts, where a context characterizes one or more publication topics. We define three ways of context-based relatedness, namely, (a) relatedness between two contexts (context-to-context relatedness) by using publications that are assigned to the contexts and the context structures in the context hierarchy, (b) relatedness between a context and a paper (paper-to-context relatedness), which is used to rank the relatedness of contexts with respect to a paper, and (c) relatedness between two papers (paper-to-paper relatedness) by using both paper-to-context and context-to-context relatedness measurements. Using existing biomedical ontology terms as contexts for genomics-oriented publications, our experiments indicate that the context-based approach is accurate, and solves the topic diffusion problem by effectively classifying and ranking related papers of a given paper based on the selected contexts of the paper. [ABSTRACT FROM AUTHOR]

Search engines of main-stream literature digital libraries such as ACM Digital Library, Google Scholar, and PubMed employ file-based systems, and provide users with a basic boolean keyword search functionalities. As a result, new and powerful querying capabilities are not easy to implement on top of such systems, and not provided. In comparison, query languages of database systems traditionally have high expressive power. This paper evaluates the scalability of the approach of deploying relational databases as backend systems to digital libraries, and, thus, making use of the query languages and the query processing capabilities of database query engines for literature digital libraries. To evaluate our approach, we built a scalable prototype digital library built on top of a relational database management system, and its advanced query interface which allows users to specify dynamic text and path queries in an intuitive, hierarchical manner. This paper evaluates the scalability of two search query processing approaches, namely, ad-hoc queries, pre-compiled queries (stored-procedures). We demonstrate that, with reasonably priced hardware, we are able to build an RDBMS-based digital library search engine that can scale to handle millions of queries per day. Keywords: Scalability, Database, Metadata, Path Query, Query Interface. [ABSTRACT FROM AUTHOR]