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oreChem ChemXSeer: a semantic digital library for chemistry (2010)
| Venue: | In Proc. JCDL 2010 |
| Citations: | 4 - 2 self |
BibTeX
@INPROCEEDINGS{Li10orechemchemxseer:,
author = {Na Li and Leilei Zhu and Prasenjit Mitra and Karl Mueller and Eric Poweleit and C. Lee Giles},
title = {oreChem ChemXSeer: a semantic digital library for chemistry},
booktitle = {In Proc. JCDL 2010},
year = {2010},
pages = {245--254}
}
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Abstract
Representing the semantics of unstructured scientific publications will certainly facilitate access and search and hopefully lead to new discoveries. However, current digital libraries are usually limited to classic flat structured metadata even for scientific publications that potentially contain rich semantic metadata. In addition, how to search the scientific literature of linked semantic metadata is an open problem. We have developed a semantic digital library oreChem ChemxSeer that models chemistry papers with semantic metadata. It stores and indexes extracted metadata from a chemistry paper repository ChemxSeer using “compound objects”. We use the Open Archives Initiative Object Reuse and Exchange (OAI-ORE) ) 1 standard to define a compound object that aggregates metadata fields related to a digital object. Aggregated metadata can be managed and retrieved easily as one unit resulting in improved ease-of-use and has the potential to improve the semantic interpretation of shared data. We show how metadata can be extracted from documents and aggregated using OAI-ORE. ORE objects are created on demand; thus, we are able to search for a set of linked metadata with one query. We were also able to model new types of metadata easily. For example, chemists are especially interested in finding information related to experiments in documents. We show how paragraphs containing experiment information in chemistry papers can be extracted and tagged based on a chemistry ontology with 470 classes, and then represented in ORE along with other document-related metadata. Our algorithm uses a classifier with features that are words that are typically only used to describe experiments, such as “apparatus”, “prepare”, etc. Using a dataset comprised of documents from the Royal Society of Chemistry digital library, we show that the our proposed method performs well in extracting experimentrelated paragraphs from chemistry documents.
Keyphrases
semantic digital library orechem chemxseer semantic metadata compound object rich semantic metadata open problem digital object semantic interpretation document-related metadata experiment information unstructured scientific publication ore object current digital library chemistry ontology chemistry paper repository chemxseer method performs new discovery chemistry digital library chemistry paper metadata field chemistry document semantic digital library orechem chemxseer royal society new type scientific publication scientific literature open archive initiative object reuse
