Information fusion in taxonomic descriptions

Providing a single access point to an information system from multiple documents is helpful for biodiversity researchers as it is true in many fields. It not only saves the time for going back and forth from different sources but also provides the opportunity to generate new information out of the complementary information in different sources and levels of description. This paper investigates the potential of information fusion techniques in biodiversity area since the researchers in this domain desperately need information from different sources to verify their decision. In another sense, there are massive amounts of collections in this area. It is not easy or even possible for the researcher to manually collect information from different places. The proposed system contains 4 steps: Text segmentation and Taxonomic Name Identification, Organ-level and Sub-organ level Information Extraction, Relationship Identification, and Information fusion. Information fusion is based on the seven out of the twenty-four relationships in CST (Cross-document Sentence Theory). We argue that this kind of information fusion system might not only save the researchers the time for going back and forth from different sources but also provides the opportunity to generate new information out of the complementary information in different sources and levels.