Biologically Motivated Distributed Designs for Adaptive Knowledge Management

We discuss how distributed designs that draw from biological network metaphors can largely improve the current state of information retrieval and knowledge management of distributed information systems. In particular, two adaptive recommendation systems named TalkMine and @ApWeb are discussed in more detail. TalkMine operates at the semantic level of keywords. It leads different databases to learn new and adapt existing keywords to the categories recognized by its communities of users using distributed algorithms. @ApWeb operates at the structural level of information resources, namely citation or hyperlink structure. It relies on collective behavior to adapt such structure to the expectations of users. TalkMine and @ApWeb are currently being implemented for the research library of the Los Alamos National Laboratory under the Active Recommendation Project. Together they define a biologically motivated information retrieval system, recommending simultaneously at the level of user knowledge categories expressed in keywords, and at the level of individual documents and their associations to other documents. Rather than passive information retrieval, with this system, users obtain an active, evolving interaction with information resources.Comment: To appear in Design Principles for the Immune System and Other Distributed Autonomous Systems. i. Cohen and L. Segel (Eds.). Oxford University Pres

Protein annotation as term categorization in the gene ontology using word proximity networks

We addressed BioCreAtIvE Task 2, the problem of annotation of a protein with a node in the Gene Ontology (GO). We approached the task as a problem of categorizing terms derived from the document neighborhood of the given protein in the given document into nodes in the GO based on the lexical overlaps with terms on GO nodes and terms identified as related to those nodes. The system incorporates NLP components such as a morphological normalizer, a named entity recognizer, a statistical term frequency analyzer, and an unsupervised method for expanding words associated with GO ids based on a probability measure that captures word proximity (Rocha, 2002). The categorization methodology uses our novel Gene Ontology Categorizer (GOC) methodology (Joslyn et al. 2004) to select GO nodes as cluster heads for the terms in the input set based on the structure of the GO. Pre-processing Swiss-Prot and TrEMBL IDs were provided as input identifiers for the protein, so we needed to establish a set of names by which that protein could be referenced in the text. We made use of both the gene name and protein names that are in Swiss-Prot itself, when available, and a collection of synonyms constructed by Procter & Gamble Company. The fallback case was to us

Biologically Motivated Distributed Designs for Adaptive Knowledge Management

We discuss how distributed designs that draw from biological network metaphors can largely improve the current state of information retrieval and knowledge management of distributed information systems. In particular, two adaptive recommendation systems named TalkMine and @ApWeb are discussed in more detail. TalkMine operates at the semantic level of keywords. It leads different databases to learn new and adapt existing keywords to the categories recognized by its communities of users using distributed algorithms