Ranganathan’s Layers of Classification Theory and the FASDA Model of Classification

Describes four waves of Ranganathan’s dynamic theory of classification. Outlines components that distinguish each wave, and porposes ways in which this understanding can inform systems design in the contemporary environment, particularly with regard to interoperability and scheme versioning. Ends with an appeal to better understanding the relationship between structure and semantics in faceted classification schemes and similar indexing languages.

Emerging Concepts in Ontogenic Analysis

Subject ontogeny is the life of the subject in an indexing language (e.g., classification scheme like the DDC). Examining how a subject is treated over time tells us about the anatomy of an indexing language. For example, gypsies as a subject has been handled differently in different editions of the DDC.

Extending the Simple Knowledge Organization System for Concept Management in Vocabulary Development Applications

In this article, we describe the development of an exten- sion to the Simple Knowledge Organization System (SKOS) to accommodate the needs of vocabulary devel- opment applications (VDA) managing metadata schemes and requiring close tracking of change to both those schemes and their member concepts. We take a neo- pragmatic epistemic stance in asserting the need for an entity in SKOS modeling to mediate between the abstract concept and the concrete scheme. While the SKOS model sufficiently describes entities for modeling the current state of a scheme in support of indexing and search on the Semantic Web, it lacks the expressive power to serve the needs of VDA needing to maintain scheme historical continuity. We demonstrate prelimi- narily that conceptualizations drawn from empirical work in modeling entities in the bibliographic universe, such as works, texts, and exemplars, can provide the basis for SKOS extension in ways that support more rig- orous demands of capturing concept evolution in VDA.

Forms, Foundations, and the Ethos of Foremost: Zen Practice, Epistemology, and Ontology in Reflective Knowledge Organization

In reflecting on the practice of knowledge organization, we tacitly or explicitly root our conceptions of work and its value in some epistemic and ontological foundation. Zen Buddhist philosophy offers a unique set of conceptions vis-à-vis organizing, indexing, and describing documents.When we engage in knowledge organization, we are setting our mind to work with an intention. We intend to make some sort of intervention. We then create a form a realization of an abstraction (like classes or terms) [1], we do this from a foundation of some set of beliefs (epistemology, ontology, and ethics), and because we have to make decisions about what to privilege, we need to decide what is foremost in our minds. We must ask what is the most important thing?Form, foundation, and the ethos of foremost require evoke in our reflection on work number of ethical, epistemic, and ontological concerns that ripple throughout our conceptions of space, “good work”, aesthetics, and moral mandate [2,3]. We reflect on this.

Function, Purpose, Predication, and Context of Information Organization Frameworks

This paper outlines the purposes, predications, functions, and contexts of information organization frameworks; including: bibliographic control, information retrieval, resource discovery, resource description, open access scholarly indexing, personal information management protocols, and social tagging in order to compare and contrast those purposes, predications, functions, and contexts. Information organization frameworks, for the purpose of this paper, consist of information organization systems (classification schemes, taxonomies, ontologies, bibliographic descriptions, etc.), methods of conceiving of and creating the systems, and the work processes involved in maintaining these systems. The paper first outlines the theoretical literature of these information organization frameworks. In conclusion, this paper establishes the first part of an evaluation rubric for a function, predication, purpose, and context analysis.

Query routing in cooperative semi-structured peer-to-peer information retrieval networks

Conventional web search engines are centralised in that a single entity crawls and indexes the documents selected for future retrieval, and the relevance models used to determine which documents are relevant to a given user query. As a result, these search engines suffer from several technical drawbacks such as handling scale, timeliness and reliability, in addition to ethical concerns such as commercial manipulation and information censorship. Alleviating the need to rely entirely on a single entity, Peer-to-Peer (P2P) Information Retrieval (IR) has been proposed as a solution, as it distributes the functional components of a web search engine – from crawling and indexing documents, to query processing – across the network of users (or, peers) who use the search engine. This strategy for constructing an IR system poses several efficiency and effectiveness challenges which have been identified in past work. Accordingly, this thesis makes several contributions towards advancing the state of the art in P2P-IR effectiveness by improving the query processing and relevance scoring aspects of a P2P web search. Federated search systems are a form of distributed information retrieval model that route the user’s information need, formulated as a query, to distributed resources and merge the retrieved result lists into a final list. P2P-IR networks are one form of federated search in routing queries and merging result among participating peers. The query is propagated through disseminated nodes to hit the peers that are most likely to contain relevant documents, then the retrieved result lists are merged at different points along the path from the relevant peers to the query initializer (or namely, customer). However, query routing in P2P-IR networks is considered as one of the major challenges and critical part in P2P-IR networks; as the relevant peers might be lost in low-quality peer selection while executing the query routing, and inevitably lead to less effective retrieval results. This motivates this thesis to study and propose query routing techniques to improve retrieval quality in such networks. Cluster-based semi-structured P2P-IR networks exploit the cluster hypothesis to organise the peers into similar semantic clusters where each such semantic cluster is managed by super-peers. In this thesis, I construct three semi-structured P2P-IR models and examine their retrieval effectiveness. I also leverage the cluster centroids at the super-peer level as content representations gathered from cooperative peers to propose a query routing approach called Inverted PeerCluster Index (IPI) that simulates the conventional inverted index of the centralised corpus to organise the statistics of peers’ terms. The results show a competitive retrieval quality in comparison to baseline approaches. Furthermore, I study the applicability of using the conventional Information Retrieval models as peer selection approaches where each peer can be considered as a big document of documents. The experimental evaluation shows comparative and significant results and explains that document retrieval methods are very effective for peer selection that brings back the analogy between documents and peers. Additionally, Learning to Rank (LtR) algorithms are exploited to build a learned classifier for peer ranking at the super-peer level. The experiments show significant results with state-of-the-art resource selection methods and competitive results to corresponding classification-based approaches. Finally, I propose reputation-based query routing approaches that exploit the idea of providing feedback on a specific item in the social community networks and manage it for future decision-making. The system monitors users’ behaviours when they click or download documents from the final ranked list as implicit feedback and mines the given information to build a reputation-based data structure. The data structure is used to score peers and then rank them for query routing. I conduct a set of experiments to cover various scenarios including noisy feedback information (i.e, providing positive feedback on non-relevant documents) to examine the robustness of reputation-based approaches. The empirical evaluation shows significant results in almost all measurement metrics with approximate improvement more than 56% compared to baseline approaches. Thus, based on the results, if one were to choose one technique, reputation-based approaches are clearly the natural choices which also can be deployed on any P2P network.

Using linear algebra for protein structural comparison and classification.

In this article, we describe a novel methodology to extract semantic characteristics from protein structures using linear algebra in order to compose structural signature vectors which may be used efficiently to compare and classify protein structures into fold families. These signatures are built from the pattern of hydrophobic intrachain interactions using Singular Value Decomposition (SVD) and Latent Semantic Indexing (LSI) techniques. Considering proteins as documents and contacts as terms, we have built a retrieval system which is able to find conserved contacts in samples of myoglobin fold family and to retrieve these proteins among proteins of varied folds with precision of up to 80%. The classifier is a web tool available at our laboratory website. Users can search for similar chains from a specific PDB, view and compare their contact maps and browse their structures using a JMol plug-in.