The document discusses the evolving landscape of information organization, emphasizing the need for flexible classification systems to address the complexities of digital content and interdisciplinary knowledge areas. It highlights the role of ontologies in establishing interoperability among heterogeneous data sets, facilitating more efficient access and analysis of information across various domains. The conclusion underscores the importance of staying informed about the standards and technologies that enhance contextual relevancy and data accessibility.

1. In the world of information organization, we work very hard (some of us do) to describe knowledge objects, from the micro-granular metadata about metadata level to the grand structure of the institution the collection lives in, to the format the collection is wrapped in (digitally) for transmission or wrapped and labeled in for warehouse storage. We do this to extend historical appreciation or at least recognize the significance of the object and its context of space, time, and civilization, but also to give access points to those who want to come back to these information objects for scholarly study.<br />Forces Converging<br />Several emerging forces are present and gaining momentum today. Classification and indexing systems historically developed for print materials or for literary warrant of specific collections are showing limitations in the face of the exponential increase of digital content and the chaotic nature of a good deal of that content’s structure (or lack thereof). In response, more flexible standards and formats are developing and constantly changing to accommodate that digital force. The frequent need to migrate collections from one format to the next to maintain accessibility dictates planning, money and time executed carefully and mindfully in the context of our evolving digital universe.<br />Also, subjects are beginning to fuse together contextually as areas of knowledge become interdisciplinary and increasingly more complex (for example, the study of bio-informatics which pulls in a fusion of knowledge areas of biology, genetics, and computer science) and contextually dependent. Subject matter experts from each of these knowledge areas likely study components from different contexts or purposes. Experts from outside the main disciplines also have reasons to study facets for their fields. In the article Ontology: a tool for organization of knowledge by Pratibha A. Gokhale , the author emphasizes:<br />“The principle of facet analysis and relationship among individual foci form the basic philosophy of classification which has retained its importance due to the emergence of inter-disciplinary and multi-disciplinary knowledge areas. .. [These interoperable relationships are] nothing more than context dependency and an effort to know ‘What is what?’ Therefore ontology is to be revisited in the technology driven world even though its roots are in Philosophy. it is a collection of concepts arranged in a hierarchy of categories combined with the relationships between those concepts in order to reflect an area of knowledge” (Gokhale, 2009). <br />Subject experts from one subject discipline are now performing more complex information seeking research, targeting knowledge areas in a previously unrelated subject discipline to solve problems contextually relevant to their area of expertise. This scenario illustrates an unforeseen information access need that emerges from an atypical or not traditionally related subject discipline. As knowledge of contextual information and data sets expands, so does our need to be able to effectively access and analyze information for a variety of nontraditional and unanticipated applications.<br />Case Study<br />Institutional collections that physically stand apart but have overlapping context in their areas of knowledge are working to map these disparate collections so that a user can, via a single point of entry, study the meaning of these objects in context with one another or separately, but using either the controlled vocabulary of one or the other collection. One such study is documented in the article Semantic Web and Vocabulary Interoperability: an Experiment with Illumination Collections published in June 2009 in ICBC Vol 38 no 2. This case study illustrates the challenges of establishing semantic interoperability across two digital iconographic collections housed in two separate institutions in two separate countries and languages. Each collection has a native classification system at work that is nothing like the other. The main concept at work is one that we will see over and over again in this area – how to establish interoperability and interconnection with heterogeneous (mixed formats) metadata. Highly complex and professional thesauri, classification schemes, etc. have been established using a Knowledge Organization System (KOS) to describe and organize the collections. <br />One-to Many Relationships<br />To accomplish this type of organization in multidisciplinary knowledge areas, “a whole new method needs to be developed to semantically map the overlapping subject fields which are dominated by variety of forms of documents from where information can be obtained” (Gokhale, 2009). In contrast, in a non-complex subject environment, where subjects are clearly discrete and the information is uniformly well organized, using a one-to-one relationship like a keyword search gives us a satisfactory degree of precision in our result set.<br /> Gokhale explains that, today “In the complex and diffused information environment, a one-to-many relationship is needed at the indexing and concept mapping stage”. Our traditional classification and data retrieval tools serve well in simple and straightforward disciplines, but where knowledge areas are not “delineated and they are highly specialized” as is the case in our nuclear medicine example, “the one to one context is no longer sufficient to reflect a fusion of subjects. Thus in a complex world of information, devices indicating one-to-many contextual relationships among concepts becomes essential” (Gokhale, 2009).<br />Ontologies bridge gaps<br />To accomplish contextual relationships among concepts in heterogeneous data, Vocabularies (less specialized) or Ontologies (highly specialized) are built. Without the functionality that ontological software tools provides, interoperability between data sets cannot be accomplished (to date). The Semantic Web depends on Web Ontology Language (OWL) and standards for KOS to bridge those previously disconnected data sets. “The role of vocabularies on the Semantic Web are to help data integration when, for example, ambiguities may exist on the terms used in the different data sets, or when a bit of extra knowledge may lead to the discovery of new relationships” (W3C, 2010). In fact, the value promise that the standards being developed for Ontologies for use with the Semantic Web holds is to apply these emerging tools precisely in this way to analyze new relationships previously undiscovered in discrete and heterogeneous data sets. In other words, dynamic subject fusions dependent on context and meaning of meaning within that context is the direction that the Semantic Web and its dependency upon Vocabularies and Ontologies is taking us. <br />Conclusion<br />Though our tools may evolve rapidly (maybe not as rapidly as our content), our organizational roots and purpose remain constant. We derive, describe, infer index, classify and otherwise organize to provide access and contextual relevancy to the information objects at hand. Stay informed on the significant functions that Ontology and related standards and platforms play for increased contextual information access and data interoperability.<br />References <br />Angjeli, A., Isaac, A., Cloarec, T., Martin, F., van der Meij, L., Matthezing, H., et al. (2009). Semantic web and vocabulary interoperability: And experiment with illumination collections. International Cataloging and Bibliographic Control, 38(2), 25-26-29. Retrieved from http://vnweb.hwwilsonweb.com.proxycu.wrlc.org/hww/jumpstart.jhtml?recid=0bc05f7a67b1790e9ca96f741220c0027b62a94f2c81f71997123085d3634b18ad4b939162cbe391&fmt=H <br />Dublin Core Metadata Institute. Metadata basics. Retrieved April 20, 2010, from http://dublincore.org/metadata-basics/ <br />Gokhale, P. A. (2009). Ontology: A tool for organization of knowledge. Information Studies, 15(4), 233-242. Retrieved from HTML: http://vnweb.hwwilsonweb.com.proxycu.wrlc.org/hww/jumpstart.jhtml?recid=0bc05f7a67b1790e9ca96f741220c0027b62a94f2c81f719972f29d39ba34f945b4d28a7942a504e&fmt=H PDF: http://vnweb.hwwilsonweb.com.proxycu.wrlc.org/hww/jumpstart.jhtml?recid=0bc05f7a67b1790e9ca96f741220c0027b62a94f2c81f719972f29d39ba34f945b4d28a7942a504e&fmt=P <br />Sicilia, M. (2006). Metadata, semantics, and ontology: Providing meaning to information resources. International Journal of Metadata, Semantics and Ontologies, 1(1), 83-86. <br />Sicilia, M. (2006). Advances in digital information services and metadata research. Online Information Review, 30(3), 213-296. Retrieved from http://vnweb.hwwilsonweb.com.proxycu.wrlc.org/hww/jumpstart.jhtml?recid=0bc05f7a67b1790e9ca96f741220c0029c5f6d29d4ea75c98903dc0e56168a8a0bda39a6997dcaed&fmt=C <br />W3C. (2010). Ontologies. Retrieved April 20, 2010, from http://www.w3.org/standards/semanticweb/ontology <br />