Taxonomy is the practice and science of classification. Taxonomies, or taxonomic schemes, are composed of taxonomic units known as taxa (singular taxon), or kinds of things that are arranged frequently in a hierarchical structure.
A PowerPoint presentation about the difficulties of categorizing technical communication. It's not an easy thing to do, if the journals and textbooks in our own field don't consistently agree as to the major and minor categories. This PDF version of a PowerPoint presentation outlines the issues confronted by the EServer TC Library as it attempts to create a system of categories for its index of thousands of works in the fields of technical, scientific and professional communication.
Managing their knowledge assets is an imperative issue for most organizations in pursuit of competitive advantage in the knowledge-based economy. Previous researchers have proposed a number of valuable taxonomies for classifying an organization’s knowledge assets. However, once knowledge assets are classified by such taxonomies as a particular type, they do not change type over time. Arguably, however, business contexts are swiftly changing, and knowledge assets may have to be constantly adapted to play new roles, and so a taxonomy capable of reflecting the changing relations between knowledge assets and environmental conditions is needed. This article proposes such a taxonomy which utilizes durability and profitability as dimensions. This taxonomy allows knowledge assets to change type in the light of the new condition. Additionally, it has the characteristics of demonstrating the alignment of assets with organizational strategies, and of being widely applicable in the for-profit sector.
Ongoing attempts to define technical writing are inevitably confounded by problems caused by an excessively broad focus, which obscures the basis and usefulness of the definition, or by an excessively narrow focus, which arbitrarily-and sometimes oddly-relegates samples of writing as in or out of the realm of technical writing. Technical writers have been doing their jobs for far too long without a definition to be satisfied with a one- or two-sentence catch-all definition, and such a definition may result in dividing technical writing into two (or more) cultures.
When hundreds of people engage in content-generation and exchange, impressive results can happen — namely, you find a lot of interesting, accurate content. Writer River doesn’t have nearly enough community to be on par with these sites, but it’s a step in the right direction.
The debate within the Web community over the optimal means by which to organize information often pits formalized classifications against distributed collaborative tagging systems. A number of questions remain unanswered, however, regarding the nature of collaborative tagging systems including whether coherent categorization schemes can emerge from unsupervised tagging by users. This paper uses data from the social bookmarking site del.icio.us to examine the dynamics of collaborative tagging systems. In particular, we examine whether the distribution of the frequency of use of tags for 'popular' sites with a long history (many tags and many users) can be described by a power law distribution, often characteristic of what are considered complex systems. We produce a generative model of collaborative tagging in order to understand the basic dynamics behind tagging, including how a power law distribution of tags could arise. We empirically examine the tagging history of sites in order to determine how this distribution arises over time and to determine the patterns prior to a stable distribution. Lastly, by focusing on the high-frequency tags of a site where the distribution of tags is a stabilized power law, we show how tag co-occurrence networks for a sample domain of tags can be used to analyze the meaning of particular tags given their relationship to other tags.
Many Web professionals consider content inventories critical parts of most projects. Are there certain specific things to look for during a content inventory? Fred Leise definitely thinks so. He proposes a set of content analysis heuristics and discusses how to utilize each one.
Many organizations have turned to component-oriented content creation to create more sophisticated knowledge products, in more languages, and at lower cost. Our research shows that organizations that use XML authoring are more mature than their peers with respect to the adoption of best practices for search and metadata. However, the use of native DITA metadata capabilities is rare, and many are also missing out on opportunities to use taxonomy for content reuse and improved content findability. This article examines the metadata capabilities within DITA (and content management systems), discusses two major benefits that can be achieved by using descriptive metadata and taxonomy, and recommends some best practices for getting started with metadata for component-oriented content.
Sometimes, content has many attributes that have different importance to different users. A hierarchy assumes everyone approaches these attributes the same way, but that’s often not the case.
Categories are only useful if they meets the needs of the user. I can’t imagine that the variations of what I think of as “Science Fiction books” that were listed in the category are of any use to anyone.
Organizing and classifying a Web sites’ content when you’re developing its information architecture (IA) is one of the key activities you must undertake to deliver a usable site. Designing an information architecture to ensure users can reliably reach the information they want—and in less time—is the main focus of an information architect’s work. To accomplish this goal, information architects employ user-centered design methods, keeping users at a project’s center. Over the years, the design and development of user interfaces for products and services has evolved, resulting in design conventions and best practices that we follow when designing a user interface. However, following common practice can occasionally lead us astray. This article cautions you against following a common information-architecture practice that can have negative consequences in terms of costs: the creation of index pages that correspond to a single item in a category.
Ontology mapping is a key problem to be solved for the success of the Semantic Web and related technologies. An ontology mapping algorithm aims at finding correspondences (or mappings) between entities of the source and target ontologies by combining several matching components, i.e., individual matchers, that exploit one or more sources of information encoded within the ontologies. In this paper, we investigate linguistic techniques for ontology mapping and underline their importance in paving the way to other matching techniques. We define a general mapping model architecture and discuss an implementation in the Lucene ontology matcher (LOM). LOM leverages the features of the Lucene search engine library. The basic idea is to gather the different kinds of linguistic information of the source ontology entities in Lucene documents that will be stored into an index. Mappings are discovered by using the values of entities in the target ontology as search arguments against the index created from the source ontology. Extensive experimental results using a popular benchmark test suite show the effectiveness of this approach in terms of precision, recall, F-measure and execution time as compared to other linguistic approaches.
The purpose of this research is to compare several machine learning techniques on the task of Asian language text classification, such as Chinese and Japanese where no word boundary information is available in written text. The paper advocates a simple language modeling based approach for this task.
When working with government and large private organizations on complex information systems, project managers and business representatives often demand early-stage validation that the proposed classification system provides the user-friendly solution they are charged with delivering. They also require this validation in a format that will be engaging for senior business stakeholders.
Content organization is fascinating. The way a help author lays out the help topics in a table of contents shows you more than simply a list of topics. It shows you how the author has wrapped his or her mind around the content, how he or she has chosen to shape order from chaos. It shows you how the author understands the user. And it shows you one perspective on the structure of the content.
I mentioned that topic-based, hierarchical navigation, which is the standard for 95% of the help files I see, is becoming a tired, less-than-useful navigation system. We rely on this system too much as technical writers, and it’s not that helpful to users. Here are a few examples to demonstrate this.
Websites that provide content creation and sharing features have become quite popular recently. These sites allow users to categorize and browse content using ‘tags’ or free-text keyword topics. Since users contribute and tag social media content across a variety of social web platforms, creating
You can tag just about anything these days: vacation photos, products, blog posts, friends on Facebook... But what about that travel approval form you can never find on the intranet? Or the latest company annual report? While the popularity and application of social tagging has been on a continuous climb on the Web since the launch of del.ici.ous in 2003, the enterprise has been slower to adopt the trend. In the past few years, vendors—from niche companies like Connectbeam to large providers like IBM Lotus—have launched social tagging products aimed at the enterprise, hoping to capitalize on the growing curiosity around how this Web 2.0 approach can benefit the business world. But just what is social tagging in the enterprise: a cheap solution to information organization in difficult economic times, giving people the tools they love at home to use at work, or just a faddish attempt to incorporate "cool web 2.0 stuff" into the enterprise context?
In this paper, we describe an application, PubCloud that uses tag clouds for the summarization of results from queries over the PubMed database of biomedical literature. PubCloud responds to queries of this database with tag clouds generated from words extracted from the abstracts returned by the query. The results of a user study comparing the PubCloud tag-cloud summarization of query results with the standard result list provided by PubMed indicated that the tag cloud interface is advantageous in presenting descriptive information and in reducing user frustration but that it is less effective at the task of enabling users to discover relations between concepts.
A good taxonomy is a win for both a company and its customers. It’s easy to see why taxonomy development is good for your users: The whole reason for creating a taxonomy for your site is to make information retrieval quick and easy by putting the information into a sensible structure that’s consistently applied. Well-designed taxonomies map out the base structure for your content, providing a navigation scheme that makes sense to your users.
It recently struck me that people become trainers for very different reasons – and that this is reflected to varying degrees in the approach that they take to training. It might even be possible to define something like a ‘taxonomy of training types.’
In this article we present the Indiana Philosophy Ontology (InPhO) project as one of the first social-semantic web endeavors which aims to harness feedback from users who are unfamiliar with designing and populating ontologies to build a precise conceptual representation of a particular domain. This representation is initially a dynamically maintained taxonomy with several non-taxonomic relations. More complex components like class axioms and additional non-taxonomic relations will be iteratively added once more intricate reasoning techniques and a finer granularity of the representation is needed. Our approach combines statistical text processing methods, feed-back gathered through user interfaces, and logic programming approaches to create a dynamically changing, semantic representation of the discipline of philosophy. The long-term goal of the project is to build novel search and exploration applications on top of this populated representation. We describe the basic ideas and methodology of the project.
Traditional help authoring tools categorize resources through folders (a carryover from Windows folders), whereas web platforms typically use tags. Tags are actually a quick and easy way to attach metadata to any information object.
In a 2009 Computers and Composition article, I examined how the terms multimedia and multimodal were used in a variety of contexts, including academic and non-academic or industry situations. I argued that rather than the use of these terms being driven by any difference in their definitions, their use is more contingent upon the context and the audience to whom a particular discussion is being directed. I suggested that these differences can be best explained by understanding the differences in how texts are valued and evaluated in academic versus non-academic or industry contexts. This text extends that original argument to investigate the ways in which a variety of other terms, including digital media and new media, are defined and utilized by respected scholars in the fields of computers and composition and education. For this piece, I interviewed scholars who use these terms extensively in their teaching, scholarship, and administration. The conversations I had with them provided insight into how individual terms are defined by scholars who use them frequently. More importantly, these conversations laid the framework for a broader consideration of the anatomy of a definition: how we develop definitions and how definitions shape our work in academia, the classroom, and public life.