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Week4: Informatics & the Foundation of Knowledge–R&R
Jinxuan Ma
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Read & React Posts (original posts due on 6/16; comments due on 6/18) 

General Guidelines for Writing Read, Reflect & React Posts:

(1) READ & REFLECT: Read the assigned number of articles on the reading list for this week. Post your reflections and critical comments on the course topic/s. For your post:(a) identify TWO main takeaways from this week’s readings/news report and (b) briefly explain why these resonated with you–these should be critical reflections and NOT summaries.

Discussion board postings must be substantive. Original (Read & Reflect) posts should be at least 300 words.  

(2) REACT: Post a substantive comment on another student’s posting for this week. These comments (React) posts must be substantive (cite authoritative sources and provide reasons for your position) and should be at least 100 words.

You may post more than one reply and continue the discussion if you wish. At the end of each post, list the articles/resources you chose to read for that week. Use APA 6th edition format for your citations.

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Week4 Reading List and Discussion Tasks: 

1. Week4 LecturePreview the documentView in a new window 

2. An Overview Video (Please only ignore the mismatched weekly timeline and my irrelevant work schedule from last fall. I would like to keep a video as it is instead compromising the quality by editing it.) 



2. Review online news 

3. Discussion Task :

select any FIVE of the following terms;
define each term with citations;
summarize your basic understanding by example/usage/application/implication of the term.
Note: Wikipedia can be a good starting place, however, your searches should go beyond Wikipedia, such as academic databases or organization websites. 
 1)  Data Analytics Models & Algorithms
 2)  Data Mining Algorithms
 3)  Digital Preservation 
 4)  Digital Repository
 5)  Folksonomy 
 6)  Information Architecture 
 7)  Information Governance
 8)  Information Management 
 9)  Information Organization 
10)  Information Representation 
11)  Information Retrieval 
12)  Information Science 
13)  Information Storage 
14)  Knowledge Management
15)  Machine Learning Algorithms
16)  Ontology (Information Science) 
17)  Semantic MediaWiki (SMW)
18)  Semantic Social Network
19)  Semantic Web
20)  Taxonomy 

4. Review your peers' postings.

Apologies in advance for the length, I used color coding to make the text (hopefully) easier to parse.....(I will post my RRR for an article in a second post later in the week).


**Digital Repository**
“A digital repository is a mechanism for managing and storing digital content. Repositories can be subject or institutional in their focus. Putting content into an institutional repository enables staff and institutions to manage and preserve it, and therefore derive maximum value from it. A repository can support research, learning, and administrative processes. Repositories use open standards to ensure that the content they contain is accessible in that it can be searched and retrieved for later use. The use of these agreed international standards allows mechanisms to be set up which import, export, identify, store and retrieve the digital content within the repository.

Digital repositories may include a wide range of content for a variety of purposes and users. What goes into a repository is currently less an issue of technological or software ability, and more a policy decision made by each institution or administrator. Typically content can include research outputs such as journal articles or research data, e-theses, e-learning objects and teaching materials, and administrative data. Some repositories only take in particular items (such as theses or journal papers), whilst others seek to gather any credible scholarly work produced by the institution; limited only by each author's retained rights from publishers. However, some more complex objects (websites, advanced learning objects, 3D topographical representations and other data sets) do present a technological challenge.”

 Repositories Support Project. (n.d.) What is a repository? Retrieved June 11, 2017 from http://www.rsp.ac.uk/start/before-you-start/what-is-a-repository/ (Links to an external site.)Links to an external site.

The Repositories Support Project (RSP) was a 7 year JISC-funded initiative contributing to building repository capacity, knowledge and skills within UK higher education institutions. Through providing guidance and advice, the RSP benefitted the whole of the UK sector and resulted in the wider take-up and development of institutional repositories in HEIs.

**Summary:** I chose this definition based on the source that provided it. Clearly this was a group with much expertise and experience in dealing with the ideas and practical realities of Digital Repositories.

Essentially, a digital repository is collecting digital objects – emails, datasets, software code, etc. (as well as the enabling technology tape drive, older computer, etc) – as opposed to a traditional archive which is collecting material objects like letters, ledgers, newspapers, paper records, etc. The is a gradient of institutional practice with many archives maintaining collections that consist of both kinds of evidence – whether its or bits.

**Knowledge Management**
“Knowledge Management, (KM) is a concept and a term that arose approximately two decades ago, roughly in 1990. Quite simply one might say that it means organizing an organization's information and knowledge holistically, but that sounds a bit wooly, and surprisingly enough, even though it sounds overbroad, it is not the whole picture. Very early on in the KM movement, Davenport (1994) offered the still widely quoted definition: "Knowledge management is the process of capturing, distributing, and effectively using knowledge."

This definition has the virtue of being simple, stark, and to the point.  A few years later, the Gartner Group created another second definition of KM, which is perhaps the most frequently cited one (Duhon, 1998): "Knowledge management is a discipline that promotes an integrated approach to identifying, capturing, evaluating, retrieving, and sharing all of an enterprise's information assets. These assets may include databases, documents, policies, procedures, and previously un-captured expertise and experience in individual workers."

Both definitions share a very organizational, a very corporate orientation. KM, historically at least, is primarily about managing the knowledge of and in organizations.”

Koenig, M. E. D. (May 4, 2012). What is KM? Knowledge management explained. Retrieved from http://www.kmworld.com/Articles/Editorial/What-Is-.../What-is-KM-Knowledge-Management-Explained-82405.aspx (Links to an external site.)Links to an external site.

KMWorld is the leading publisher, conference organizer, and information provider serving the knowledge management, content management, and document management markets.

**Summary:** I chose this definition because it comes from the primary journal in the field of Knowledge Management and because of the term itself, as is clear from the above definition, comes out of a business oriented conceptualization of how to manage knowledge in the enterprise environment. This helps historically situate the term as well as providing a contrast with the newer and more expansive term of Information Governance. Examples of Knowledge management systems include expert systems, intranets, and email systems.

**Machine Learning Algorithms**
“Machine learning is a type of artificial intelligence (AI) that provides computers with the ability to learn without being explicitly programmed. Machine learning focuses on the development of computer programs that can change when exposed to new data. 

The process of machine learning is similar to that of data mining. Both systems search through data to look for patterns. However, instead of extracting data for human comprehension -- as is the case in data mining applications -- machine learning uses that data to detect patterns in data and adjust program actions accordingly.  Machine learning algorithms are often categorized as being supervised or unsupervised. Supervised algorithms can apply what has been learned in the past to new data. Unsupervised algorithms can draw inferences from datasets.”

Rouse, M. (n.d.) Machine learning. Retrieved June 11, 2017 from http://whatis.techtarget.com/definition/machine-learning (Links to an external site.)Links to an external site.

WhatIs.com® is a reference and self-education tool about information technology. The site provides readers with definitions for over 10,000 terms and over 1,000 fast references, cheat sheets and quizzes.

For a list of actual machine learning algorithms follow this link: http://machinelearningmastery.com/a-tour-of-machine-learning-algorithms/ (Links to an external site.)Links to an external site. which categorizes and provides additional resources from learning more about particular algorithms and methodologies.

**Summary:** It was difficult to select a source for this definition because there are numerous sources that define the term from academic, corporate, and educational perspectives. The concept of machine learning algorithms has a history almost as long as the development of computers themselves. Alan Turing and John von Neumann are among the foundation thinkers who laid the theoretical and engineering approaches to creating non-programmed approaches to create teachable machines. As I essentially conceive it, machine learning algorithms use experiential training to create statistically “tuned” systems that can perform a variety of pattern recognition and response tasks. Facial recognition, textual analysis, and thousands of other tasks. This is an application of biological learning methodology to machines and as the methods have gotten closer to biological methods of learning, great progress has been made in the abilities of machine learning systems.

**Taxonomy**
“A taxonomy is typically a controlled vocabulary with a hierarchical structure, with the understanding that there are different definitions of a hierarchy. Terms within a taxonomy have relations to other terms within the taxonomy. These are typically: parent/broader term, child/narrower term, or often both if the term is at mid-level within a hierarchy.

Taxonomies are often displayed as a tree structure. Terms within a taxonomy are often called "nodes." A node may be repeated at more than one place within the taxonomy if it has multiple broader terms. This is referred to as a polyhierarchy.

Another type of taxonomy, with a more limited hierarchy, comprises multiple sub-taxonomies or "facets", whereby the top-level node of each represents a different type of taxonomy, attribute, or context. This is used on post-coordinated searching, whereby the user chooses a combination of nodes, one from each facet.

The term taxonomy tends to be used to refer to two different things:

a tree-hierarchical controlled vocabulary lacking more complex relationships found in thesauri or ontologies, or
any kind of controlled vocabulary, especially when applied to the world of enterprise content management and web site information architecture, rather than library science literature retrieval.”
Taxonomies & controlled vocabularies SIG. (n.d.) Taxonomies. Retrieved June 11, 2017 from http://www.taxonomies-sig.org/about.htm (Links to an external site.)Links to an external site.

A Special Interest Group of the American Society for Indexing The American Society for Indexing, Inc. (ASI) is a national association founded in 1968 to promote excellence in indexing and increase awareness of the value of well-written and well-designed indexes. A nonprofit educational and charitable organization, ASI serves indexers, librarians, abstractors, editors, publishers, database producers, data searchers, product developers, technical writers, academic professionals, researchers and readers, and others concerned with indexing. It is the only professional organization in the United States devoted solely to the advancement of indexing, abstracting and database construction.

**Summary:** For this definition and the following, I selected the definitions provided by the American Society for Indexing because of their expertise in developing explicit knowledge structures in both the analog and digital domains. Taxonomy is essentially a way of structuring representation elements in a classification system and creates relationships between representational elements. A taxonomy helps both to organize representational elements as well as creating finding relationships. Library classification systems are in the lineage of computer based taxonomies.

**Ontology (Information Science)**
“An ontology, like a thesaurus, is a kind of taxonomy with structure and specific types of relationships between terms. In an ontology, the types of relationships are greater in number and more specific in their function. Relationships could include, for example, located in to relate an organization to a place, produces/is produced by to relate a company and its product, and employs/employed by to relate a company and a person. Information, that in a simple controlled vocabulary or taxonomy is conveyed through indexing, is embedded into the ontology itself.

Ontological relationships are used in more complex information systems, such as the Semantic Web.”

Taxonomies & controlled vocabularies SIG. (n.d.) Ontologies. Retrieved June 11, 2017 from http://www.taxonomies-sig.org/about.htm (Links to an external site.)Links to an external site.

**Summary:** Developing from the philosophical domain, ontology is the study of being or existence. In the informatics context, ontology has become a way of coding meaning into collections of representational units so that they can be searched, organized, and traversed by machine algorithms to produce knowledge structures that are similar to human-structured classifications of knowledge. Essentially it is a form of scaffolding to allow computers to act as if they human. This is the realm of XML and metadata. This encoding is an essential part of many machine learning projects.

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One thing that struck me about performing this assignment, was how difficult it was to determine what resources offered authoritative definitions. Normally, I begin my research into topics with Wikipedia - quickly gaining a scope of conceptual boundaries, and vocabulary - and then look for books - followed by websites and articles - to get a further understanding of complex topics. For most of the terms I chose to define for this assignment, there was so much conceptual blending of commercial and academic meanings, that it was hard to decide which definition sources could be considered neutral.

This just highlighted (again!) that informatics is a very "tangled knot" of knowledge, institutions, and interests.

Cheers,

-B