What is involved in Knowledge Base
Find out what the related areas are that Knowledge Base connects with, associates with, correlates with or affects, and which require thought, deliberation, analysis, review and discussion. This unique checklist stands out in a sense that it is not per-se designed to give answers, but to engage the reader and lay out a Knowledge Base thinking-frame.
How far is your company on its Knowledge Base journey?
Take this short survey to gauge your organization’s progress toward Knowledge Base leadership. Learn your strongest and weakest areas, and what you can do now to create a strategy that delivers results.
To address the criteria in this checklist for your organization, extensive selected resources are provided for sources of further research and information.
Start the Checklist
Below you will find a quick checklist designed to help you think about which Knowledge Base related domains to cover and 146 essential critical questions to check off in that domain.
The following domains are covered:
Knowledge Base, The Engine, Topic Maps, Gödel’s incompleteness theorems, Uniform resource identifier, Information Technology, Knowledge representation, Knowledge retrieval, Web Content Management, Description logic, Semantic matching, Authority control, Semantic analytics, Knowledge Base, Inference engine, A.I. Artificial Intelligence, Information storage, Dublin Core, General Problem Solver, Embedded RDF, Concurrent user, Internationalized resource identifier, Semantic network, Web Science Trust, Information repository, Knowledge engineering, Commonsense reasoning, Semantic triple, Common logic, Calculus ratiocinator, Knowledge Graph, Hilbert’s program, Snow Crash, Mind map, Application-Level Profile Semantics, Personal knowledge base, Linked data, Data Web, Library classification, Knowledge management, Alphabet of human thought, Logic programming, Commonsense knowledge, Question answering, Web 2.0, Semantic computing, Knowledge-based systems, Lotus Notes, Enterprise bookmarking, Text mining, Semantically-Interlinked Online Communities, Knowledge extraction, Semantic search:
Knowledge Base Critical Criteria:
Reason over Knowledge Base goals and explain and analyze the challenges of Knowledge Base.
– Does Knowledge Base include applications and information with regulatory compliance significance (or other contractual conditions that must be formally complied with) in a new or unique manner for which no approved security requirements, templates or design models exist?
– Do we support the certified Cybersecurity professional and cyber-informed operations and engineering professionals with advanced problem-solving tools, communities of practice, canonical knowledge bases, and other performance support tools?
– How do we ensure that implementations of Knowledge Base products are done in a way that ensures safety?
– Can specialized social networks replace learning management systems?
– Have you identified your Knowledge Base key performance indicators?
The Engine Critical Criteria:
Dissect The Engine leadership and tour deciding if The Engine progress is made.
– When the engineering team is satisfied, and pushes the new features to a full automation run, including load testing, how long does it take to declare the service ready to use?
– Are there any disadvantages to implementing Knowledge Base? There might be some that are less obvious?
– Is maximizing Knowledge Base protection the same as minimizing Knowledge Base loss?
– How does the organization define, manage, and improve its Knowledge Base processes?
– Where are the Engineers?
Topic Maps Critical Criteria:
Coach on Topic Maps strategies and balance specific methods for improving Topic Maps results.
– How can skill-level changes improve Knowledge Base?
– Who sets the Knowledge Base standards?
– What is our Knowledge Base Strategy?
Gödel’s incompleteness theorems Critical Criteria:
Inquire about Gödel’s incompleteness theorems planning and spearhead techniques for implementing Gödel’s incompleteness theorems.
– How do we make it meaningful in connecting Knowledge Base with what users do day-to-day?
– Will Knowledge Base deliverables need to be tested and, if so, by whom?
Uniform resource identifier Critical Criteria:
Adapt Uniform resource identifier failures and revise understanding of Uniform resource identifier architectures.
– What may be the consequences for the performance of an organization if all stakeholders are not consulted regarding Knowledge Base?
– Among the Knowledge Base product and service cost to be estimated, which is considered hardest to estimate?
– What is the source of the strategies for Knowledge Base strengthening and reform?
Information Technology Critical Criteria:
Confer re Information Technology outcomes and attract Information Technology skills.
– Does your company have defined information technology risk performance metrics that are monitored and reported to management on a regular basis?
– Do the response plans address damage assessment, site restoration, payroll, Human Resources, information technology, and administrative support?
– If a survey was done with asking organizations; Is there a line between your information technology department and your information security department?
– Who will be responsible for deciding whether Knowledge Base goes ahead or not after the initial investigations?
– How does new information technology come to be applied and diffused among firms?
– The difference between data/information and information technology (it)?
– When do you ask for help from Information Technology (IT)?
– How do we Lead with Knowledge Base in Mind?
Knowledge representation Critical Criteria:
Huddle over Knowledge representation projects and correct Knowledge representation management by competencies.
– What are our best practices for minimizing Knowledge Base project risk, while demonstrating incremental value and quick wins throughout the Knowledge Base project lifecycle?
– Does Knowledge Base analysis isolate the fundamental causes of problems?
– What business benefits will Knowledge Base goals deliver if achieved?
Knowledge retrieval Critical Criteria:
Consider Knowledge retrieval planning and document what potential Knowledge retrieval megatrends could make our business model obsolete.
– What sources do you use to gather information for a Knowledge Base study?
– What is Effective Knowledge Base?
Web Content Management Critical Criteria:
Design Web Content Management outcomes and clarify ways to gain access to competitive Web Content Management services.
– What are your most important goals for the strategic Knowledge Base objectives?
– Is Knowledge Base Realistic, or are you setting yourself up for failure?
Description logic Critical Criteria:
Chart Description logic strategies and plan concise Description logic education.
– What is the total cost related to deploying Knowledge Base, including any consulting or professional services?
– How do senior leaders actions reflect a commitment to the organizations Knowledge Base values?
Semantic matching Critical Criteria:
Investigate Semantic matching adoptions and describe the risks of Semantic matching sustainability.
– Does Knowledge Base systematically track and analyze outcomes for accountability and quality improvement?
– What new services of functionality will be implemented next with Knowledge Base ?
– How do we know that any Knowledge Base analysis is complete and comprehensive?
Authority control Critical Criteria:
Wrangle Authority control governance and inform on and uncover unspoken needs and breakthrough Authority control results.
– How do you incorporate cycle time, productivity, cost control, and other efficiency and effectiveness factors into these Knowledge Base processes?
– Does our organization need more Knowledge Base education?
– What are the long-term Knowledge Base goals?
Semantic analytics Critical Criteria:
Grade Semantic analytics adoptions and innovate what needs to be done with Semantic analytics.
– What is the purpose of Knowledge Base in relation to the mission?
– Do we have past Knowledge Base Successes?
Knowledge Base Critical Criteria:
Analyze Knowledge Base strategies and find the ideas you already have.
– Are accountability and ownership for Knowledge Base clearly defined?
– How to Secure Knowledge Base?
Inference engine Critical Criteria:
Categorize Inference engine governance and maintain Inference engine for success.
– When a Knowledge Base manager recognizes a problem, what options are available?
– Is there any existing Knowledge Base governance structure?
A.I. Artificial Intelligence Critical Criteria:
Air ideas re A.I. Artificial Intelligence leadership and acquire concise A.I. Artificial Intelligence education.
– What management system can we use to leverage the Knowledge Base experience, ideas, and concerns of the people closest to the work to be done?
– Who is the main stakeholder, with ultimate responsibility for driving Knowledge Base forward?
– Are we making progress? and are we making progress as Knowledge Base leaders?
Information storage Critical Criteria:
Boost Information storage strategies and drive action.
– What are the Key enablers to make this Knowledge Base move?
– Is Knowledge Base Required?
Dublin Core Critical Criteria:
Communicate about Dublin Core planning and stake your claim.
– Is there an organization-wide metadata standard, such as an extension of the dublin core, for use by search tools, multiple repositories, etc.?
– Has the semantic interoperability been ensured through schema mapping or using established standards like NISO, DoD, Dublin Core?
– How do we manage Knowledge Base Knowledge Management (KM)?
– How to deal with Knowledge Base Changes?
General Problem Solver Critical Criteria:
Probe General Problem Solver goals and find out what it really means.
– Does Knowledge Base analysis show the relationships among important Knowledge Base factors?
– Can we do Knowledge Base without complex (expensive) analysis?
Embedded RDF Critical Criteria:
Have a session on Embedded RDF governance and attract Embedded RDF skills.
– In the case of a Knowledge Base project, the criteria for the audit derive from implementation objectives. an audit of a Knowledge Base project involves assessing whether the recommendations outlined for implementation have been met. in other words, can we track that any Knowledge Base project is implemented as planned, and is it working?
Concurrent user Critical Criteria:
Disseminate Concurrent user engagements and correct better engagement with Concurrent user results.
– How do you determine the key elements that affect Knowledge Base workforce satisfaction? how are these elements determined for different workforce groups and segments?
– How can you negotiate Knowledge Base successfully with a stubborn boss, an irate client, or a deceitful coworker?
– How do we maintain Knowledge Bases Integrity?
Internationalized resource identifier Critical Criteria:
Interpolate Internationalized resource identifier tactics and gather Internationalized resource identifier models .
– Consider your own Knowledge Base project. what types of organizational problems do you think might be causing or affecting your problem, based on the work done so far?
– What prevents me from making the changes I know will make me a more effective Knowledge Base leader?
– How likely is the current Knowledge Base plan to come in on schedule or on budget?
Semantic network Critical Criteria:
Define Semantic network visions and plan concise Semantic network education.
– what is the best design framework for Knowledge Base organization now that, in a post industrial-age if the top-down, command and control model is no longer relevant?
– Do several people in different organizational units assist with the Knowledge Base process?
– What are the Essentials of Internal Knowledge Base Management?
Web Science Trust Critical Criteria:
Sort Web Science Trust results and adopt an insight outlook.
– Do we aggressively reward and promote the people who have the biggest impact on creating excellent Knowledge Base services/products?
– How will you know that the Knowledge Base project has been successful?
Information repository Critical Criteria:
Differentiate Information repository leadership and look in other fields.
– A compounding model resolution with available relevant data can often provide insight towards a solution methodology; which Knowledge Base models, tools and techniques are necessary?
– How can we improve Knowledge Base?
Knowledge engineering Critical Criteria:
Model after Knowledge engineering failures and work towards be a leading Knowledge engineering expert.
– Do we cover the five essential competencies-Communication, Collaboration,Innovation, Adaptability, and Leadership that improve an organizations ability to leverage the new Knowledge Base in a volatile global economy?
Commonsense reasoning Critical Criteria:
Have a round table over Commonsense reasoning leadership and research ways can we become the Commonsense reasoning company that would put us out of business.
– Meeting the challenge: are missed Knowledge Base opportunities costing us money?
– Which individuals, teams or departments will be involved in Knowledge Base?
Semantic triple Critical Criteria:
Nurse Semantic triple visions and find the essential reading for Semantic triple researchers.
– Why is it important to have senior management support for a Knowledge Base project?
Common logic Critical Criteria:
Apply Common logic decisions and observe effective Common logic.
– Do we monitor the Knowledge Base decisions made and fine tune them as they evolve?
– How do we go about Comparing Knowledge Base approaches/solutions?
Calculus ratiocinator Critical Criteria:
Jump start Calculus ratiocinator strategies and change contexts.
– How do we measure improved Knowledge Base service perception, and satisfaction?
Knowledge Graph Critical Criteria:
Test Knowledge Graph projects and improve Knowledge Graph service perception.
– What other jobs or tasks affect the performance of the steps in the Knowledge Base process?
– How is the value delivered by Knowledge Base being measured?
– How do we keep improving Knowledge Base?
Hilbert’s program Critical Criteria:
Have a meeting on Hilbert’s program engagements and define Hilbert’s program competency-based leadership.
– What will be the consequences to the business (financial, reputation etc) if Knowledge Base does not go ahead or fails to deliver the objectives?
Snow Crash Critical Criteria:
Focus on Snow Crash failures and budget the knowledge transfer for any interested in Snow Crash.
– What are our needs in relation to Knowledge Base skills, labor, equipment, and markets?
– How do we Improve Knowledge Base service perception, and satisfaction?
Mind map Critical Criteria:
Mix Mind map visions and separate what are the business goals Mind map is aiming to achieve.
– Do those selected for the Knowledge Base team have a good general understanding of what Knowledge Base is all about?
Application-Level Profile Semantics Critical Criteria:
Deliberate Application-Level Profile Semantics risks and frame using storytelling to create more compelling Application-Level Profile Semantics projects.
– Why are Knowledge Base skills important?
Personal knowledge base Critical Criteria:
Systematize Personal knowledge base decisions and correct Personal knowledge base management by competencies.
– What are the key elements of your Knowledge Base performance improvement system, including your evaluation, organizational learning, and innovation processes?
– Why should we adopt a Knowledge Base framework?
Linked data Critical Criteria:
Substantiate Linked data visions and find the essential reading for Linked data researchers.
– What role does communication play in the success or failure of a Knowledge Base project?
– Risk factors: what are the characteristics of Knowledge Base that make it risky?
Data Web Critical Criteria:
Pilot Data Web tasks and probe Data Web strategic alliances.
– What are the barriers to increased Knowledge Base production?
Library classification Critical Criteria:
Be responsible for Library classification adoptions and balance specific methods for improving Library classification results.
– Think about the people you identified for your Knowledge Base project and the project responsibilities you would assign to them. what kind of training do you think they would need to perform these responsibilities effectively?
Knowledge management Critical Criteria:
Substantiate Knowledge management governance and display thorough understanding of the Knowledge management process.
– Learning Systems Analysis: once one has a good grasp of the current state of the organization, there is still an important question that needs to be asked: what is the organizations potential for developing and changing – in the near future and in the longer term?
– Will new equipment/products be required to facilitate Knowledge Base delivery for example is new software needed?
– What are the best practices in knowledge management for IT Service management ITSM?
– What best practices in knowledge management for Service management do we use?
– When is Knowledge Management Measured?
– How is Knowledge Management Measured?
Alphabet of human thought Critical Criteria:
Reconstruct Alphabet of human thought governance and probe the present value of growth of Alphabet of human thought.
– What is our formula for success in Knowledge Base ?
Logic programming Critical Criteria:
Track Logic programming risks and transcribe Logic programming as tomorrows backbone for success.
– How important is Knowledge Base to the user organizations mission?
Commonsense knowledge Critical Criteria:
Ventilate your thoughts about Commonsense knowledge projects and summarize a clear Commonsense knowledge focus.
– In what ways are Knowledge Base vendors and us interacting to ensure safe and effective use?
Question answering Critical Criteria:
Set goals for Question answering issues and describe the risks of Question answering sustainability.
– What tools do you use once you have decided on a Knowledge Base strategy and more importantly how do you choose?
Web 2.0 Critical Criteria:
Familiarize yourself with Web 2.0 issues and secure Web 2.0 creativity.
– Who are the people involved in developing and implementing Knowledge Base?
– Do you monitor the effectiveness of your Knowledge Base activities?
Semantic computing Critical Criteria:
Scrutinze Semantic computing issues and intervene in Semantic computing processes and leadership.
– What are the business goals Knowledge Base is aiming to achieve?
– Are we Assessing Knowledge Base and Risk?
Knowledge-based systems Critical Criteria:
Disseminate Knowledge-based systems projects and look at the big picture.
– Does Knowledge Base create potential expectations in other areas that need to be recognized and considered?
Lotus Notes Critical Criteria:
Track Lotus Notes decisions and look in other fields.
– What are your results for key measures or indicators of the accomplishment of your Knowledge Base strategy and action plans, including building and strengthening core competencies?
Enterprise bookmarking Critical Criteria:
Confer re Enterprise bookmarking issues and explore and align the progress in Enterprise bookmarking.
– Which Knowledge Base goals are the most important?
Text mining Critical Criteria:
Check Text mining planning and pioneer acquisition of Text mining systems.
– Where do ideas that reach policy makers and planners as proposals for Knowledge Base strengthening and reform actually originate?
– Can Management personnel recognize the monetary benefit of Knowledge Base?
– What are our Knowledge Base Processes?
Semantically-Interlinked Online Communities Critical Criteria:
Add value to Semantically-Interlinked Online Communities visions and attract Semantically-Interlinked Online Communities skills.
Knowledge extraction Critical Criteria:
Reason over Knowledge extraction engagements and track iterative Knowledge extraction results.
– Is Knowledge Base dependent on the successful delivery of a current project?
Semantic search Critical Criteria:
Chat re Semantic search outcomes and differentiate in coordinating Semantic search.
– Does the Knowledge Base task fit the clients priorities?
This quick readiness checklist is a selected resource to help you move forward. Learn more about how to achieve comprehensive insights with the Knowledge Base Self Assessment:
Author: Gerard Blokdijk
CEO at The Art of Service | http://theartofservice.com
Gerard is the CEO at The Art of Service. He has been providing information technology insights, talks, tools and products to organizations in a wide range of industries for over 25 years. Gerard is a widely recognized and respected information expert. Gerard founded The Art of Service consulting business in 2000. Gerard has authored numerous published books to date.
To address the criteria in this checklist, these selected resources are provided for sources of further research and information:
Knowledge Base External links:
Welcome to the BroadCloud Knowledge Base
Star2Star Communications Knowledge Base
Constant Contact Knowledge Base
The Engine External links:
The Engine Shed
The Engine Room
GoSwiff | The engine behind payments
Topic Maps External links:
Topic Maps: Adopting User-Centred Indexing Technologies …
An Introduction to Topic Maps – msdn.microsoft.com
Gödel’s incompleteness theorems External links:
Gödel’s Incompleteness Theorems (Stanford …
Uniform resource identifier External links:
Uniform Resource Identifier (URI) Schemes
Uniform Resource Identifier (URI) list – 3GPP
Information Technology External links:
OHIO: Office of Information Technology |About Email
Umail | University Information Technology Services
SOLAR | Division of Information Technology
Knowledge representation External links:
Semantic Knowledge Representation – SemRep
Web Content Management External links:
Ingeniux CMS Web Content Management Software Log-in
Web Content Management | Hannon Hill Corp.
Ingeniux CMS Web Content Management Software Log-in
Description logic External links:
Description logic rules (Book, 2010) [WorldCat.org]
Description logic rules (eBook, 2010) [WorldCat.org]
Description Logic Rules – korrekt.org
Authority control External links:
Authority Control Task Force | OhioLINK
Authority Control | FIU Libraries
Semantic analytics External links:
SciBite – The Semantic Analytics Company
Knowledge Base External links:
Star2Star Communications Knowledge Base
Constant Contact Knowledge Base
Support : Knowledge Base
Inference engine External links:
Utilization Of Inference Engine Technology For Navy …
[PDF]An Expert Inference Engine for Generation of …
A.I. Artificial Intelligence External links:
A.I. Artificial Intelligence (2001) – Rotten Tomatoes
A.I. Artificial Intelligence (2001) – IMDb
Information storage External links:
Home – HigherGround – Information Storage
[PDF]Information Storage and Management—Storing, …
[PDF]Information Storage and Disposal Policy – WA Health
Dublin Core External links:
[PDF]Dublin Core Metadata – Minnesota
https://mn.gov/showcase/assets/DC Metadata Training_tcm12-247842.pdf
ERIC – Keeping Dublin Core Simple: Cross-Domain …
Dublin Core and the Cataloguing Rules: Example 7
General Problem Solver External links:
GPS: General Problem Solver – Instructional Design
General Problem Solver | computer model | Britannica.com
General Problem Solver ver.01 『実践Common Lisp』より
Concurrent user External links:
Concurrent User Dashboard (207807) – Quest Support
Internationalized resource identifier External links:
Internationalized Resource Identifier (The Java™ …
Semantic network External links:
[PDF]UMLS 2005AA – Semantic Network UMLS Semantic …
ERIC – The Semantic Network Model of Creativity: …
The UMLS Semantic Network
Web Science Trust External links:
Web Science Trust – YouTube
Web Science Trust – Infogalactic: the planetary knowledge …
Web Science Trust – Home | Facebook
Information repository External links:
DoDMERB Secure Applicant Information Repository – …
Paul Swoyer Septics — Information Repository
Payment Information Repository (PIR)
Knowledge engineering External links:
Knowledge Engineering Conference 2017 | Technology …
KNOWLEDGE ENGINEERING CORPORATION-Home …
What is Knowledge Engineering | IGI Global
Commonsense reasoning External links:
Commonsense Reasoning – (Second Edition) – …
ERIC – Commonsense Reasoning about the Physical …
Commonsense Reasoning – ScienceDirect
Common logic External links:
Common Logic – Home | Facebook
Logic – Common Logic / Midnight Marauder – YouTube
Knowledge Graph External links:
Introducing the Knowledge Graph – YouTube
Stardog—The Knowledge Graph Platform for the Enterprise
wizdom.ai – the world’s largest research knowledge graph
Hilbert’s program External links:
Hilbert’s Program – Detlefsen, Michael – 9789027721518 | …
Hilbert’s program then and now – Philsci-Archive
Did the Incompleteness Theorems Refute Hilbert’s Program?
Snow Crash External links:
Thomas and Snow Crash. – Roblox
Snow Crash – Lexile® Find a Book | MetaMetrics Inc.
Neal Stephenson – Snow Crash
Mind map External links:
How To Use A Mind Map – YouTube
[PDF]FREE Mind Map Templates – Northern Virginia …
Get M8! – Mind Map – Microsoft Store
Application-Level Profile Semantics External links:
ALPS means Application-Level Profile Semantics
Application-Level Profile Semantics · GitHub
Personal knowledge base External links:
Slant – 9 best personal Knowledge Base apps as of 2017
Linked data External links:
Tim Berners-Lee: The next Web of open, linked data – …
Linked Data for Libraries – YouTube
Data Web External links:
MHEC Secure Data Web Home
PARCEL VIEWER | Best California property data web map.
Library classification External links:
U.S. Geological Survey Library Classification System
ERIC – Functions of Library Classification., 1988
Knowledge management External links:
Knowledge Management Consulting Firm | Iknow LLC
A hub for technical knowledge management. — NDCAC
tealbook | Supplier Discovery, Knowledge Management …
Alphabet of human thought External links:
Gottfried Y. Leibniz – Alphabet of Human Thought – YouTube
Logic programming External links:
[PDF]Introduction to Logic Programming
http://www.eng.ucy.ac.cy/theocharides/Courses/ECE317/Logic Programming 1.pdf
Logic programming (eBook, 1991) [WorldCat.org]
Logic programming (Book, 1991) [WorldCat.org]
Question answering External links:
Myth Busting, Question Answering, and Publishing …
IndianAsk.com : Question Answering Service
Web 2.0 External links:
What Is Web 2.0? – CBS News
Pasadena Bar Association – Web 2.0
web 2.0 lawyer
Semantic computing External links:
Yactraq – Speech-based Semantic Computing
Knowledge-based systems External links:
Knowledge-Based Systems – ScienceDirect
Lotus Notes External links:
How to Configure the Lotus Notes Client
[PDF]Lotus Notes Demo – Topaz Systems
Text mining External links:
Text Mining / Text Analytics Specialist – bigtapp
Text Mining – AbeBooks
Text Mining in R: A Tutorial – Springboard Blog
Knowledge extraction External links:
Text Analytics & Knowledge Extraction – Lymba Corporation
Semantic search External links:
Open Semantic Search: Your own search engine for …
Semantic Search Marketing by Hunch Manifest
Google Semantic Search – Google+