Top 160 Knowledge Base Goals and Objectives Questions

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 160 essential critical questions to check off in that domain.

The following domains are covered:

Knowledge Base, Information repository, Web Content Management, Wolfram Alpha, Knowledge engineering, Personal knowledge base, Tractatus Logico-Philosophicus, RDF Schema, Rule-based system, Semantic broker, Collective intelligence, Information storage, Knowledge retrieval, Library 2.0, Internationalized resource identifier, Hilbert’s program, Topic Maps, Knowledge Graph, A.I. Artificial Intelligence, Hypertext Transfer Protocol, Snow Crash, Linked data, General Problem Solver, Commonsense knowledge, Information architecture, A Logic Named Joe, Semantic matching, Authority control, Expert systems, Knowledge Base, Semantic analytics, Semantic Web, Unstructured information, Embedded RDF, Mind map, Data Web, Information Technology, Library classification, Semantic reasoner, Facebook Platform, Question answering, Knowledge-based system, Characteristica universalis, Description logic, Common logic, Rule Interchange Format, Text mining, Web engineering, Semantic service-oriented architecture, Automated reasoning, Inference engine, Versant Object Database, Application-Level Profile Semantics, Knowledge-based systems, Dublin Core, Knowledge representation and reasoning:

Knowledge Base Critical Criteria:

Design Knowledge Base management and interpret which customers can’t participate in Knowledge Base because they lack skills.

– 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?

– Think about the kind of project structure that would be appropriate for your Knowledge Base project. should it be formal and complex, or can it be less formal and relatively simple?

– 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?

– Are there Knowledge Base Models?

Information repository Critical Criteria:

Analyze Information repository outcomes and overcome Information repository skills and management ineffectiveness.

– Which customers cant participate in our Knowledge Base domain because they lack skills, wealth, or convenient access to existing solutions?

– What are your most important goals for the strategic Knowledge Base objectives?

– Who needs to know about Knowledge Base ?

Web Content Management Critical Criteria:

Be clear about Web Content Management leadership and know what your objective is.

– Do those selected for the Knowledge Base team have a good general understanding of what Knowledge Base is all about?

– Does Knowledge Base systematically track and analyze outcomes for accountability and quality improvement?

– How likely is the current Knowledge Base plan to come in on schedule or on budget?

Wolfram Alpha Critical Criteria:

Adapt Wolfram Alpha decisions and get out your magnifying glass.

– How will you measure your Knowledge Base effectiveness?

– Do we all define Knowledge Base in the same way?

– How do we keep improving Knowledge Base?

Knowledge engineering Critical Criteria:

Adapt Knowledge engineering failures and look for lots of ideas.

– Record-keeping requirements flow from the records needed as inputs, outputs, controls and for transformation of a Knowledge Base process. ask yourself: are the records needed as inputs to the Knowledge Base process available?

– Do the Knowledge Base decisions we make today help people and the planet tomorrow?

– Is the Knowledge Base organization completing tasks effectively and efficiently?

Personal knowledge base Critical Criteria:

Meet over Personal knowledge base tasks and change contexts.

– 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?

– Is there any existing Knowledge Base governance structure?

Tractatus Logico-Philosophicus Critical Criteria:

Understand Tractatus Logico-Philosophicus results and cater for concise Tractatus Logico-Philosophicus education.

– 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?

– Risk factors: what are the characteristics of Knowledge Base that make it risky?

– How can the value of Knowledge Base be defined?

RDF Schema Critical Criteria:

Illustrate RDF Schema engagements and sort RDF Schema activities.

– How is the value delivered by Knowledge Base being measured?

Rule-based system Critical Criteria:

Chart Rule-based system governance and perfect Rule-based system conflict management.

– How do you determine the key elements that affect Knowledge Base workforce satisfaction? how are these elements determined for different workforce groups and segments?

– What are the top 3 things at the forefront of our Knowledge Base agendas for the next 3 years?

– Why should we adopt a Knowledge Base framework?

Semantic broker Critical Criteria:

Deliberate Semantic broker projects and find out.

– How do senior leaders actions reflect a commitment to the organizations Knowledge Base values?

– What sources do you use to gather information for a Knowledge Base study?

– Does Knowledge Base analysis isolate the fundamental causes of problems?

Collective intelligence Critical Criteria:

Add value to Collective intelligence planning and describe the risks of Collective intelligence sustainability.

– 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?

– Meeting the challenge: are missed Knowledge Base opportunities costing us money?

– Are we Assessing Knowledge Base and Risk?

Information storage Critical Criteria:

Concentrate on Information storage visions and adopt an insight outlook.

– What are your current levels and trends in key measures or indicators of Knowledge Base product and process performance that are important to and directly serve your customers? how do these results compare with the performance of your competitors and other organizations with similar offerings?

– When a Knowledge Base manager recognizes a problem, what options are available?

Knowledge retrieval Critical Criteria:

Rank Knowledge retrieval adoptions and research ways can we become the Knowledge retrieval company that would put us out of business.

– How can we incorporate support to ensure safe and effective use of Knowledge Base into the services that we provide?

– Think about the functions involved in your Knowledge Base project. what processes flow from these functions?

Library 2.0 Critical Criteria:

Scrutinze Library 2.0 governance and don’t overlook the obvious.

– What prevents me from making the changes I know will make me a more effective Knowledge Base leader?

– How important is Knowledge Base to the user organizations mission?

– How do we manage Knowledge Base Knowledge Management (KM)?

Internationalized resource identifier Critical Criteria:

Study Internationalized resource identifier risks and point out Internationalized resource identifier tensions in leadership.

– What tools do you use once you have decided on a Knowledge Base strategy and more importantly how do you choose?

– Are accountability and ownership for Knowledge Base clearly defined?

– Which Knowledge Base goals are the most important?

Hilbert’s program Critical Criteria:

Focus on Hilbert’s program governance and test out new things.

– What may be the consequences for the performance of an organization if all stakeholders are not consulted regarding Knowledge Base?

– Will new equipment/products be required to facilitate Knowledge Base delivery for example is new software needed?

– What about Knowledge Base Analysis of results?

Topic Maps Critical Criteria:

Rank Topic Maps issues and define Topic Maps competency-based leadership.

– What are our Knowledge Base Processes?

Knowledge Graph Critical Criteria:

Be responsible for Knowledge Graph projects and finalize specific methods for Knowledge Graph acceptance.

– What is the source of the strategies for Knowledge Base strengthening and reform?

– What new services of functionality will be implemented next with Knowledge Base ?

– What threat is Knowledge Base addressing?

A.I. Artificial Intelligence Critical Criteria:

Chart A.I. Artificial Intelligence engagements and gather A.I. Artificial Intelligence models .

– What are your key performance measures or indicators and in-process measures for the control and improvement of your Knowledge Base processes?

– What are internal and external Knowledge Base relations?

Hypertext Transfer Protocol Critical Criteria:

Analyze Hypertext Transfer Protocol quality and sort Hypertext Transfer Protocol activities.

– 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 do you incorporate cycle time, productivity, cost control, and other efficiency and effectiveness factors into these Knowledge Base processes?

Snow Crash Critical Criteria:

Canvass Snow Crash issues and find the ideas you already have.

– What will be the consequences to the business (financial, reputation etc) if Knowledge Base does not go ahead or fails to deliver the objectives?

– How do we go about Securing Knowledge Base?

Linked data Critical Criteria:

Use past Linked data results and test out new things.

– What is the total cost related to deploying Knowledge Base, including any consulting or professional services?

– In what ways are Knowledge Base vendors and us interacting to ensure safe and effective use?

General Problem Solver Critical Criteria:

Explore General Problem Solver quality and get the big picture.

– Does Knowledge Base create potential expectations in other areas that need to be recognized and considered?

– How do we Lead with Knowledge Base in Mind?

Commonsense knowledge Critical Criteria:

Recall Commonsense knowledge outcomes and find answers.

Information architecture Critical Criteria:

Demonstrate Information architecture issues and diversify disclosure of information – dealing with confidential Information architecture information.

– Are content chunks small enough that we can group them together on one page, or should they remain on separate pages?

– Are we using ontology standards such as OWL and RDF in our information architecture or data management practices?

– what is a good application for drawing tree diagrams when planning information architecture?

– What is the difference between information architecture and interaction design?

– Does the size of your site suggest that users will get huge retrieval results?

– Is there a plan for ongoing measurement and evaluation of navigation?

– What types of content should and should not be part of the site?

– Do Nondomain Experts Enlist the Strategies of Domain Experts?

– Have the interoperability requirements been evaluated?

– What is the mission or purpose of the organization?

– How many retrieved documents should be displayed?

– How about a tear-away table of contents feature?

– Is social media a better investment than SEO?

– What pieces of content does the site need?

– Who was the site designed for?

– Is there a metadata strategy?

– What Do IT Architects Do?

– What is Service Design?

– What can you afford?

A Logic Named Joe Critical Criteria:

Gauge A Logic Named Joe issues and cater for concise A Logic Named Joe education.

– In a project to restructure Knowledge Base outcomes, which stakeholders would you involve?

– Are assumptions made in Knowledge Base stated explicitly?

Semantic matching Critical Criteria:

Depict Semantic matching strategies and summarize a clear Semantic matching focus.

– Will Knowledge Base have an impact on current business continuity, disaster recovery processes and/or infrastructure?

– Who is responsible for ensuring appropriate resources (time, people and money) are allocated to Knowledge Base?

– Are there recognized Knowledge Base problems?

Authority control Critical Criteria:

Pilot Authority control management and get answers.

– Are there any easy-to-implement alternatives to Knowledge Base? Sometimes other solutions are available that do not require the cost implications of a full-blown project?

– Can Management personnel recognize the monetary benefit of Knowledge Base?

– How do we go about Comparing Knowledge Base approaches/solutions?

Expert systems Critical Criteria:

Model after Expert systems planning and frame using storytelling to create more compelling Expert systems projects.

– Is there a Knowledge Base Communication plan covering who needs to get what information when?

Knowledge Base Critical Criteria:

Differentiate Knowledge Base risks and arbitrate Knowledge Base techniques that enhance teamwork and productivity.

– Do several people in different organizational units assist with the Knowledge Base process?

– What other jobs or tasks affect the performance of the steps in the Knowledge Base process?

Semantic analytics Critical Criteria:

Reorganize Semantic analytics adoptions and cater for concise Semantic analytics education.

– Do we aggressively reward and promote the people who have the biggest impact on creating excellent Knowledge Base services/products?

– Where do ideas that reach policy makers and planners as proposals for Knowledge Base strengthening and reform actually originate?

Semantic Web Critical Criteria:

Audit Semantic Web engagements and define what our big hairy audacious Semantic Web goal is.

– What are the Key enablers to make this Knowledge Base move?

Unstructured information Critical Criteria:

Facilitate Unstructured information failures and find the ideas you already have.

– Is the solution going to generate structured, semistructured, unstructured information for its own use or for use by entities either internal or external to the enterprise?

– Among the Knowledge Base product and service cost to be estimated, which is considered hardest to estimate?

– How do we Improve Knowledge Base service perception, and satisfaction?

– What is our Knowledge Base Strategy?

Embedded RDF Critical Criteria:

Differentiate Embedded RDF planning and oversee Embedded RDF management by competencies.

– How do we measure improved Knowledge Base service perception, and satisfaction?

Mind map Critical Criteria:

Transcribe Mind map failures and report on the economics of relationships managing Mind map and constraints.

– To what extent does management recognize Knowledge Base as a tool to increase the results?

– How can we improve Knowledge Base?

Data Web Critical Criteria:

Unify Data Web visions and check on ways to get started with Data Web.

Information Technology Critical Criteria:

Do a round table on Information Technology leadership and look at the big picture.

– 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?

– How does new information technology come to be applied and diffused among firms?

– The difference between data/information and information technology (it)?

– What potential environmental factors impact the Knowledge Base effort?

– When do you ask for help from Information Technology (IT)?

– How can you measure Knowledge Base in a systematic way?

Library classification Critical Criteria:

Deliberate Library classification decisions and change contexts.

– Have you identified your Knowledge Base key performance indicators?

Semantic reasoner Critical Criteria:

Systematize Semantic reasoner tasks and customize techniques for implementing Semantic reasoner controls.

– 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?

Facebook Platform Critical Criteria:

Set goals for Facebook Platform quality and separate what are the business goals Facebook Platform is aiming to achieve.

– What are the business goals Knowledge Base is aiming to achieve?

Question answering Critical Criteria:

Participate in Question answering management and research ways can we become the Question answering company that would put us out of business.

Knowledge-based system Critical Criteria:

Collaborate on Knowledge-based system quality and spearhead techniques for implementing Knowledge-based system.

– Why is Knowledge Base important for you now?

– Is Knowledge Base Required?

Characteristica universalis Critical Criteria:

Have a session on Characteristica universalis quality and find the essential reading for Characteristica universalis researchers.

– Which individuals, teams or departments will be involved in Knowledge Base?

– Will Knowledge Base deliverables need to be tested and, if so, by whom?

Description logic Critical Criteria:

Depict Description logic leadership and research ways can we become the Description logic company that would put us out of business.

– 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?

Common logic Critical Criteria:

Incorporate Common logic tactics and gather Common logic models .

– How do we Identify specific Knowledge Base investment and emerging trends?

Rule Interchange Format Critical Criteria:

Graph Rule Interchange Format decisions and achieve a single Rule Interchange Format view and bringing data together.

Text mining Critical Criteria:

Reason over Text mining planning and probe the present value of growth of Text mining.

Web engineering Critical Criteria:

Depict Web engineering quality and reinforce and communicate particularly sensitive Web engineering decisions.

– How does the organization define, manage, and improve its Knowledge Base processes?

Semantic service-oriented architecture Critical Criteria:

Design Semantic service-oriented architecture strategies and point out improvements in Semantic service-oriented architecture.

– What business benefits will Knowledge Base goals deliver if achieved?

Automated reasoning Critical Criteria:

Sort Automated reasoning results and probe the present value of growth of Automated reasoning.

– How do we maintain Knowledge Bases Integrity?

Inference engine Critical Criteria:

Learn from Inference engine results and explore and align the progress in Inference engine.

– Think of your Knowledge Base project. what are the main functions?

Versant Object Database Critical Criteria:

See the value of Versant Object Database leadership and track iterative Versant Object Database results.

– Why is it important to have senior management support for a Knowledge Base project?

Application-Level Profile Semantics Critical Criteria:

Consider Application-Level Profile Semantics tasks and spearhead techniques for implementing Application-Level Profile Semantics.

– Are there Knowledge Base problems defined?

Knowledge-based systems Critical Criteria:

Jump start Knowledge-based systems visions and ask questions.

– At what point will vulnerability assessments be performed once Knowledge Base is put into production (e.g., ongoing Risk Management after implementation)?

Dublin Core Critical Criteria:

Infer Dublin Core projects and know what your objective is.

– 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?

Knowledge representation and reasoning Critical Criteria:

Think about Knowledge representation and reasoning engagements and simulate teachings and consultations on quality process improvement of Knowledge representation and reasoning.

– Do we have past Knowledge Base Successes?

– How to deal with Knowledge Base Changes?


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 |

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.

External links:

To address the criteria in this checklist, these selected resources are provided for sources of further research and information:

Knowledge Base External links:

Indiana University – IU Knowledge Base

Welcome to the BroadCloud Knowledge Base

Public Knowledge Base – Home

Information repository External links:

Information Repository – Odoo

Payment Information Repository (PIR)

Paul Swoyer Septics — Information Repository

Web Content Management External links:

What is Drupal? | Drupal for Web Content Management

Best Web Content Management Software in 2017 | G2 Crowd

Ingeniux CMS Web Content Management Software Log-in

Wolfram Alpha External links:

Software Site Licensing: Wolfram Alpha Pro

Wolfram Alpha – Official Site

Knowledge engineering External links:

What is Knowledge Engineering | IGI Global

Tractatus Logico-Philosophicus External links:

Tractatus Logico-Philosophicus –

Tractatus logico-philosophicus (Book, 1994) …

Remarks about Wittgenstein’s Tractatus Logico-Philosophicus

RDF Schema External links:

[PDF]RDF and RDF Schema – University of Alberta

RDF Schema – Official Site

What is RDF Schema? Webopedia Definition

Rule-based system External links:

What is RULE-BASED SYSTEM – Science Dictionary

What is a rule-based system? – Jocelyn Ireson-Paine

What is Rule-Based System | IGI Global

Collective intelligence External links:

Collective Intelligence – AbeBooks

Collective Intelligence in Action

Aladdin – Powering Collective Intelligence – BlackRock

Information storage External links:

Home – HigherGround – Information Storage

[PDF]Information Storage and Management—Storing, …


Library 2.0 External links:

WIA Automation Library 2.0 does not support duplex scan

Learning Express Library 2.0 | Davenport University

Internationalized resource identifier External links:

Internationalized Resource Identifier (The Java™ …

Hilbert’s program External links:

Hilbert’s program then and now – Philsci-Archive

Hilbert’s Program (Stanford Encyclopedia of Philosophy)

Did the Incompleteness Theorems Refute Hilbert’s Program?

Topic Maps External links:

An Introduction to Topic Maps –

Knowledge Graph External links:

Unigraph – The world’s largest Knowledge Graph

Introducing the Knowledge Graph – YouTube

Stardog—The Knowledge Graph Platform for the Enterprise

A.I. Artificial Intelligence External links:

A.I. Artificial Intelligence Blu-ray

A.I. Artificial Intelligence (2001) – IMDb

Hypertext Transfer Protocol External links:

Hypertext Transfer Protocol (HTTP)

[PDF]HyperText Transfer Protocol: A Short Course

CSRC – Glossary – Hypertext Transfer Protocol

Snow Crash External links:

Snow Crash – Tag –

Snow Crash : NPR

Thomas and Snow Crash. – Roblox

Linked data External links:

Tim Berners-Lee: The next Web of open, linked data – YouTube

General Problem Solver External links:

GPS: General Problem Solver – Instructional Design

General Problem Solver | computer model |

Information architecture External links:

Information Architecture | The Understanding Group

Information Architecture Resource Library | IA Institute

Information Architecture Basics |

A Logic Named Joe External links:

A Logic Named Joe by Murray Leinster – Fantastic Fiction

A Logic Named Joe by Murray Leinster – Goodreads

A logic named Joe (Book, 2005) []

Authority control External links:

Authority Control Group > Orbis Cascade Alliance

Expert systems External links:

Expert systems. (Journal, magazine, 1984) []

[PDF]Expert Systems

Expert Systems – AbeBooks

Knowledge Base External links:

UX Knowledge Base Sketch

Indiana University – IU Knowledge Base

Emsi Support – Knowledge Base

Semantic Web External links:

What Is the Semantic Web? –

As of 2015, is the semantic web dead? – Updated 2017 – Quora

Semantic Web Working Group SPARQL endpoint

Unstructured information External links:

MedEx-Unstructured Information Management …

Mind map External links:

M8! – Mind Map – Free Download

ScapeHop: Best Mind Map AR App | Free download for Android – brainstorm and mind map online

Data Web External links:

MHEC Secure Data Web Home

PARCEL VIEWER | Best California property data web map.

Information Technology External links:

OHIO: Office of Information Technology |About Email

Umail | University Information Technology Services

SOLAR | Division of Information Technology

Library classification External links:

U.S. Geological Survey Library Classification System

ERIC – Functions of Library Classification., 1988

Semantic reasoner External links:

Semantic reasoner – Infogalactic: the planetary knowledge …

Question answering External links:

Myth Busting, Question Answering, and Publishing … : Question Answering Service

CloudCV: Visual Question Answering

Knowledge-based system External links:

A Knowledge-Based System Design/Information Tool | …

Concepts for Knowledge-Based System Design Environments

Description logic External links:

Description Logic Rules –

[PDF]Description Logic Reasoning with Syntactic Updates

Description logic rules (eBook, 2010) []

Common logic External links:

Logic – Common Logic / Midnight Marauder – YouTube

Rule Interchange Format External links:

[PDF]Use Cases for a Rule Interchange Format – REWERSE

RIF Rule Interchange Format Current Status – W3C

Text mining External links:

Text Mining / Text Analytics Specialist – bigtapp

Text Mining – FREE download Text Mining

Text mining — University of Illinois at Urbana-Champaign

Web engineering External links:

What Is Web Engineering? | eHow

Codebase Consulting – Web Engineering

What are quality attributes of web engineering? – Quora

Semantic service-oriented architecture External links:

CiteSeerX — WSMX – a semantic service-oriented architecture

Automated reasoning External links:

Handbook of Automated Reasoning – ScienceDirect

ARCOE – Workshop on Automated Reasoning about …

Inference engine External links:

[PDF]An Expert Inference Engine for Generation of …

Utilization Of Inference Engine Technology For Navy …

Application-Level Profile Semantics External links:

Application-Level Profile Semantics · GitHub

ALPS means Application-Level Profile Semantics

Knowledge-based systems External links:

Knowledge-Based Systems – ScienceDirect

Dublin Core External links:

Dublin Core and the Cataloging Rules

[PDF]ANSI NISO Z39-85-2007, The Dublin Core Metadata …

Dublin Core and the Cataloguing Rules: Example 7

Knowledge representation and reasoning External links:

Knowledge Representation and Reasoning – …

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