Showing posts with label knowledge. Show all posts
Showing posts with label knowledge. Show all posts
Wednesday, June 10, 2009
Types of Models - For Business Versus For IT
I was looking through my notes about articles that I had read - and found an interesting Burton Group report entitled Generalized and Detailed Data Models: Seeking the Best of Both Worlds. (I think that it was published earlier this year.) I must admit to having been both confused and intrigued by the title. :-)
In the paper, "generalized" models are those used to define database/storage structures and to find the general themes and fundamental aspects of the data (and its values). In short, they are the data models defined by IT to effectively and efficiently use the technologies that are in place (like SQL databases). Maybe "reduced" is a better word than "generalized" ...
On the other hand, "detailed" models are those that are useful to business people. They define and describe the information requirements of the business, and its vocabularies, rules and processes. They hold the details from the business perspective. Again, maybe another word like "conceptual" is better (since even the "generalized" models hold "details") ...
What is valuable is not the titles used for these models but their semantics. :-) The key message is that a business needs both types of models and they need to stay in sync. This is really important. The conceptual/detailed models hold the real business requirements and language. They haven't been reduced to basic data values whose semantics are lost in the technology used to define and declare them.
IMHO, a business loses information and knowledge when it only retains and works from the IT models. There is much to be gleaned from the business input and much value in keeping the business people engaged in the work. This is almost impossible once you reduce the business requirements to technology-speak.
As the report says, "do not allow generalized models to compromise your understanding of the business."
In the paper, "generalized" models are those used to define database/storage structures and to find the general themes and fundamental aspects of the data (and its values). In short, they are the data models defined by IT to effectively and efficiently use the technologies that are in place (like SQL databases). Maybe "reduced" is a better word than "generalized" ...
On the other hand, "detailed" models are those that are useful to business people. They define and describe the information requirements of the business, and its vocabularies, rules and processes. They hold the details from the business perspective. Again, maybe another word like "conceptual" is better (since even the "generalized" models hold "details") ...
What is valuable is not the titles used for these models but their semantics. :-) The key message is that a business needs both types of models and they need to stay in sync. This is really important. The conceptual/detailed models hold the real business requirements and language. They haven't been reduced to basic data values whose semantics are lost in the technology used to define and declare them.
IMHO, a business loses information and knowledge when it only retains and works from the IT models. There is much to be gleaned from the business input and much value in keeping the business people engaged in the work. This is almost impossible once you reduce the business requirements to technology-speak.
As the report says, "do not allow generalized models to compromise your understanding of the business."
Labels:
business vocabularies,
data models,
knowledge
Tuesday, May 26, 2009
Web 3.0 and the Web of Data
Web 3.0 is coming up (a lot) in posts on Read-Write Web and in other places. One Read-Write Web posting (The Web of Data, written by Alexander Korth in April of this year) discussed the 3 aspects of the next web (Web 3.0) ... "In the coming years, we will see a revolution in the ability of machines to access, process, and apply information. This revolution will emerge from three distinct areas of activity connected to the Semantic Web: the Web of Data, the Web of Services, and the Web of Identity providers. These webs aim to make semantic knowledge of data accessible, semantic services available and connectable, and semantic knowledge of individuals processable ...".
Tim Berners-Lee focused on the Web of Data in his TED talk on the next Web (recorded in Feb 2009). The talk is only a little longer than 15 minutes in length, and I highly recommend it. The key points are that we are now moving from a document-centric approach to storing information, to making raw data available and processable. That raw data is "linked data" - data about things (identified by URIs), including other interesting information (as RDF triples) and highlighting the relationships between the things. It is important to note that this is not about making data available through specific APIs or anticipated/pre-programmed queries on a "pretty" web site - but about making the "unadulterated data" available for machine understanding and new uses. It is about sharing and adding to data, making connections and relationships in novel ways, and bridging disciplines.
If you think about business and an enterprise, think about how powerful this would be - to capture knowledge, share it via social networking technologies, allow update and addition to the knowledge within the enterprise (again using the social networking tools of today), and to bridge disciplines and knowledge using the Semantic web mining and matching technologies. Overall, we improve the ability of the enterprise to capture and access its knowledge, and increase the captured knowledge. In the talk, Tim Berners-Lee asks people to imagine the "incredible resource" of "people doing their bit to produce a little bit, and it all connecting."
Just imagine ....
Tim Berners-Lee focused on the Web of Data in his TED talk on the next Web (recorded in Feb 2009). The talk is only a little longer than 15 minutes in length, and I highly recommend it. The key points are that we are now moving from a document-centric approach to storing information, to making raw data available and processable. That raw data is "linked data" - data about things (identified by URIs), including other interesting information (as RDF triples) and highlighting the relationships between the things. It is important to note that this is not about making data available through specific APIs or anticipated/pre-programmed queries on a "pretty" web site - but about making the "unadulterated data" available for machine understanding and new uses. It is about sharing and adding to data, making connections and relationships in novel ways, and bridging disciplines.
If you think about business and an enterprise, think about how powerful this would be - to capture knowledge, share it via social networking technologies, allow update and addition to the knowledge within the enterprise (again using the social networking tools of today), and to bridge disciplines and knowledge using the Semantic web mining and matching technologies. Overall, we improve the ability of the enterprise to capture and access its knowledge, and increase the captured knowledge. In the talk, Tim Berners-Lee asks people to imagine the "incredible resource" of "people doing their bit to produce a little bit, and it all connecting."
Just imagine ....
Labels:
knowledge,
linked data,
semantic web,
Web 3.0,
Web of Data
Monday, May 11, 2009
Going to School - Knowledge Management Style
In May 2001, Michael Earl wrote about three main categories and seven schools of knowledge management. His article was published in the Journal of Management Information Systems (Vol 18, Issue 1).
The three categories for capturing and sharing knowledge are:
Within each of the categories, Earl posited that there are "schools" or focuses for knowledge management. Earl's seven schools are listed below (with some short descriptions):
And, how do you do this? Via capturing, publishing and mapping each business group's/community's vocabularies (ontologies) and processes, and understanding that community's organizational structure.
The three categories for capturing and sharing knowledge are:
- Technocratic - involved with tooling and the use of technology for knowledge management
- Economic - relating knowledge and income
- Behavioral -dealing with how to organize to facilitate knowledge capture and exchange
Within each of the categories, Earl posited that there are "schools" or focuses for knowledge management. Earl's seven schools are listed below (with some short descriptions):
- Systems - Part of the technocratic category, focusing on the use of technology and the storing of explicit knowledge in databases and various systems and repositories. The knowledge is typically organized by domain.
- Cartographic - Part of the technocratic category, focusing on who the "experts" are, in a company, and how to find and contact them. So, instead of explicit captured knowledge, the tacit knowledge held by individuals is paramount.
- Engineering - Part of the technocratic category, focusing on capturing and sharing knowledge for process improvement. In addition, the details and outputs of various processes and knowledge flows are captured. The knowledge in this school is organized by activities with the goal of business process improvement.
- Commercial - This is the only "economic" school and focuses on knowledge as a commercial asset. The emphasis is on income, which can be achieved in various ways ... such as limiting access to knowledge, based on payments or other exchanges, or rigorously managing a company's intellectual portfolio (individual know-how, patents, trademarks, etc.).
- Organizational - Part of the behavioral category, focusing on building and enabling knowledge-sharing networks and communities of practice, for some business purpose. Earl defines it as a behavioral school "because the essential feature of communities is that they exchange and share knowledge interactively, often in nonroutine, personal, and unstructured ways". For those not familiar with the term "community of practice", it is defined by Etienne Wenger as “groups of people who share a concern or a passion for something they do and learn how to do it better as they interact regularly.”
- Spatial - Part of the behavioral category, focusing on how space is used to facilitate socialization and the exchange of knowledge. This can be achieved by how office buildings are arranged, co-locating individuals working on the same project, etc.
- Strategic - Part of the behavioral category, focusing on knowledge (according to Earl) as "the essence of a firm's strategy ... The aim is to build, nurture, and fully exploit knowledge assets through systems, processes, and people and convert them into value as knowledge-based products and services." This may seem like the strategic school rolls all the others into it, and it does. But, what distinguishes it, again according to Earl, "is that knowledge or intellectual capital are viewed as the key resource."
And, how do you do this? Via capturing, publishing and mapping each business group's/community's vocabularies (ontologies) and processes, and understanding that community's organizational structure.
Labels:
knowledge,
knowledge management,
ontologies
Tuesday, May 5, 2009
Organizing Knowledge
I thought that I would examine what other writers think of "organizing knowledge", since I chose this for the title of my blog.
My passion for this title comes from the need to meld business knowledge with IT infrastructure - organizing the business' inherent and (usually) implicit knowledge by first capturing it and then making it usable, accessible and actionable (within the IT infrastructure). There is another aspect to this also - taking lots of information (already in the IT infrastructure) and organizing it to turn it into knowledge (not just bits of data).
Given these two goals, you find (or will find) lots of postings about ontologies, business processes, semantic web and similar topics in this blog. (Also, you will occasionally find some riffs on digital natives and education - since these are of particular interest to me.) I will not repeat postings from my earlier blog (while I was at Microsoft). You can read these yourself at http://blogs.msdn.com/policy_based_business .
Well, back to what others think about "organizing knowledge". Most of the work in this space is related to organizing and cataloging library materials, since libraries were the main repositoryof knowledge, and books the main format up until this digital age. This has now all changed. The need to catalog and classify books, using a single scheme, in order to find a particular book on a particular shelf in a physical library building is no longer a primary driver. One would argue that it is not even an appropriate driver, in a fast-paced, online business environment. (However, I must confess to a passion for reading real, physical books, away from the electronic distractions of today's environments.)
Libraries had a need for a single, driving organizational scheme since they often only had a few copies of a book and could not have them scattered across many shelves, classified in different ways. Now, multiple classifications/organization schemes can exist and cross-reference each other.
Where before knowledge extraction was all manual ... someone had to read the books, examine the world, organize and build on the knowledge, draw new insights and conclusions ... we now have tons of data stored on our computers and on the Web, and the help of the semantic web and description logic reasoners. Notice that I said "the help of semantic web" - it still requires a person to classify, organize and query knowledge in valid ways, and to interpret the results. I am not close to advocating for or finding the HAL computer from 2001. :-)
So, back to what others think about "organizing knowledge". I did a search on "organizing knowledge" on Amazon. Here is what I found (all quotations are from the editorial reviews on Amazon):
As you can see, there is some interesting material out there, and some mundane stuff. I have ordered several of the books listed above and will report on them in future posts on this blog. Hopefully, the information will be of help to all of us.
My passion for this title comes from the need to meld business knowledge with IT infrastructure - organizing the business' inherent and (usually) implicit knowledge by first capturing it and then making it usable, accessible and actionable (within the IT infrastructure). There is another aspect to this also - taking lots of information (already in the IT infrastructure) and organizing it to turn it into knowledge (not just bits of data).
Given these two goals, you find (or will find) lots of postings about ontologies, business processes, semantic web and similar topics in this blog. (Also, you will occasionally find some riffs on digital natives and education - since these are of particular interest to me.) I will not repeat postings from my earlier blog (while I was at Microsoft). You can read these yourself at http://blogs.msdn.com/policy_based_business .
Well, back to what others think about "organizing knowledge". Most of the work in this space is related to organizing and cataloging library materials, since libraries were the main repositoryof knowledge, and books the main format up until this digital age. This has now all changed. The need to catalog and classify books, using a single scheme, in order to find a particular book on a particular shelf in a physical library building is no longer a primary driver. One would argue that it is not even an appropriate driver, in a fast-paced, online business environment. (However, I must confess to a passion for reading real, physical books, away from the electronic distractions of today's environments.)
Libraries had a need for a single, driving organizational scheme since they often only had a few copies of a book and could not have them scattered across many shelves, classified in different ways. Now, multiple classifications/organization schemes can exist and cross-reference each other.
Where before knowledge extraction was all manual ... someone had to read the books, examine the world, organize and build on the knowledge, draw new insights and conclusions ... we now have tons of data stored on our computers and on the Web, and the help of the semantic web and description logic reasoners. Notice that I said "the help of semantic web" - it still requires a person to classify, organize and query knowledge in valid ways, and to interpret the results. I am not close to advocating for or finding the HAL computer from 2001. :-)
So, back to what others think about "organizing knowledge". I did a search on "organizing knowledge" on Amazon. Here is what I found (all quotations are from the editorial reviews on Amazon):
- Organizing Knowledge (Jennifer Rowley and Richard Hartley) - "Incorporates extensive revisions reflecting the increasing shift towards a networked and digital information environment, and its impact on documents, information, knowledge, users and managers ... [offers] a broad-based overview of the approaches and tools used in the structuring and dissemination of knowledge".
- Organising Knowledge: Taxonomies, Knowledge and Organisational Effectiveness (Patrick Lambe) - Defines and discusses various taxonomic forms and how these "can help organizations to leverage and articulate their knowledge"
- The Organization of Information (Arlene Taylor) - "Provides a detailed and insightful discussion of such basic retrieval tools as bibliographies, catalogs, indexes, finding aids, registers, databases, major bibliographic utilities, and other organizing entities"
- The Intellectual Foundation of Information Organization (Elaine Svenonius) - Analyzes the foundations of information organization, and then presents three bibliographic languages: work languages, document languages, and subject languages. From the review, "The effectiveness of a system for accessing information is a direct function of the intelligence put into organizing it."
- Organizing Business Knowledge (Thomas Malone) - "Proposes a set of fundamental concepts to guide analysis and a classification framework for organizing knowledge, and describes the publicly available online knowledge base developed by the project, which includes a set of representative templates and specific case examples as well as a set of software tools for organizing and sharing knowledge"
As you can see, there is some interesting material out there, and some mundane stuff. I have ordered several of the books listed above and will report on them in future posts on this blog. Hopefully, the information will be of help to all of us.
Thursday, April 30, 2009
Thinking about the "Curse of Knowledge"
I was reading some old blog posts from Semantic Focus, and ran across one that was good food for thought - The Curse of Knowledge and the Semantic Web . Although I do not agree with everything in the article, it highlighted some important topics.
The premise is that experts include everything and the kitchen sink in an ontology (because they know so much about it) and use technology-specific language (which means little to people outside the domain of expertise). So, there ends up being a mapping problem between experts and lay people, and therefore between computers programmed (by people) to search for certain information.
On the importance (or curse) of mapping, I totally agree. However, the issue that peaks my interest is not the mapping between lay people and domain experts, as much as the mapping between perspectives of different groups in a business. These perspectives are what define the groups' vocabularies and ontologies. There is no single, "right" perspective - and there is a huge need to map and align the perspectives - to allow the unimpeded flow of information between groups, and to correct inconsistencies.
That is why I advocate mapping to an upper ontology. Upper ontologies capture general and reusable terms and definitions (for more information, see my earlier post ). They should not restrict a mapping to a certain perspective, but allow all the perspectives to be aligned. (That is also why you may need more than one.) There will certainly be subsets and supersets of information, as well as information in only one perspective. That is to be expected. However, the relationships should be known, mappable and should NOT conflict.
Getting back to the article, it does highlight a few things to help with the "curse of knowledge":
The premise is that experts include everything and the kitchen sink in an ontology (because they know so much about it) and use technology-specific language (which means little to people outside the domain of expertise). So, there ends up being a mapping problem between experts and lay people, and therefore between computers programmed (by people) to search for certain information.
On the importance (or curse) of mapping, I totally agree. However, the issue that peaks my interest is not the mapping between lay people and domain experts, as much as the mapping between perspectives of different groups in a business. These perspectives are what define the groups' vocabularies and ontologies. There is no single, "right" perspective - and there is a huge need to map and align the perspectives - to allow the unimpeded flow of information between groups, and to correct inconsistencies.
That is why I advocate mapping to an upper ontology. Upper ontologies capture general and reusable terms and definitions (for more information, see my earlier post ). They should not restrict a mapping to a certain perspective, but allow all the perspectives to be aligned. (That is also why you may need more than one.) There will certainly be subsets and supersets of information, as well as information in only one perspective. That is to be expected. However, the relationships should be known, mappable and should NOT conflict.
Getting back to the article, it does highlight a few things to help with the "curse of knowledge":
- Focus on the intent of the ontology, instead of the details (However, I think that you need both.)
- Define small, focused ontologies, each with a single intent and extensions for details
- Determine the core concept(s) and label them
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