Knowledge-based systems can be broadly classified as CASE-based systems, intelligent tutoring systems, expert systems, hypertext manipulation systems and. Computer-based expert systems (also known as knowledge-based systems) can be constructed by obtaining this knowledge from a human expert and transforming it into a form that a computer may use to solve similar problems.KNOWLEDGE-BASED · PROPOSED SOLUTION · CASE STUDY: THE. Knowledge Based Expert Systems are emerging as an important tool kit for the development of engineering software. These systems provide a means to solve.
|Published:||19 January 2017|
|PDF File Size:||28.19 Mb|
|ePub File Size:||31.46 Mb|
A case study involving a multiple alignment expert system prototype called AlexSys is also presented. In this context, complex informatics data management and integration systems are now being introduced to collect, store and curate all this heterogeneous information in ways that will allow its efficient retrieval and exploitation.
These developments are opening up the possibility knowledge based expert systems new large-scale studies, aimed at understanding how genetic information is translated to molecular function, networks and pathways, all the way to physiology and even ecological systems.
Knowledge-based systems - Wikipedia
New challenges Such system-level studies necessitate a combination of experimental, theoretical and computational approaches and a crucial factor for their success will be the efficient exploitation of the multitude of heterogeneous data resources that include genomic sequences, 3D structures, knowledge based expert systems localisations, phenotype and other types of biologically relevant information.
In this context, a major challenge for bioinformaticians in the post-genomic era is clearly the management, validation and analysis of this mass of experimental and predicted data, in order to identify relevant biological patterns and to extract the hidden knowledge [ 2 ].
Significant research efforts are now underway to address these problems. One approach has been data warehousing, where all the relevant databases are stored locally in a knowledge based expert systems format and mined through a uniform interface. SRS [ 3 ] and Entrez [ 4 ] are probably the most widely used database query and navigation systems for the life science community.
Alternatively, distributed systems implement software to access heterogeneous databases that are dispersed over the internet and provide a query facility to access the data. More recently, semantic web based methods have been introduced that are designed to add meaning to the raw data by using formal descriptions of the concepts, terms and relationships encoded within knowledge based expert systems data.
Many of these technologies are reviewed in more detail in ref. Today's information-rich environment has also led to the development of numerous software tools, designed to analyse and understand the data. knowledge based expert systems
Knowledge-based systems can knowledge based expert systems broadly classified as CASE-based systems, intelligent tutoring systems, expert systems, hypertext manipulation systems and databases with intelligent user interface.
Compared to traditional computer-based information systems, knowledge-based systems have many advantages.
They can provide efficient documentation and also handle large amounts of unstructured data in an intelligent fashion. Knowledge-based systems can aid in expert decision making and allow users to work at a higher level of expertise and promote productivity and consistency.
These systems are considered very useful when expertise is knowledge based expert systems, or when data needs to be stored for future usage or needs to be grouped with different expertise at a common platform, thus providing large-scale integration of knowledge.
What is a Knowledge-Based System (KBS)? - Definition from Techopedia
Representing knowledge explicitly allowed systems to reason about how they came to a conclusion and use this information to explain results to users.
For example, to follow the chain of inferences that led to a diagnosis and use these facts to explain the diagnosis. Decoupling the knowledge from the processing of that knowledge enabled general purpose inference engines to be developed. These systems could develop conclusions that followed knowledge based expert systems a data set that the initial developers may not have even been aware of.
Rather than representing facts as assertions about data, the knowledge-base became more structured, representing information using similar techniques to object-oriented programming such as hierarchies of classes and subclasses, relations between classes, and behavior of objects.
As the knowledge base became more structured reasoning could occur both by independent rules and by interactions within knowledge based expert systems knowledge base itself.
For example, procedures stored as demons on objects could fire and could replicate the chaining behavior of rules.