Integrated Knowledge Base: An Approach to Knowledge Extraction
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Abstract:
This paper describes an approach to integrate knowledge base via converting predicates into Semantic networks and in frames. A knowledge base can be represented in a tabular form, a rule form, a tree form or any other form suitable for knowledge representation. Form conversion can be accomplished at all times. Unification of knowledge always overcome individual limitations and has synergetic effects in knowledge extraction. The graphical representation of knowledge base has more understandability than any other representation. Aim of this paper is to develop a system which accepts input from the user in the form of predicates and generates outputs with graphical representation of semantic networks as well as of frames.Keywords:
Unification
Representation
Semantic network
Base (topology)
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Traditional approaches to building intelligent information systems employ an ontology to define a representational structure for the data and information of interest within the target domain of the system. At runtime, the ontology provides a constrained template for the creation of the individual objects and relationships that together define the state of the system at a given point in time. The ontology also provides a vocabulary for expressing domain knowledge typically in the form of rules (declarative knowledge) or methods (procedural knowledge). The system utilizes the encoded knowledge, often in conjunction user input, to progress the state of the system towards the specific goals indicated by the users. While this approach has been very successful, it has some drawbacks. Regardless of the implementation paradigm the knowledge is essentially buried in the code and therefore inaccessible to most domain experts. The knowledge also tends to be very domain specific and is not extensible at runtime. This paper describes a variation on the traditional approach that employs an explicit knowledge level within the ontology to mitigate the identified drawbacks.
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The approaches for extraction and representation of knowledge from textual user requirements could be summarized in two groups: i) when we have in mind an end model and seek, mine and represent knowledge for it. In short, this approach can be defined as “knowledge driven from a target model”; ii) when we have in mind knowledge in a text, and seek an appropriate model to represent it. The intermediate model obtained in this manner can be processed further, to achieve another kind of end model. We define this approach as “model driven from source knowledge”. This paper presents an approach of the second kind, for analyzing textual user requirements and extraction and representation of knowledge as a graphical requirement engineering (RE) model – Use Case Paths.
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This paper introduces a new approach to visualize a knowledge base in a form of IF-THEN rules. First part introduces a related work relating to knowledge base representation and visualization. Next part describes fact-based modeling as a potential candidate to create a diagram from IF-THEN rules. Third part proposes needed extensions to express knowledge base visually and perform and automated transfer between IF-THEN rules and diagram.
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A relational knowledge base model and an architecture which manipulates the model are presented. An item stored in the relational knowledge base is called a term. A unification operation on terms in the relational knowledge base is used as the retrieval mechanism. The relational knowledge base architecture we propose consists of a number of unification engines, several disk systems, a control processor, and a multiport page-memory. The system has a knowledge compiler to support a variety of knowledge representations.
Unification
Base (topology)
Statistical relational learning
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Knowledge Acquisition
Representation
Associative property
Legal expert system
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The need to add an automatic learning phase to the construction process of a knowledge base is stressed. This work introduces a new technique based on machine learning methodologies which automatically creates a particular knowledge representation structure called knowledge space, from which common generalizations of knowledge objects may be efficiently inferred. Also introduced are some of the improved and added processing capabilities made possible by this structure.< >
Structuring
Representation
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We describe a knowledge representation scheme called KNET and a problem solving system called SNIFFER designed to answer queries using a K-NET knowledge base. K-NET uses a partitioned semantic net to combine the expressive capabilities of the first-order predicate calculus with linkage to procedural knowledge and with full indexing of objects to the relationships in which they participate. Facilities are also included for representing taxonomies of sets and for maintaining hierarchies of contexts. SNIFFER is a manager and coordinator of deductive and problem-solving processes. The basic system includes a logically complete set of natural deduction facilities that do not require statements to be converted into clause or prenex normal form. Using SNIFFER's coroutine-based control structure, alternative proofs may be constructed in pseudo-parallel and results shared among them. In addition, SNIFFER can also manage the application of specialist procedures that have specific knowledge about a particular domain or about the topology of the K-NET structures, for example, specialist procedures are used to manipulate taxonomic information and to link the system to information in external data bases.
Predicate (mathematical logic)
First-order logic
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A method for learning knowledge from a database is used to address the bottleneck of manual knowledge acquisition. An attempt is made to improve representation with the assistance of experts and from computer resident knowledge. The knowledge representation is described in the framework of a conceptual schema consisting of a semantic model and an event model. A concept classifies a domain into different subdomains. As a method of knowledge acquisition, inductive learning techniques are used for rule generation. The theory of rough sets is used in designing the learning algorithm. Examples of certain concepts are used to induce general specifications of the concepts called classification rules. The basic approach is to partition the information into equivalence classes and to derive conclusions based on equivalence relations. In a sense, what is involved is a data-reduction process, where the goal is to reduce a large database of information to a small number of rules describing the domain. This completely integrated approach includes user interface, semantics, constraints, representations of temporal events, induction, etc.< >
Knowledge Acquisition
Schema (genetic algorithms)
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Vagueness
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Information is pervasive element in all human activities. Organised information leads to knowledge. Information processing language provides a modeller for knowledge base systems. The development of inference engine and user interface acts as filter process for connecting knowledge base. The definition of semantic domain and the syntactical featuring of objects, properties, action, space and time will set arrangement of information into knowledge base system. The Facet Analytic Approach provides an analytical synthetic base for the same.
Base (topology)
Information Space
Information processor
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