A Knowledge Representation Model using Concept-Relation Graph (original) (raw)
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A semantic network that displays the connections between entities is known as a knowledge graph. Data can be visualised to help with information analysis and comprehension through the use of the knowledge graph. Knowledge graphs can help professionals with complex analysis applications and decision support while also erecting barriers in the market. A good construction system can help companies construct knowledge graphs efficiently and quickly. However, Extraction of valuable information from such huge, complex and unstructured data requires a human approach to handle user queries related to data, which causes delays and uncertainty in decision making and strategy planning. In this paper, we propose an approach for automatic knowledge graph construction and automated querying engine to answer user queries using generated knowledge graph. The proposed system will benefit Professionals with faster, easier understanding and analysis of complex and huge unstructured data. Experimental results show that our proposed solution is more effective in constructing a generic knowledge graph.
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Hospitals generate and store a large amount of clinical data each year, a significant portion of which is in free text format. Conventional database storage and retrieval algorithms are incapable of effectively processing free text medical data. The rich information and knowledge buried in healthcare records are unavailable for clinical decision-making. We examined a number of techniques for structuring and processing free text documents to effective and efficient for information retrieval and knowledge discovery. One critical success criterion is that the complexity of the techniques must be polynomial both in space and time for them to be able to cope with very large databases. We used conceptual graphs (CG) to capture the structure and semantic information/knowledge contained within the free text medical documents. Ordering and self-organising techniques (lattice techniques and knowledge space) were used to improve organisation of concepts from standard medical nomenclatures and large sets of free text medical documents. Pair-wise union of CG was performed to identify the common generalisation structure and a lattice structure of these CG documents. A combination of all three techniques allowed us to organise a set of 9000 discharge summaries into a generalisation hierarchy that supported efficient and rich information/knowledge retrieval.
Building Concept Lattices by Learning Concepts from RDF Graphs Annotating Web Documents
Lecture Notes in Computer Science, 2002
This paper presents a method for building concept lattices by learning concepts from RDF annotations of Web documents. It consists in extracting conceptual descriptions of the Web resources from the RDF graph gathering all the resource annotations and then forming concepts from all possible subsets of resources-each such subset being associated with a set of descriptions shared by the resources belonging to it. The concept hierarchy is the concept lattice built upon a context built from the power context family representing the RDF graph. In the framework of the CoMMA European IST project dedicated to ontologyguided Information Retrieval in a corporate memory, the hierarchy of the so learned concepts will enrich the ontology of primitive concepts, organize the documents of the organization's Intranet and then improve Information Retrieval. The RDF Model is close to the Simple Conceptual Graph Model; our method can be thus generalized to Simple Conceptual Graphs.
Mining Conceptual Graphs for Knowledge Acquisition
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This work addresses the use of computational linguistic analysis techniques for conceptual graphs learning from unstructured texts. A technique including both content mining and interpretation, as well as clustering and data cleaning, is introduced. Our proposal exploits sentence structure in order to generate concept hypothese, rank them according to plausibility and select the most credible ones. It enables the knowledge acquisition task to be performed without supervision, minimizing the possibility of failing to retrieve information contained in the document, in order to extract non-taxonomic relations.
Towards MORK: Model for Representing Knowledge
International Journal of Modern Education and Computer Science, 2016
Smart world needs intelligent system for effective and timely decision making. This is achieved only through a knowledge based system with functional knowledge representation units. In this paper, two models are proposed for representing knowledge. This process involves in getting the data and placing the information in the correct location. Logical notations are used for taking the clauses and graph is used for putting the entities. In Model one, the data is translated into logical statements using predicate logics, later the knowledge is stored in conceptual graph and retrieved. Whereas in Model two, the given information is translated using First Order Logic (FOL), by applying description logic concept rules are defined and as a result reasoning is done. Storage is done by using concept-relation graph. The main aims of our models are to have easy and simple access over the information. These models return the required exact answer, for the higher order query posted by the end user to the intelligent system.
Simple concept graphs: A logic approach
Lecture Notes in Computer Science, 1998
Conceptual Graphs and Formal Concept Analysis are combined by developing a logical theory for concept graphs of relational contexts. Therefore, concept graphs are introduced as syntactical constructs, and their semantics is de ned based on relational contexts. For this contextual logic, a sound and complete system of inference rules is presented and a standard graph is introduced that entails all concept graphs being valid in a given relational context. A possible use for conceptual knowledge representation and processing is suggested.