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Analysis of Ontology-Based Semantic Association Rule Mining
Published in Archana Patel, Narayan C. Debnath, Bharat Bhushan, Semantic Web Technologies, 2023
Semantic association relations can be derived from the text as well as the web by using formal representation languages. The semantic web provides a large platform for semantic data on accumulating knowledge from different sources by sharing and interlinking multiple domains’ structural data. The technological growth made more semantic data to be stored on the web, a rich source of hidden patterns. The semantic web’s heterogeneous data with semantic properties are expressed as OWL or RDFS in a subject, predicate, or object representation. The description logic, a formal knowledge representation language is used to represent information into OWL and RDFS. An ontology represents the semantic web knowledge in the form of classes, relations, and properties, and plays a vital role in extracting patterns from semantic web data. The traditional association rule mining technique cannot apply to semantic data to extract relations. The combination of association rule mining with ontology as background knowledge can effectively extract semantic relations from semantic data [41]. The online software tools FRED can be used to convert the text into semantically connected ontologies in RDFS or OWL format [42].
Development Toward Autonomous Systems
Published in Ulrich Rembold, Robot Technology and Applications, 2020
There are basically two approaches to knowledge representation: declarative and procedural. Declarative representation is a neutral way to represent knowledge, independent of its use. Control is achieved at a higher level, by general-purpose knowledge processing strategies, which are applied uniformly. An example of this approach is the implementation of the language Prolog. The knowledge base is formed from associations between items of information and rules of inference operating on the associations. The control is achieved through the backtracking mechanism of the interpreter. A variation of this approach is to include control elements in the knowledge base as distinct items. An implementation of a rule-based system in LISP is an example of this approach, since LISP doesn’t provide an inference mechanism.
Introduction to Expert Systems
Published in Chris Nikolopoulos, Expert Systems, 1997
Knowledge acquisition encompasses the processes of knowledge elicitation and knowledge representation. Knowledge elicitation deals with the issues involved in acquiring the necessary knowledge and constructing a conceptual model of this knowledge in order to solve the problem at hand. Knowledge representation is the process of choosing an appropriate knowledge representation formalism, in which the acquired knowledge is encoded so that it becomes amenable to computer manipulation. The amount of competence, which an expert system exhibits in solving problems, depends on the quantity and quality of the knowledge obtained as a result of the knowledge elicitation process. The efficiency, with which problems are solved, depends among other things on the knowledge representation formalism chosen to encode this knowledge. This Chapter mainly deals with the topic of knowledge representation. Knowledge elicitation is covered in Chapter 4.
Towards knowledge-based geovisualisation using Semantic Web technologies: a knowledge representation approach coupling ontologies and rules
Published in International Journal of Digital Earth, 2020
One prominent advantage of harnessing Semantic Web technologies is the inherent knowledge representation capacity equipped with the technology stack. Knowledge representation is a branch of symbolic artificial intelligence, which studies the formalisation of knowledge and its processing within machines (Grimm, Hitzler, and Abecker 2007). Since 1960s, the focus of knowledge representation has evolved through several stages, including general problem solver, expert systems, frame based languages, and rule-based systems, and currently one of the most active areas of knowledge representation research is the Semantic Web. The Semantic Web provisions us with the capacity for representing knowledge, supporting search queries on knowledge and inference. In the Semantic Web, knowledge is represented in different forms, and ontologies (description logics) and rules (horn logic) are the two main paradigms for knowledge representation (Hitzler and Parsia 2009).
Analogical stimuli retrieval approach based on R-SBF ontology model
Published in Journal of Engineering Design, 2019
Lizhen Jia, Qingjin Peng, Runhua Tan, Xuehong Zhu
Knowledge representation is the first task in analogical reasoning. Analogue determines whether analogy can happen and be implemented successfully. Research discussed in literature has proposed different forms or methods to represent analogue and target for mapping and transferring the design knowledge easily. Analogues may stem from engineering systems, biological systems, or abstract heuristics. Overall, there are generally two methods of the knowledge representation: natural language expression and modelling based on the difference among objects and goals described. Both of them abstract the engineering system at a high level, making the analogues searching frontier larger.
From CAD assemblies toward knowledge-based assemblies using an intrinsic knowledge-based assembly model
Published in Computer-Aided Design and Applications, 2018
Harold Vilmart, Jean-Claude Léon, Federico Ulliana
Knowledge representation frameworks are commonly specified with description logics languages like the lightweight DL-Lite [9], or the expressive Horn-SHIQ [12], or, more generally, rule-based languages representing fragments of first-order logic like Datalog± [8] and existential rules [3]. Each knowledge representation language has a distinctive set of modeling features and expressivity. This led to the implementation of a number of reasoners each one optimized for a specific language [7, 14, 35].