Coupling two complementary knowledge discovery systems

Computational Intelligence, 1991

The development of efficient algorithms for learning from large relational databases is an important task in applicative machine learning. In this paper, we study knowledge discovery in relational databases and develop an attribute-oriented learning method which extracts generalization rules from relational databases. The method adopts the artificial intelligence "learning-from-examples" paradigm and applies in the learning process an attribute-oriented concept tree ascending technique which integrates database operations with the learning process and provides a simple and efficient way of learning from databases. The method learns both characteristic rules and classification rules of a learning concept, where a characteristic rule characterizes the properties shared by all the facts of the class being learned; while a classification rule characterizes the properties that distinguish the class being learned from other classes. The learning result could be a conjunctive rule or a rule with a small number of Jisjuncts. Moreover, learning can be performed with databases containing noisy data and exceptional cases using database statistics. Our analysis of the algorithms shows that attributeoriented induction substantially reduces the computational complexity of the database learning process.

2011

The exponential growth of data generates terabytes of very large databases. The growing number of data dimensions and data objects presents tremendous challenges for effective data analysis and data exploration methods and tools. One solution commonly proposed is the use of a condensed description of the properties and structure of data. Thus, it becomes crucial to have visualization tools capable of representing the data structure, not from the data themselves, but from these condensed descriptions. The purpose of our work described in this paper is to develop and put a synergistic visualization of data and knowledge into the knowledge discovery process. We propose here a method of describing data from enriched and segmented prototypes using a clustering algorithm. We then introduce a visualization tool that can enhance the structure within and between groups in data. We show, using some artificial and real databases, the relevance of the proposed method.

Today most of the information we produce is stored digitally. We are slowly forced to leave behind us thinking about information as something stored in physical containers as books, drawings etc. We make it possible to dynamically create logical containers of information on the fly. The paper focuses on how we in the future can aggregate, classify and generalize digitally stored information in order to make it more accessible and how we can define underlying knowledge container models to support knowledge discovery and collaboration. Examples are picked from ongoing research and the outcomes are generally valid and in particular for the structural engineering field.

AAAI 2023 Spring Symposium on Challenges Requiring the Combination of Machine Learning and Knowledge Engineering (AAAI-MAKE 2023), 2023

This position paper purports a novel extension for knowledge extraction and interpretation by exploring the existence of knowledge via interdisciplinary routes. The existing knowledge discovery mindset operates in the embedded paradigm which encompasses the premise that knowledge is embedded in data and should be discovered. Hence, at present, data representation and computational approaches use structural properties of data to discover new knowledge. The limitation of this perspective is that it leads to finding a possible existence rather than possible knowledge within a context. As a solution, we propose a new perspective to knowledge discovery, the interpretive paradigm. In this approach, we argue that knowledge in its true definition is interactive, even though the structural properties play a significant role in data representation and transformation. Thus, knowledge is nonsensical in the existence of absolute nature. Knowledge is a construct by the existence of a schema of associated other constructs. Given this premise, data becomes a signal to an interpreter but not the interpretation itself. Hence, multiple interpretations can be accommodated from the same data depending on the schema that is used to interpret them. The knowledge of the interpretive paradigm is in constant evolution as it is constructed (as opposed to mining in the embedded paradigm) at the interaction of the signal and the interpreter. We believe that the proposed paradigm will bring a new perspective to knowledge discovery methods. This will enable systems to adopt diversified knowledge that is unique to a variety of representations of the knowledge the society, such as different stages of an individual, groups, cultures, and so on.

2002

Abstract This paper discusses a few of the lessons we have learned developing a relational knowledge discovery system. The relationships among data instances in relational data provide extra information for “mining.” This additional information has the potential to greatly improve the quality of learned models. However, the dependencies among instances in the data also introduce new statistical challenges for learning algorithms.

PeachFuzz 2000. 19th International Conference of the North American Fuzzy Information Processing Society - NAFIPS (Cat. No.00TH8500), 2000

A method to mine relational databases for knowledge and generalizations is presented The concept of a network of expert systems with a user in the loop to provide domain knowledge of features is discussed. The network of expert systems is important to solve the problems of a knowledge-acquisition bottleneck. The example of battery design is described as a possible application of this technology. This application is important to the U.S. Marine Corps because better batteries will enhance ability of Marine battalions for the field deployment.

2004

Abstract As the amount of available data continues to increase, more and more effective means for discovering important patterns and relationships within that data are required. Although the power of automated tools continues to increase, we contend that greater gains can be achieved by coordinating results from a variety of tools and by enhancing the user's ability to direct the application of these tools.

Lecture Notes in Computer Science

In this paper, we present research trends carried out in the Orpailleur team at loria, showing how knowledge discovery and knowledge processing may be combined. The knowledge discovery in databases process (kdd) consists in processing a huge volume of data for extracting significant and reusable knowledge units. From a knowledge representation perspective, the kdd process may take advantage of domain knowledge embedded in ontologies relative to the domain of data, leading to the notion of "knowledge discovery guided by domain knowledge" or kddk. The kddk process is based on the classification process (and its multiple forms), e.g. for modeling, representing, reasoning, and discovering. Some applications are detailed, showing how kddk can be instantiated in an application domain. Finally, an architecture of an integrated kddk system is proposed and discussed.

2007