Knowledge representation and reasoning

About: Knowledge representation and reasoning is a research topic. Over the lifetime, 20078 publications have been published within this topic receiving 446310 citations. The topic is also known as: KR & KR².

A (Very) Brief History of Artificial Intelligence

Abstract: In this brief history, the beginnings of artificial intelligence are traced to philosophy, fiction, and imagination. Early inventions in electronics, engineering, and many other disciplines have influenced AI. Some early milestones include work in problems solving which included basic work in learning, knowledge representation, and inference as well as demonstration programs in language understanding, translation, theorem proving, associative memory, and knowledge-based systems. The article ends with a brief examination of influential organizations and current issues facing the field.

Classification of information sources using graph structures

Abstract: In a knowledge classification system, both the information sources and queries are processed to generate knowledge representation graph structures. The graph structures for both the query and the information sources are then converted to views and displayed to a searcher. By manipulating the graph structure views for each information source, the searcher can examine the source for relevance. A search can be performed by comparing the graph structure of the query to the graph structure of each information source by a graph matching computer algorithm. Information sources are classified by constructing hierarchies of knowledge representations. The simplest construction is obtained by using the knowledge representation of a query as the top of the hierarchy. The structures in the hierarchy are substructures of the query. The hierarchy of structures may also be constructed by using the knowledge representation of the query as the bottom of the hierarchy. Structures in the hierarchy, in this case, are structures that contain the query. The vertices of a graph structure view can be displayed on a computer screen next to the corresponding items, such as words, phrases and visual features, of an information source view. Selecting a vertex in the graph structure causes the selected vertex and vertices adjacent to the selected vertex to be “highlighted.” By selecting a succession of vertices in the graph structure, a searcher can perform knowledge navigation of the information source. By successively selecting items of the information source, a searcher can perform knowledge exploration of the information source.

Symbolic interpretation of artificial neural networks

Abstract: Hybrid intelligent systems that combine knowledge-based and artificial neural network systems typically have four phases, involving domain knowledge representation, mapping of this knowledge into an initial connectionist architecture, network training and rule extraction, respectively. The final phase is important because it can provide a trained connectionist architecture with explanation power and validate its output decisions. Moreover, it can be used to refine and maintain the initial knowledge acquired from domain experts. In this paper, we present three rule extraction techniques. The first technique extracts a set of binary rules from any type of neural network. The other two techniques are specific to feedforward networks, with a single hidden layer of sigmoidal units. Technique 2 extracts partial rules that represent the most important embedded knowledge with an adjustable level of detail, while the third technique provides a more comprehensive and universal approach. A rule-evaluation technique, which orders extracted rules based on three performance measures, is then proposed. The three techniques area applied to the iris and breast cancer data sets. The extracted rules are evaluated qualitatively and quantitatively, and are compared with those obtained by other approaches.

Concept learning in description logics using refinement operators

Abstract: With the advent of the Semantic Web, description logics have become one of the most prominent paradigms for knowledge representation and reasoning. Progress in research and applications, however, is constrained by the lack of well-structured knowledge bases consisting of a sophisticated schema and instance data adhering to this schema. It is paramount that suitable automated methods for their acquisition, maintenance, and evolution will be developed. In this paper, we provide a learning algorithm based on refinement operators for the description logic ALCQ including support for concrete roles. We develop the algorithm from thorough theoretical foundations by identifying possible abstract property combinations which refinement operators for description logics can have. Using these investigations as a basis, we derive a practically useful complete and proper refinement operator. The operator is then cast into a learning algorithm and evaluated using our implementation DL-Learner. The results of the evaluation show that our approach is superior to other learning approaches on description logics, and is competitive with established ILP systems.

The UMLS project: making the conceptual connection between users and the information they need

Abstract: Conceptual connections between users and information sources depend on an accurate representation of the content of available information sources, an accurate representation of specific user information needs, and the ability to match the two. Establishing such connections is a principal function of medical librarians. The goal of the National Library of Medicine's Unified Medical Language System (UMLS) project is to facilitate the development of conceptual connections between users and relevant machine-readable information. The UMLS model involves a combination of three centrally developed Knowledge Sources (a Metathesaurus, a Semantic Network, and an Information Sources Map) and a variety of smart interface programs that make use of these Knowledge Sources to help users in different environments find machine-readable information relevant to their particular practice or research problems. The third experimental edition of the UMLS Knowledge Sources was issued in the fall of 1992. Current priorities for the UMLS project include developing applications that make use of the Knowledge Sources and using feedback from these applications to guide ongoing enhancement and expansion of the Knowledge Sources. Medical librarians are involved heavily in the direction of the UMLS project, in the development of the Knowledge Sources, and in their experimental application. The involvement of librarians in reviewing, testing, and providing feedback on UMLS products will increase the likelihood that the UMLS project will achieve its goal of improving access to machine-readable biomedical information.