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 new cognitive model: Cloud model

Abstract: Randomness and fuzziness are the two most important uncertainties inherent in human cognition, which have attracted great attention in artificial intelligence research. In this paper, regarding linguistic terms or concepts as the basic units of human cognition, we propose a new cognitive model—cloud model, which can synthetically describe the randomness and fuzziness of concepts and implement the uncertain transformation between a qualitative concept and its quantitative instantiations. Furthermore, by analyzing in detail the statistical properties of normal cloud model, that is, an important kind of cloud models based on normal distribution and Gauss membership function, we show that normal cloud model can not only be viewed as a generalized normal distribution with weak constraints but also avoid the flaw of fuzzy sets to quantify the membership degree of an element as an accurate value between 0 and 1 and, therefore, may be more adaptive for the uncertainty description of linguistic concepts. Finally, two demonstration examples about the fractal evolution of plants and network topologies based on cloud models are given to illustrate the promising applications of cloud models in some more complex knowledge representation tasks. © 2009 Wiley Periodicals, Inc.

Ontology-based information extraction: An introduction and a survey of current approaches

Abstract: Information extraction (IE) aims to retrieve certain types of information from natural language text by processing them automatically. For example, an IE system might retrieve information about geopolitical indicators of countries from a set of web pages while ignoring other types of information. Ontology-based information extraction (OBIE) has recently emerged as a subfield of information extraction. Here, ontologies - which provide formal and explicit specifications of conceptualizations - play a crucial role in the IE process. Because of the use of ontologies, this field is related to knowledge representation and has the potential to assist the development of the Semantic Web. In this paper, we provide an introduction to ontology-based information extraction and review the details of different OBIE systems developed so far. We attempt to identify a common architecture among these systems and classify them based on different factors, which leads to a better understanding on their operation. We also discuss the implementation details of these systems including the tools used by them and the metrics used to measure their performance. In addition, we attempt to identify the possible future directions for this field.

Importing the Semantic Web in UDDI

Abstract: The web is moving from being a collection of pages toward a collection of services that interoperate through the Internet. A fundamental step toward this interoperation is the ability of automatically locating services on the bases of the functionalities that they provide. Such a functionality would allow services to locate each other and automatically interoperate. Location of web services is inherently a semantic problem, because it has to abstract from the superficial differences between representations of the services provided, and the services requested to recognize semantic similarities between the two.Current Web Services technology based on UDDI and WSDL does not make any use of semantic information and therefore fails to address the problem of matching between capabilities of services and allowing service location on the bases of what functionalities are sought, failing therefore to address the problem of locating web services. Nevertheless, previous work within DAML-S, a DAML-based language for service description, shows how ontological information collected through the semantic web can be used to match service capabilities. This work expands on previous work by showing how DAML-S Service Profiles, that describe service capabilities within DAML-S, can be mapped into UDDI records providing therefore a way to record semantic information within UDDI records. Furthermore we show how this encoded information can be used within the UDDI registry to perform semantic matching.

A review: Knowledge reasoning over knowledge graph

Abstract: Mining valuable hidden knowledge from large-scale data relies on the support of reasoning technology. Knowledge graphs, as a new type of knowledge representation, have gained much attention in natural language processing. Knowledge graphs can effectively organize and represent knowledge so that it can be efficiently utilized in advanced applications. Recently, reasoning over knowledge graphs has become a hot research topic, since it can obtain new knowledge and conclusions from existing data. Herein we review the basic concept and definitions of knowledge reasoning and the methods for reasoning over knowledge graphs. Specifically, we dissect the reasoning methods into three categories: rule-based reasoning, distributed representation-based reasoning and neural network-based reasoning. We also review the related applications of knowledge graph reasoning, such as knowledge graph completion, question answering, and recommender systems. Finally, we discuss the remaining challenges and research opportunities for knowledge graph reasoning.