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 Survey on Knowledge Graphs: Representation, Acquisition, and Applications

Abstract: Human knowledge provides a formal understanding of the world. Knowledge graphs that represent structural relations between entities have become an increasingly popular research direction toward cognition and human-level intelligence. In this survey, we provide a comprehensive review of the knowledge graph covering overall research topics about: 1) knowledge graph representation learning; 2) knowledge acquisition and completion; 3) temporal knowledge graph; and 4) knowledge-aware applications and summarize recent breakthroughs and perspective directions to facilitate future research. We propose a full-view categorization and new taxonomies on these topics. Knowledge graph embedding is organized from four aspects of representation space, scoring function, encoding models, and auxiliary information. For knowledge acquisition, especially knowledge graph completion, embedding methods, path inference, and logical rule reasoning are reviewed. We further explore several emerging topics, including metarelational learning, commonsense reasoning, and temporal knowledge graphs. To facilitate future research on knowledge graphs, we also provide a curated collection of data sets and open-source libraries on different tasks. In the end, we have a thorough outlook on several promising research directions.

OKBC: a programmatic foundation for knowledge base interoperability

Abstract: The technology for building large knowledge bases (KBs) is yet to witness a breakthrough so that a KB can be constructed by the assembly of prefabricated knowledge components. Knowledge components include both pieces of domain knowledge (for example, theories of economics or fault diagnosis) and KB tools (for example, editors and theorem provers). Most of the current KB development tools can only manipulate knowledge residing in the knowledge representation system (KRS) for which the tools were originally developed. Open Knowledge Base Connectivity (OKBC) is an application programming interface for accessing KRSs, and was developed to enable the construction of reusable KB tools. OKBC improves upon its predecessor, the Generic Frame Protocol (GFP), in several significant ways. OKBC can be used with a much larger range of systems because its knowledge model supports an assertional view of a KRS. OKBC provides an explicit treatment of entities that are not frames, and it has a much better way of controlling inference and specifying default values. OKBC can be used on practically any platform because it supports network transparency and has implementations for multiple programming languages. In this paper, we discuss technical design issues faced in the development of OKBC, highlight how OKBC improves upon GFP, and report on practical experiences in using it.

Building up and reasoning about architectural knowledge

Abstract: Architectural knowledge consists of architecture design as well as the design decisions, assumptions, context, and other factors that together determine why a particular solution is the way it is. Except for the architecture design part, most of the architectural knowledge usually remains hidden, tacit in the heads of the architects. We conjecture that an explicit representation of architectural knowledge is helpful for building and evolving quality systems. If we had a repository of architectural knowledge for a system, what would it ideally contain, how would we build it, and exploit it in practice? In this paper we describe a use-case model for an architectural knowledge base, together with its underlying ontology. We present a small case study in which we model available architectural knowledge in a commercial tool, the Aduna Cluster Map Viewer, which is aimed at ontology-based visualization. Putting together ontologies, use cases and tool support, we are able to reason about which types of architecting tasks can be supported, and how this can be done.

Computational generation of referring expressions: A survey

Abstract: This article offers a survey of computational research on referring expression generation (REG). It introduces the REG problem and describes early work in this area, discussing what basic assumptions lie behind it, and showing how its remit has widened in recent years. We discuss computational frameworks underlying REG, and demonstrate a recent trend that seeks to link REG algorithms with well-established Knowledge Representation techniques. Considerable attention is given to recent efforts at evaluating REG algorithms and the lessons that they allow us to learn. The article concludes with a discussion of the way forward in REG, focusing on references in larger and more realistic settings.

MnM: Ontology Driven Semi-automatic and Automatic Support for Semantic Markup

Abstract: An important precondition for realizing the goal of a semantic web is the ability to annotate web resources with semantic information. In order to carry out this task, users need appropriate representation languages, ontologies, and support tools. In this paper we present MnM, an annotation tool which provides both automated and semi-automated support for annotating web pages with semantic contents. MnM integrates a web browser with an ontology editor and provides open APIs to link to ontology servers and for integrating information extraction tools. MnM can be seen as an early example of the next generation of ontology editors, being web-based, oriented to semantic markup and providing mechanisms for large-scale automatic markup of web pages.