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.

Representation independence, confinement and access control [extended abstract]

Abstract: Denotational semantics is given for a Java-like language with pointers, subclassing and dynamic dispatch, class oriented visibility control, recursive types and methods, and privilege-based access control. Representation independence (relational parametricity) is proved, using a semantic notion of confinement similar to ones for which static disciplines have been recently proposed.

Joint Video and Text Parsing for Understanding Events and Answering Queries

Abstract: We propose a framework for parsing video and text jointly for understanding events and answering user queries. Our framework produces a parse graph that represents the compositional structures of spatial information (objects and scenes), temporal information (actions and events) and causal information (causalities between events and fluents) in the video and text. The knowledge representation of our framework is based on a spatial-temporal-causal And-Or graph (S/T/C-AOG), which jointly models possible hierarchical compositions of objects, scenes and events as well as their interactions and mutual contexts, and specifies the prior probabilistic distribution of the parse graphs. We present a probabilistic generative model for joint parsing that captures the relations between the input video/text, their corresponding parse graphs and the joint parse graph. Based on the probabilistic model, we propose a joint parsing system consisting of three modules: video parsing, text parsing and joint inference. Video parsing and text parsing produce two parse graphs from the input video and text respectively. The joint inference module produces a joint parse graph by performing matching, deduction and revision on the video and text parse graphs. The proposed framework has the following objectives: Firstly, we aim at deep semantic parsing of video and text that goes beyond the traditional bag-of-words approaches; Secondly, we perform parsing and reasoning across the spatial, temporal and causal dimensions based on the joint S/T/C-AOG representation; Thirdly, we show that deep joint parsing facilitates subsequent applications such as generating narrative text descriptions and answering queries in the forms of who, what, when, where and why. We empirically evaluated our system based on comparison against ground-truth as well as accuracy of query answering and obtained satisfactory results.

Controlled English for knowledge representation

Abstract: Knowledge representation is a long-standing research area of computer science that aims at representing human knowledge in a form that computers can interpret. Most knowledge representation approaches, however, have suffered from poor user interfaces. It turns out to be difficult for users to learn and use the logic-based languages in which the knowledge has to be encoded. A new approach to design more intuitive but still reliable user interfaces for knowledge representation systems is the use of controlled natural language (CNL). CNLs are subsets of natural languages that are restricted in a way that allows their automatic translation into formal logic. A number of CNLs have been developed but the resulting tools are mostly just prototypes so far. Furthermore, nobody has yet been able to provide strong evidence that CNLs are indeed easier to understand than other logic-based languages. The goal of this thesis is to give the research area of CNLs for knowledge representation a shift in perspective: from the present explorative and proof-of-concept-based approaches to a more engineering-focussed point of view. For this reason, I introduce theoretical and practical building blocks for the design and application of controlled English for the purpose of knowledge representation. I first show how CNLs can be defined in an adequate and simple way by the introduction of a novel grammar notation and I describe efficient algorithms to process such grammars. I then demonstrate how these theoretical concepts can be implemented and how CNLs can be embedded in knowledge representation tools so that they provide intuitive and powerful user interfaces that are accessible even to untrained users. Finally, I discuss how the understandability of CNLs can be evaluated. I argue that the understandability of CNLs cannot be assessed reliably with existing approaches, and for this reason I introduce a novel testing framework. Experiments based on this framework show that CNLs are not only easier to understand than comparable languages but also need less time to be learned and are preferred by users.

Conceptual graphs for semantics and knowledge processing

Abstract: This paper discusses the representational and algorithmic power of the conceptual graph model for natural language semantics and knowledge processing. Also described is a Prolog-like resolution method for conceptual graphs, which allows to perform deduction on very large semantic domains. The interpreter developed is similar to a Prolog interpreter in which the terms are any conceptual graphs and in which the unification algorithm is replaced by a specialized algorithm for conceptual graphs.