Knowledge Representation Research Research Articles

Modelling Value-Oriented Legal Reasoning in LogiKEy

The logico-pluralist LogiKEy knowledge engineering methodology and framework is applied to the modelling of a theory of legal balancing, in which legal knowledge (cases and laws) is encoded by utilising context-dependent value preferences. The theory obtained is then used to formalise, automatically evaluate, and reconstruct illustrative property law cases (involving the appropriation of wild animals) within the Isabelle/HOL proof assistant system, illustrating how LogiKEy can harness interactive and automated theorem-proving technology to provide a testbed for the development and formal verification of legal domain-specific languages and theories. Modelling value-oriented legal reasoning in that framework, we establish novel bridges between the latest research in knowledge representation and reasoning in non-classical logics, automated theorem proving, and applications in legal reasoning.

The Shapley Value of Inconsistency Measures for Functional Dependencies

Quantifying the inconsistency of a database is motivated by various goals including reliability estimation for new datasets and progress indication in data cleaning. Another goal is to attribute to individual tuples a level of responsibility to the overall inconsistency, and thereby prioritize tuples in the explanation or inspection of dirt. Therefore, inconsistency quantification and attribution have been a subject of much research in Knowledge Representation and, more recently, in Databases. As in many other fields, a conventional responsibility sharing mechanism is the Shapley value from cooperative game theory. In this paper, we carry out a systematic investigation of the complexity of the Shapley value in common inconsistency measures for functional-dependency (FD) violations. For several measures we establish a full classification of the FD sets into tractable and intractable classes with respect to Shapley-value computation. We also study the complexity of approximation in intractable cases.

Toward a Symbolic AI Approach to the WHO/ACSM Physical Activity & Sedentary Behavior Guidelines

The World Health Organization and the American College of Sports Medicine have released guidelines on physical activity and sedentary behavior, as part of an effort to reduce inactivity worldwide. However, to date, there is no computational model that can facilitate the integration of these recommendations into health solutions (e.g., digital coaches). In this paper, we present an operational and machine-readable model that represents and is able to reason about these guidelines. To this end, we adopted a symbolic AI approach that combines two paradigms of research in knowledge representation and reasoning: ontology and rules. Thus, we first present HeLiFit, a domain ontology implemented in OWL, which models the main entities that characterize the definition of physical activity, as defined per guidance. Then, we describe HeLiFit-Rule, a set of rules implemented in the RDFox Rule language, which can be used to represent and reason with these recommendations in concrete real-world applications. Furthermore, to ensure a high level of syntactic/semantic interoperability across different systems, our framework is also compliant with the FHIR standard. Through motivating scenarios that highlight the need for such an implementation, we finally present an evaluation of our model that provides results that are both encouraging in terms of the value of our solution and also provide a basis for future work.

Towards a new monolingual Hungarian explanatory dictionary

The Lexical Knowledge Representation Research Group at the Department of Lexicology is one of the youngest research groups of the Hungarian Research Centre for Linguistics, founded in February 2020. The group is currently working on a new version of a monolingual explanatory dictionary partly based on The Explanatory Dictionary of the Hungarian Language. The aim is to compile an up-to-date online dictionary of contemporary Hungarian (2001–2020) by corpus-driven methods. The present article describes The Explanatory Dictionary of the Hungarian Language and the Comprehensive Dictionary of Hungarian by presenting their history, the circumstances of their compilation, and the basic editorial guidelines. Then it outlines how the corpus for the planned dictionary is to be set up and how this corpus is to be analysed.

RDF 1.1: Knowledge Representation and Data Integration Language for the Web

Resource Description Framework (RDF) can seen as a solution in today’s landscape of knowledge representation research. An RDF language has symmetrical features because subjects and objects in triples can be interchangeably used. Moreover, the regularity and symmetry of the RDF language allow knowledge representation that is easily processed by machines, and because its structure is similar to natural languages, it is reasonably readable for people. RDF provides some useful features for generalized knowledge representation. Its distributed nature, due to its identifier grounding in IRIs, naturally scales to the size of the Web. However, its use is often hidden from view and is, therefore, one of the less well-known of the knowledge representation frameworks. Therefore, we summarise RDF v1.0 and v1.1 to broaden its audience within the knowledge representation community. This article reviews current approaches, tools, and applications for mapping from relational databases to RDF and from XML to RDF. We discuss RDF serializations, including formats with support for multiple graphs and we analyze RDF compression proposals. Finally, we present a summarized formal definition of RDF 1.1 that provides additional insights into the modeling of reification, blank nodes, and entailments.

Online Knowledge Learning Model Based on Gravitational Field Theory

Currently, low-dimensional embedded representation learning models are the mainstream approach in knowledge representation research, due to ease of calculation and ability to utilize the spatial relationship between knowledge areas, which benefit from static knowledge learning. However, these models cannot update and learn knowledge online. Although using update strategies to update the knowledge base has been proposed by some scholars, this still requires retraining of knowledge and does not use the previous learning parameters and models. TransOnLine, an online knowledge learning method based on the theory of gravitational field, inspired by the fact that the forces acting on two objects in a gravitational field are only related to the distances between objects, rebalances the knowledge space caused by new knowledge through dynamic programming via introducing the spatial energy function and energy transfer function to solve the above problems. TransOnLine can reuse the parameters and models of previous learning. Experiments show that the performance of the TransOnLine method is close to state-of-the-art methods, and it is suitable for online learning and updating a relational-intensive knowledge base.

Belief Update within Propositional Fragments

Belief change within the framework of fragments of propositional logic is one of the main and recent challenges in the knowledge representation research area. While previous research works focused on belief revision, belief merging, and belief contraction, the problem of belief update within fragments of classical logic has not been addressed so far. In the context of revision, it has been proposed to refine existing operators so that they operate within propositional fragments, and that the result of revision remains in the fragment under consideration. This approach is not restricted to the Horn fragment but also applicable to other propositional fragments like Krom and affine fragments. We generalize this notion of refinement to any belief change operator. We then focus on a specific belief change operation, namely belief update. We investigate the behavior of the refined update operators with respect to satisfaction of the KM postulates and highlight differences between revision and update in this context.

Semantic Interpretation of Multi-Modal Human-Behaviour Data

This special issue presents interdisciplinary research—at the interface of artificial intelligence, cognitive science, and human-computer interaction—focussing on the semantic interpretation of human behaviour. The special issue constitutes an attempt to highlight and steer foundational methods research in artificial intelligence, in particular knowledge representation and reasoning, for the development of human-centred cognitive assistive technologies. Of specific interest and focus have been application outlets for basic research in knowledge representation and reasoning and computer vision for the cognitive, behavioural, and social sciences.

Commonsense Interpretation of Triangle Behavior

The ability to infer intentions, emotions, and other unobservable psychological states from people's behavior is a hallmark of human social cognition, and an essential capability for future Artificial Intelligence systems. The commonsense theories of psychology and sociology necessary for such inferences have been a focus of logic-based knowledge representation research, but have been difficult to employ in robust automated reasoning architectures. In this paper we model behavior interpretation as a process of logical abduction, where the reasoning task is to identify the most probable set of assumptions that logically entail the observable behavior of others, given commonsense theories of psychology and sociology. We evaluate our approach using Triangle-COPA, a benchmark suite of 100 challenge problems based on an early social psychology experiment by Fritz Heider and Marianne Simmel. Commonsense knowledge of actions, social relationships, intentions, and emotions are encoded as defeasible axioms in first-order logic. We identify sets of assumptions that logically entail observed behaviors by backchaining with these axioms to a given depth, and order these sets by their joint probability assuming conditional independence. Our approach solves almost all (91) of the 100 questions in Triangle-COPA, and demonstrates a promising approach to robust behavior interpretation that integrates both logical and probabilistic reasoning.

From Concept Representations to Ontologies: A Paradigm Shift in Health Informatics?

ObjectivesThis work aims at uncovering challenges in biomedical knowledge representation research by providing an understanding of what was historically called "medical concept representation" and used as the name for a working group of the International Medical Informatics Association.MethodsBibliometrics, text mining, and a social media survey compare the research done in this area between two periods, before and after 2000.ResultsBoth the opinion of socially active groups of researchers and the interpretation of bibliometric data since 1988 suggest that the focus of research has moved from "medical concept representation" to "medical ontologies".ConclusionsIt remains debatable whether the observed change amounts to a paradigm shift or whether it simply reflects changes in naming, following the natural evolution of ontology research and engineering activities in the 1990s. The availability of powerful tools to handle ontologies devoted to certain areas of biomedicine has not resulted in a large-scale breakthrough beyond advances in basic research.

An Integration Ontology for Components Composition

Software components composition can improve the efficiency of knowledge management by composing individual components together for complex distributed application. There are two main areas of research in knowledge representation for component composition: the syntactic based approach and the semantic-based approach. In this paper, the authors propose an integrated ontology-supported software component composition. The authors’ approach provides dual modes to perform component composition. Ontologies are employed to enrich semantics at both components description and composition. The authors demonstrate that their search engine SEC++ fulfills automated component composition, in particular, and knowledge management in general.

Reconciling description logics and rules

Description logics (DLs) and rules are formalisms that emphasize different aspects of knowledge representation: whereas DLs are focused on specifying and reasoning about conceptual knowledge, rules are focused on nonmonotonic inference. Many applications, however, require features of both DLs and rules. Developing a formalism that integrates DLs and rules would be a natural outcome of a large body of research in knowledge representation and reasoning of the last two decades; however, achieving this goal is very challenging and the approaches proposed thus far have not fully reached it. In this paper, we present a hybrid formalism of MKNF + knowledge bases , which integrates DLs and rules in a coherent semantic framework. Achieving seamless integration is nontrivial, since DLs use an open-world assumption, while the rules are based on a closed-world assumption. We overcome this discrepancy by basing the semantics of our formalism on the logic of minimal knowledge and negation as failure (MKNF) by Lifschitz. We present several algorithms for reasoning with MKNF + knowledge bases, each suitable to different kinds of rules, and establish tight complexity bounds.

Representing function: Relating functional representation and functional modeling research streams

This paper is an informal description of some recent insights about what a device function is, how it arises in response to needs, and how function arises from the structure of a device and the functions of its components. These results formalize and clarify a set of contending intuitions about function that researchers have had. The paper relates the approaches, results, and goals of this stream of research, called functional representation (FR), with the functional modeling (FM) stream in engineering. Despite the occurrence of the term function in the two streams, often the results and techniques in the two streams appear not to have much to do with each other. I argue that, in fact, the two streams are performing research that is mutually complementary. FR research provides the basic layer for device ontology in a formal framework that helps to clarify the meanings of terms such as function and structure, and also to support representation of device knowledge for automated reasoning. FM research provides another layer in device ontology, by attempting to identify behavior primitives that are applicable to subsets of devices, with the hope that functions can be described in those domains with an economy of terms. This can lead to useful catalogs of functions and devices in specific areas of engineering. With increased attention to formalization, the work in FM can provide domain-specific terms for FR research in knowledge representation and automated reasoning.

Trouble ahead: Propositional attitudes and metadata

AbstractWe argue that the natural pressure to develop increasingly expressive metadata systems will soon present new and very difficult problems to the metadata community. In particular we draw attention to propositional attitudes, which will be important for a wide variety of metadata applications, including representations of trust in ecommerce, and representations of provenance, expert assessments, and social context in the management of cultural objects. However propositional attitudes are extremely difficult to represent in a way that supports automated inferencing. Both the web and the museum informatics community have recognized this problem and stipulate limitations to their systems. But eventually this problem will require more functional resolution. We believe that intentionality‐related problems, like the representation of propositional attitudes will soon be more important to metadata and knowledge representation research than traditional modeling problems, such as the expressiveness vs. efficiency tradeoff.

A View of Semantic Web from Knowledge Representation Research

A View of Semantic Web from Knowledge Representation Research

Formalizations of Commonsense Psychology

■ The central challenge in commonsense knowledge representation research is to develop content theories that achieve a high degree of both competency and coverage. We describe a new methodology for constructing formal theories in commonsense knowledge domains that complements traditional knowledge representation approaches by first addressing issues of coverage. We show how a close examination of a very general task (strategic planning) leads to a catalog of the concepts and facts that must be encoded for general commonsense reasoning. These concepts are sorted into a manageable number of coherent domains, one of which is the representational area of commonsense human memory. We then elaborate on these concepts using textual corpus-analysis techniques, where the conceptual distinctions made in natural language are used to improve the definitions of the concepts that should be expressible in our formal theories. These representational areas are then analyzed using more traditional knowledge representation techniques, as demonstrated in this article by our treatment of commonsense human memory.

Applying epistemic logic and evidential theory to strategic arguments

This paper extends work originally reported in a previous SMJ article on strategic policy issues. To accomplish this extension, we draw on research from separate but related areas. Concepts from the application of first-order logic to organizational theory and formal methods from research in knowledge representation are combined using epistemic logic and evidential theory. It is shown that the original formulation of the policy evaluation problem fits neatly into this framework. This allows application of more formal and powerful methods for representing and evaluating the evidence offered in support of strategic policy statements. © 1998 John Wiley & Sons, Ltd.

Reasoning about structured objects: Knowledge representation meets databases

Structured objects are items with defined properties that are to be represented in a computer system. Research in Knowledge Representation (KR) and in Database Design (DB) has produced languages for describing structured objects. Although different in the particular means for defining properties, both areas share the goal of representing a part of the world in a structured way. Moreover, the rise of object-centred formalisms in the last decade has significantly influenced the convergence of languages.

The DenK-architecture: A fundamental approach to user-interfaces

In this paper we present the basic principles underlying the DenK-system, a generic cooperative interface combining linguistic and visual interaction. The system integrates results from fundamental research in knowledge representation, communication, natural language semantics and pragmatics, and object-oriented animation. Our design incorporates a cooperative and knowledgeable electronic assistant that communicates with a user in natural language, and an application domain, which is presented visually. The assistant, that we call thecooperator, has an information state that is represented in a rich form of Type Theory, a formalism that enables us to model the inherent cognitive dynamics of a dialogue participant. Pragmatic issues in man-machine interaction, concerning the use of natural language and knowledge in cooperative communication, are central to our approach.

NSF workshop on Visual Information Management Systems

One of the most important technologies needed across many traditional areas as well as emerging new frontiers of computing, is the management of visual information. For example, most of the Grand Challenge applications, under the High Performance Computing and Communication (HPCC) initiative, require management of large volumes of non-alphanumeric information, computations, communication, and visualization of results. Considering the growing need and interest in the organization and retrieval of visual and other non-alphanumeric information, and in order to stimulate academic projects in this area, a workshop on Visual Information Management Systems (VIMS) was sponsored by the National Science Foundation. This workshop was held in Redwood, CA, on February 24-25, 1992. The goal of the workshop was to identify major research areas that should be addressed by researchers for VIMS that would be useful in scientific, industrial, medical, environmental, educational, entertainment, and other applications. The major findings of the workshop were that VIMS require new techniques in all aspects of databases, computer vision, and knowledge representation and management; and that such techniques are best developed in the context of concrete, practical applications. VIMS will provide impetus and testbeds for many techniques being explored for the future database systems. Researchers from image processing and understanding, knowledge representation and knowledge based systems, and databases must work very closely to develop VIMS. Such systems should be developed in the context of applications that will be of immediate interest in industrial, medical, or scientific contexts. Without concrete applications and ambitious implementation projects, most of the important and difficult issues are likely to be ignored. Considering the interdisciplinary nature of the research in this area, a few major research projects in this area are essential for its growth. Increased emphasis on HPCC by many Federal agencies can help in the rapid development of VIMS technology. Similarly, by addressing some of the Grand Challenges, research interested in VIMS can understand critical issues and develop techniques to solve them, in a concrete and useful context. Parallel processing is essential for implementing VIMS. As is well known, the processing of images is one of the most computation-intensive tasks. For entering images in databases and for performing required operations at query time, an enormous volume of data must be processed. Parallel computing will be essential for implementing a VIMS that can insert images in reasonable time and provide fast response to user queries. The computational requirements of video databases are likely to be one of the most demanding. It is very likely that video databases will require research in highly parallel-pipelined architectures. In interdisciplinary research areas such as VIMS, most important and difficult problems usually fall through the cracks. The three most relevant areas for the development of VIMS are: databases, computer vision, and knowledge representation. Data compression, fault-tolerant real time access to image data through networks, and parallel processing issues should be addressed in the context of databases for VIMS. VIMS should not be considered as an application of the existing state of the art in any of these fields to manage and process images. Database researchers must understand the issues specific to managing and processing images and other forms of data by granting them the same status that has been given to alphanumeric information. Computer vision researchers should identify features required for interactive image understanding, rather than their discipline's current emphasis on automatic techniques, and develop techniques to compute features in interactive environments. Most knowledge representation research has been concerned with symbolic k knowledge. For VIMS and HPCC applications, techniques for representing symbolic and non-symbolic representations at the same level will be required. Reasoning approaches that can deal with such representations will be useful not only in VIMS, but in many other applications also. Finally, performance issues pose a significant challenge in all aspects of VIMS, from memory organization to information retrieval.