Traditional Knowledge Representation Research Articles

Extending the description logic [formula omitted] with threshold concepts induced by concept measures

In applications of AI systems where exact definitions of the important notions of the application domain are hard to come by, the use of traditional logic-based knowledge representation languages such as Description Logics may lead to very large and unintuitive definitions, and high complexity of reasoning. To overcome this problem, we define new concept constructors that allow us to define concepts in an approximate way. To be more precise, we present a family τEL(m) of extensions of the lightweight Description Logic EL that use threshold constructors for this purpose. To define the semantics of these constructors we employ graded membership functions m, which for each individual in an interpretation and concept yield a number in the interval [0,1] expressing the degree to which the individual belongs to the concept in the interpretation. Threshold concepts C⋈t for ⋈∈{<,≤,>,≥} then collect all the individuals that belong to C with degree ⋈t. The logic τEL(m) extends EL with threshold concepts whose semantics is defined relative to a function m. To construct appropriate graded membership functions, we show how concept measures ∼ (which are graded generalizations of subsumption or equivalence between concepts) can be used to define graded membership functions m∼. Then we introduce a large class of concept measures, called simi-d, for which the logics τEL(m∼) have good algorithmic properties. Basically, we show that reasoning in τEL(m∼) is NP/coNP-complete without TBox, PSpace-complete w.r.t. acyclic TBoxes, and ExpTime-complete w.r.t. general TBoxes. The exception is the instance problem, which is already PSpace-complete without TBox w.r.t. combined complexity. While the upper bounds hold for all elements of simi-d, we could prove some of the hardness results only for a subclass of simi-d. This article considerably improves on and generalizes results we have shown in three previous conference papers and it provides detailed proofs of all our results.

Clinical decision support system for hypertension medication based on knowledge graph

BackgroundHigh prevalence of hypertension and complicated medication knowledge have presented challenges to hypertension clinicians and general practitioners. Clinical decision support systems (CDSSs) are developed to aid clinicians in decision making. Current clinical knowledge is stored in fixed templates, which are not intuitive for clinicians and limit the knowledge reusability. Knowledge graphs (KGs) store knowledge in a way that is not only intuitive to humans but also processable by computers directly. However, existing medical KGs such as UMLS and CMeKG are general purpose and thus lack enough knowledge to enable hypertension medication. MethodsWe first construct a KG specific to hypertension medication according to the Chinese hypertension guideline and then develop the corresponding CDSS to implement hypertension medication and knowledge management. Current advances in knowledge graph representation and modelling are researched and applied in the complex medical knowledge representation. Traditional knowledge representation and KG representation are innovatively combined in the storage of the KG to enable convenient knowledge management and easy application by the CDSS. Along a predefined reasoning path in the KG, the CDSS finally accomplishes the hypertension medication by applying knowledge stored in the KG. 124 health records of a hypertension Chief Physician from Beijing Anzhen Hospital, Capital Medical University, are collected to evaluate the system metrics on the single drug recommendation task. Results and conclusionThe proposed CDSS has functions of medication knowledge graph management and hypertension medication decision support. With elaborate design on knowledge representation, knowledge management is intuitive and convenient. By virtue of the KG, medication recommendations are highly visualized and explainable. Experiments on 124 health records with 90% guideline compliance collected from hospitals in single class recommendation task achieve 91%, 83% and 77% on recall, hit@3 and MRR metrics respectively, which demonstrates the quality of the KG and effectiveness of the system.

Multi-modal knowledge graphs representation learning via multi-headed self-attention

Traditional knowledge graphs (KG) representation learning focuses on the link information between entities, and the effectiveness of learning is influenced by the complexity of KGs. Considering a multi-modal knowledge graph (MKG), due to the introduction of considerable other modal information(such as images and texts), the complexity of KGs further increases, which degrades the effectiveness of representation learning. To resolve this solve the problem, this study proposed the multi-modal knowledge graphs representation learning via multi-head self-attention (MKGRL-MS) model, which improved the effectiveness of link prediction by adding rich multi-modal information to the entity. We first generated a single-modal feature vector corresponding to each entity. Then, we used multi-headed self-attention to obtain the attention degree of different modal features of entities in the process of semantic synthesis. In this manner, we learned the multi-modal feature representation of entities. New knowledge representation is the sum of traditional knowledge representation and an entity’s multi-modal feature representation. Simultaneously, we successfully train our model on two existing models and two different datasets and verified its versatility and effectiveness on the link prediction task.

A Cognitive Knowledge-based Model for an Academic Adaptive e-Advising System

Aim/Purpose: This study describes a conceptual model, based on the principles of concept algebra that can provide intelligent academic advice using adaptive, knowledge-based feedback. The proposed model advises students based on their traits and academic history. The system aims to deliver adaptive advice to students using historical data from previous and current students. This data-driven approach utilizes a cognitive knowledge-based (CKB) model to update the weights (values that indicate the strength of relationships between concepts) that exist between student’s performances and recommended courses. Background: A research study conducted at the Public Authority for Applied Education and Training (PAAET), a higher education institution in Kuwait, indicates that students’ have positive perceptions of the e-Advising system. Most students believe that PAAET’s e-Advising system is effective because it allows them to check their academic status, provides a clear vision of their academic timeline, and is a convenient, user-friendly, and attractive online service. Student advising can be a tedious element of academic life but is necessary to fill the gap between student performance and degree requirements. Higher education institutions have prioritized assisting undecided students with career decisions for decades. An important feature of e-Advising systems is personalized feedback, where tailored advice is provided based on students' characteristics and other external parameters. Previous e-Advising systems provide students with advice without taking into consideration their different attributes and goals. Methodology: This research describes a model for an e-Advising system that enables students to select courses recommended based on their personalities and academic performance. Three algorithms are used to provide students with adaptive course selection advice: the knowledge elicitation algorithm that represents students' personalities and academic information, the knowledge bonding algorithm that combines related concepts or ideas within the knowledge base, and the adaptive e-Advising model that recommends relevant courses. The knowledge elicitation algorithm acquires student and academic characteristics from data provided, while the knowledge bonding algorithm fuses the newly acquired features with existing information in the database. The adaptive e-Advising algorithm provides recommended courses to students based on existing cognitive knowledge to overcome the issues associated with traditional knowledge representation methods. Contribution: The design and implementation of an adaptive e-Advising system are challenging because it relies on both academic and student traits. A model that incorporates the conceptual interaction between the various academic and student-specific components is needed to manage these challenges. While other e-Advising systems provide students with general advice, these earlier models are too rudimentary to take student characteristics (e.g., knowledge level, learning style, performance, demographics) into consideration. For the online systems that have replaced face-to-face academic advising to be effective, they need to take into consideration the dynamic nature of contemporary students and academic settings. Findings: The proposed algorithms can accommodate a highly diverse student body by providing information tailored to each student. The academic and student elements are represented as an Object-Attribute-Relationship (OAR) model. Recommendations for Practitioners: The model proposed here provides insight into the potential relationships between students’ characteristics and their academic standing. Furthermore, this novel e-Advising system provides large quantities of data and a platform through which to query students, which should enable developing more effective, knowledge-based approaches to academic advising. Recommendation for Researchers: The proposed model provides researches with a framework to incorporate various academic and student characteristics to determine the optimal advisory factors that affect a student’s performance. Impact on Society: The proposed model will benefit e-Advising system developers in implementing updateable algorithms that can be tested and improved to provide adaptive advice to students. The proposed approach can provide new insight to advisors on possible relationships between student’s characteristics and current academic settings. Thus, providing a means to develop new curriculums and approaches to learning. Future Research: In future studies, the proposed algorithms will be implemented, and the adaptive e-Advising model will be tested on real-world data and then further improved to cater to specific academic settings. The proposed model will benefit e-Advising system developers in implementing updateable algorithms that can be tested and improved to provide adaptive advisory to students. The approach proposed can provide new insight to advisors on possible relationships between student’s characteristics and current academic settings. Thus, providing a means to develop new curriculums and approaches to course recommendation.

Representing Strategies

Quite some work in the ATL-tradition uses the differences between various types of strategies (positional, uniform, perfect recall) to give alternative semantics to the same logical language. This paper contributes to another perspective on strategy types, one where we characterise the differences between them on the syntactic (object language) level. This is important for a more traditional knowledge representation view on strategic content. Leaving differences between strategy types implicit in the semantics is a sensible idea if the goal is to use the strategic formalism for model checking. But, for traditional knowledge representation in terms of object language level formulas, we need to extent the language. This paper introduces a strategic STIT syntax with explicit operators for knowledge that allows us to charaterise strategy types. This more expressive strategic language is interpreted on standard ATL-type concurrent epistemic game structures. We introduce rule-based strategies in our language and fruitfully apply them to the representation and characterisation of positional and uniform strategies. Our representations highlight crucial conditions to be met for strategy types. We demonstrate the usefulness of our work by showing that it leads to a critical reexamination of coalitional uniform strategies.

Standard model of knowledge representation

Knowledge representation is the core of artificial intelligence research. Knowledge representation methods include predicate logic, semantic network, computer programming language, database, mathematical model, graphics language, natural language, etc. To establish the intrinsic link between various knowledge representation methods, a unified knowledge representation model is necessary. According to ontology, system theory, and control theory, a standard model of knowledge representation that reflects the change of the objective world is proposed. The model is composed of input, processing, and output. This knowledge representation method is not a contradiction to the traditional knowledge representation method. It can express knowledge in terms of multivariate and multidimensional. It can also express process knowledge, and at the same time, it has a strong ability to solve problems. In addition, the standard model of knowledge representation provides a way to solve problems of non-precision and inconsistent knowledge.

Document Clustering Using an Ontology-Based Vector Space Model

This paper introduces a novel conceptual framework to support the creation of knowledge representations based on enriched Semantic Vectors, using the classical vector space model approach extended with ontological support. One of the primary research challenges addressed here relates to the process of formalization and representation of document contents, where most existing approaches are limited and only take into account the explicit, word-based information in the document. This research explores how traditional knowledge representations can be enriched through incorporation of implicit information derived from the complex relationships (semantic associations) modelled by domain ontologies with the addition of information presented in documents. The relevant achievements pursued by this work are the following: (i) conceptualization of a model that enables the semantic enrichment of knowledge sources supported by domain experts; and (ii) implementation of a proof-of-concept, named SENSE (Semantic Enrichment knowledge Sources).

Management of Knowledge Sources Supported by Domain Ontologies: Building and Construction Case Studys

SummaryThis paper introduces a novel conceptual framework to support the creation of knowledge representations based on enriched semantic vectors, using the classical vector space model approach extended with ontological support. This work is focused on collaborative engineering projects where knowledge plays a key role in the process. Collaboration is the arena, engineering projects are the target and knowledge is the currency used to provide harmony into the arena since it can potentially support innovation and, hence, a successful collaboration. The test bed for the assessment of the approach comes from the Building and Construction sector, which is challenged with significant problems for exchanging, sharing and integrating information among actors. Semantic gaps or lack of meaning definition at the conceptual and technical levels, for example, are problems fundamentally originated through the employment of representations to map the ‘world’ into models in an endeavour to anticipate other actors’ views, vocabulary and even motivations. One of the primary research challenges addressed in this work relates to the process of formalization and representation of document contents, where most existing approaches are limited and only take into account the explicit, word‐based information in the document. The research described in this paper explores how traditional knowledge representations can be enriched through incorporation of implicit information derived from the complex relationships (semantic associations) modelled by domain ontologies with the addition of information presented in documents, by providing a baseline for facilitating knowledge interpretation and sharing between humans and machines. Preliminary results were collected using a clustering algorithm for document classification, which indicates that the proposed approach does improve the precision and recall of classifications. Future work and open issues are also discussed. Copyright © 2015 John Wiley &amp; Sons, Ltd.

Facilitating knowledge sharing and reuse in building and construction domain: an ontology-based approach

This paper brings a contribution focused on collaborative engineering projects where knowledge plays a key role in the process. Collaboration is the arena, engineering projects are the target, knowledge is the currency used to provide harmony into the arena since it can potentially support innovation and, hence, a successful collaboration. The building and construction domain is challenged with significant problems for exchanging, sharing and integrating information between actors. For example, semantic gaps or lack of meaning definition at the conceptual and technical level, are problems fundamentally created through the employment of representations to map the `world' into models in an endeavour to anticipate different actors' views, vocabulary, and objectives. One of the primary research challenges addressed in this work is the process of formalization and representation of document content, where most existing approaches are limited in their capability and only take into account the explicit, word-based information in the document. The research described in this paper explores how traditional knowledge representations can be enriched by incorporation of implicit information derived from the complex relationships (the Semantic Associations) modelled by domain ontologies combined with the information presented in documents, thereby providing a baseline for facilitating knowledge interpretation and sharing between humans and machines. The paper introduces a novel conceptual framework for representation of knowledge sources, where each knowledge source is semantically represented (within its domain of use) by a Semantic Vector. This work contributes to the enrichment of Semantic Vectors, using the classical vector space model approach extended with ontological support, employing ontology concepts and their relations in the enrichment process. The test bed for the assessment of the approach is the Building and Construction industry, using an appropriate B&C domain Ontology. Preliminary results were collected using a clustering algorithm for document classification, which indicates that the proposed approach does improve the precision and recall of classifications. Future work and open issues are also discussed.

Fuzzy Knowledge Representation and Reasoning of the Smart Grid Based on Medium Logic and its Application

Medium logic completely reflects both the contrary relation and the contradictory relation. This paper introduces fuzzy privative and opposition privative in traditional knowledge representation. It takes a power transmission and distribution program for example, adopts a ratio function of distance to define the fuzzy set's member functions. Moreover, one confirmed method is presented to define the value as well as the threshold value in fuzzy production rules.Finally it discusses fuzzy knowledge reasoning by a specific example.

Creation of extension knowledge base system about intelligent detection in dendrobium huoshanense photosynthesis process

Aiming at the limitations of the existing knowledge representations in intelligent detection, a novel extension-based knowledge representation (EKR) is proposed. The definitions, grammar rules, and formal semantics of EKR are presented. A rhombus solving strategy (RSS) based on EKR is discussed in detail, including creation of the problem oriented model, extension operator, the solution formation of contradictions problem and extended inference of matter-element. A knowledge base system based on EKR and RSS is developed, which is applied in intelligent detection in the Dendrobium huoshanense photosynthesis process (DHPP). More reasonable results are obtained than traditional rule-based system. The EKR is feasible in intelligent detection to solve the limitations of traditional knowledge representations.

Web of Data and Web of Entities: Identity and Reference in Interlinked Data in the Semantic Web

Using web standards, such as uniform resource identifiers (URIs), XML and HTTP, for naming and describing resources which are not information objects is the key difference between the Web as we know it today and the Semantic Web. Naming and interlinking this type of resources by HTTP URIs (instead of individual constants in a formal language) is the key feature which distinguishes traditional knowledge representation from web-scale knowledge representation. However, this use of URIs brought back attention to the old philosophical problem of identity and reference in a new form. In this paper, we analyze the new version of the problem, provide a formal model for dealing with it when interlinking knowledge on the Web, and argue for the need of a distinction between the use of URIs for describing and accessing resources, and the use of URIs for fixing the reference. We show that in the current practice of linking data these roles are not clearly distinguished, and that this fact may cause unwanted effects and prevent some basic forms of data integration. We also discuss the role of an entity name system as a potential piece of infrastructure for fixing the reference in the Semantic Web.

What is the Semantic Web and What Will it Do for eScience?

The paper discusses what kind of entity the proposed Semantic Web (SW) is, in terms of the relationship of natural language structure to knowledge representation (KR). It argues that there are three distinct views on the issue: first, that the SW is basically a renaming of the traditional AI knowledge representation task, with all the problems and challenges of that task. If that is the case, as many believe, then there is no particular reason to expect progress in this new form of presentation, as all the traditional problems of logic and representation reappear and it will be no more successful outside the narrow scientific domains where KR seems to work even though the formal ontology movement has brought some benefits. The paper contains some discussion of the relationship of current SW doctrine to representation issues covered by traditional AI, and also discusses issues of how far SW proposals are able to deal with difficult relationships in parts of concrete science. Secondly, there is a view that the SW will be the WorldWideWeb with its constituent documents annotated so as to yield their content or meaning structure more directly. This view of the SW makes natural language processing central as the procedural bridge from texts to KR, usually via a form of automated Information Extraction. This view is discussed in some detail and it is argued that this is in fact the only way of justifying the structures used as KR for the SW. There is a third view, possibly Berners-Lee's own, that the SW is about trusted databases as the foundation of a system of web processes and services, but it is argued that this ignores the whole history of the web as a textual system, and gives no better guarantee of agreed meanings for terms than the other two approaches.

Implementation of a Referent Tracking System

Traditional database resources and semantic Web technology face problems when there is a need to keep track of individuals in reality as these individuals undergo changes of various sorts. We describe an application which implements the referent tracking paradigm in which each real world entity has its own unique ID. The application is designed to be able to store relationships between tracked instances and also to be extendable to very high orders of magnitude (in principle to accept numbers of entries in the billions). Our approach is based on ontologies grounded in realism, but it can be extended also to information that is captured using the terminologies or concept-based ontologies used in traditional knowledge representation systems. The repository uses resource description framework (RDF) as representation format, and it can thus be queried with query languages such as SPARQL, SeRQL, and RQL, thereby providing support for reasoning over multiple ontologies.

Constraint-based knowledge representation for individualized instruction

Traditional knowledge representations were developed to encode complete explicit and executable programs, a goal that makes them less than ideal for representing the incomplete and partial knowledge of a student. In this paper, we discuss state constraints, a type of knowledge unit originally invented to explain how people can detect and correct their own errors. Constraint-based student modeling has been implemented in several intelligent tutoring systems (ITS) so far, and the empirical data verifies that students learn while interacting with these systems. Furthermore, learning curves are smooth when plotted in terms of individual constraints, supporting the psychological appropriateness of the representation. We discuss the differences between constraints and other representational formats, the advantages of constraint-based models and the types of domains in which they are likely to be useful.

Knowledge Is Power: A View from the Semantic Web

emerging Semantic Web focuses on bringing knowledge representationlike capabilities to Web applications in a Web-friendly way. ability to put knowledge on the Web, share it, and reuse it through standard Web mechanisms provides new and interesting challenges to artificial intelligence. In this paper, I explore the similarities and differences between the Semantic Web and traditional AI knowledge representation systems, and see if I can validate the analogy The Semantic Web is to KR as the Web is to hypertext.

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.

Predictive Statistical Models for User Modeling

The limitations of traditional knowledge representation methods for modeling complex human behaviour led to the investigation of statistical models. Predictive statistical models enable the anticipation of certain aspects of human behaviour, such as goals, actions and preferences. In this paper, we motivate the development of these models in the context of the user modeling enterprise. We then review the two main approaches to predictive statistical modeling, content-based and collaborative, and discuss the main techniques used to develop predictive statistical models. We also consider the evaluation requirements of these models in the user modeling context, and propose topics for future research.

Book review: The Phenomenon of Commonsense Reasoning: nonmonotonicity, action and information by Dimitrios Thanassas (Ellis Horwood, 1992)

This book deals with a well-known problem in artificial intelligence, namely commonsense reasoning. The author tackles this problem from the standpoint of classical AI, building on theories of nonmonotonic reasoning developed by McCarthy, McDermott, Poole, Reiter, and Allen. However, the proposals given in this book differ from traditional approaches in two respects: (1) a novel approach to integrating formal models of commonsense reasoning with traditional knowledge representation formalisms, such as frames and scripts is suggested, and (2) a "typicality logic" is described, and the claim is made that default logic formalisms that have been proposed in the literature are "specializations" of the author's typicality logic.

Multimedia as a vehicle for knowledge modeling in expert systems

This article examines the integration of expert systems (ES) with multimedia technology. Multimedia allows a wide variety of sensory information and knowledge to be stored in ES and thus allows ES to be more effective. Additionally, this article explores the types of knowledge that should be embedded in expert systems to provide users with support in decision-making tasks. Traditional knowledge representation schemes, such as frames and production rules, have not always proved effective for presenting requisite knowledge to the user. Traditional knowledge representations force users to translate their conceptualizations of problem attributes and characteristics into text form and then convert back to their own conceptualizations in interpreting the recommended solutions. To provide better support to users, both a knowledge representation and a knowledge presentation are necessary to foster communication, knowledge explanation, as well as user learning and corporate training. The notions of knowledge representation and knowledge presentation are derived from the concepts of symbolic and replica models proposed by Chapanis (1961). Replica models, a form of knowledge presentation, enhance communication and learning. The use of multimedia as a vehicle for knowledge presentation provides a powerful interface with a full range of audio/visual features to express knowledge more completely. This greatly enhances a corporation's ability to capture, embed, and distribute knowledge.