Complex Knowledge Representation Research Articles

Applications of Knowledge Graphs in Telecommunication Systems Management

Telecommunications systems management represents a technical challenge with profound economic implications. Many different sources of information must be integrated, and complex decisions must be automatically taken and executed. In this article, we review the usage of Knowledge Graphs in this domain since they are a powerful tool for dealing with data integration and complex knowledge representation. The main insights are that they have been applied in several areas of Telecommunications Management, such as resource/service discovery and monitoring, with a particular focus on Software-Defined Networks. Their main advantages are their extensibility and interoperability. However, data quality and graph performance are still challenges, and work is yet to be done for its application to the rest of the telecommunication management areas.

Nomenclature over 5 years in TaxonWorks: Approach, implementation, limitations and outcomes

We are now over four decades into digitally managing the names of Earth's species. As the number of federating (i.e., software that brings together previously disparate projects under a common infrastructure, for example TaxonWorks) and aggregating (e.g., International Plant Name Index, Catalog of Life (CoL)) efforts increase, there remains an unmet need for both the migration forward of old data, and for the production of new, precise and comprehensive nomenclatural catalogs. Given this context, we provide an overview of how TaxonWorks seeks to contribute to this effort, and where it might evolve in the future. In TaxonWorks, when we talk about governed names and relationships, we mean it in the sense of existing international codes of nomenclature (e.g., the International Code of Zoological Nomenclature (ICZN)). More technically, nomenclature is defined as a set of objective assertions that describe the relationships between the names given to biological taxa and the rules that determine how those names are governed. It is critical to note that this is not the same thing as the relationship between a name and a biological entity, but rather nomenclature in TaxonWorks represents the details of the (governed) relationships between names. Rather than thinking of nomenclature as changing (a verb commonly used to express frustration with biological nomenclature), it is useful to think of nomenclature as a set of data points, which grows over time. For example, when synonymy happens, we do not erase the past, but rather record a new context for the name(s) in question. The biological concept changes, but the nomenclature (names) simply keeps adding up. Behind the scenes, nomenclature in TaxonWorks is represented by a set of nodes and edges, i.e., a mathematical graph, or network (e.g., Fig. 1). Most names (i.e., nodes in the network) are what TaxonWorks calls "protonyms," monomial epithets that are used to construct, for example, bionomial names (not to be confused with "protonym" sensu the ICZN). Protonyms are linked to other protonyms via relationships defined in NOMEN, an ontology that encodes governed rules of nomenclature. Within the system, all data, nodes and edges, can be cited, i.e., linked to a source and therefore anchored in time and tied to authorship, and annotated with a variety of annotation types (e.g., notes, confidence levels, tags). The actual building of the graphs is greatly simplified by multiple user-interfaces that allow scientists to review (e.g. Fig. 2), create, filter, and add to (again, not "change") the nomenclatural history. As in any complex knowledge-representation model, there are outlying scenarios, or edge cases that emerge, making certain human tasks more complex than others. TaxonWorks is no exception, it has limitations in terms of what and how some things can be represented. While many complex representations are hidden by simplified user-interfaces, some, for example, the handling of the ICZN's Family-group name, batch-loading of invalid relationships, and comparative syncing against external resources need more work to simplify the processes presently required to meet catalogers' needs. The depth at which TaxonWorks can capture nomenclature is only really valuable if it can be used by others. This is facilitated by the application programming interface (API) serving its data (https://api.taxonworks.org), serving text files, and by exports to standards like the emerging Catalog of Life Data Package. With reference to real-world problems, we illustrate different ways in which the API can be used, for example, as integrated into spreadsheets, through the use of command line scripts, and serve in the generation of public-facing websites. Behind all this effort are an increasing number of people recording help videos, developing documentation, and troubleshooting software and technical issues. Major contributions have come from developers at many skill levels, from high school to senior software engineers, illustrating that TaxonWorks leads in enabling both technical and domain-based contributions. The health and growth of this community is a key factor in TaxonWork's potential long-term impact in the effort to unify the names of Earth's species.

Ontology-Based Approach to Semantically Enhanced Question Answering for Closed Domain: A Review

For many users of natural language processing (NLP), it can be challenging to obtain concise, accurate and precise answers to a question. Systems such as question answering (QA) enable users to ask questions and receive feedback in the form of quick answers to questions posed in natural language, rather than in the form of lists of documents delivered by search engines. This task is challenging and involves complex semantic annotation and knowledge representation. This study reviews the literature detailing ontology-based methods that semantically enhance QA for a closed domain, by presenting a literature review of the relevant studies published between 2000 and 2020. The review reports that 83 of the 124 papers considered acknowledge the QA approach, and recommend its development and evaluation using different methods. These methods are evaluated according to accuracy, precision, and recall. An ontological approach to semantically enhancing QA is found to be adopted in a limited way, as many of the studies reviewed concentrated instead on NLP and information retrieval (IR) processing. While the majority of the studies reviewed focus on open domains, this study investigates the closed domain.

Semi-Automated Formalization and Representation of the Engineering Knowledge Extracted From Spreadsheet Data

The development of new methods and tools for formalization and representation of complex knowledge in the context of the creation of intelligent systems remains in the scope of scientific research. There is a trend to automate the knowledge formalization and representation by using various sources of information, in particular, spreadsheets. This paper proposes a novel approach to the semi-automatic formalization and representation of engineering knowledge in the form of conceptual models and knowledge base codes from spreadsheet data. Our approach contains three phases: (I) rule-driven data transformation source spreadsheet tables to a specific canonical form (data preprocessing), (II) domain knowledge formalization and representation via the extraction and aggregation of conceptual model fragments from canonicalized tables, (III) model-driven synthesizing knowledge base and source codes from a domain model. The approach is implemented by our tools: TABBYXL provides the development of a software application for the spreadsheet data canonicalization; Personal Knowledge Base Designer is used to build and aggregate conceptual models fragments, as well as to construct a target knowledge base and generation source codes. Our case study on the industrial safety inspection (ISI) demonstrates the applicability of the approach for prototyping knowledge bases containing decision-making rules.

A New Linguistic Petri Net for Complex Knowledge Representation and Reasoning

Fuzzy Petri nets (FPNs) are a useful instrument for modelling expert systems to conduct knowledge representation and reasoning. Many studies have been carried out for improving the performance of FPNs in terms of their accurate representation of knowledge and power of approximate reasoning. Nevertheless, the current representation methods with FPNs are unable to handle the uncertain linguistic knowledge given by domain experts and the reliability of their judgments. In addition, the existing reasoning algorithms have no way to capture the interrelationship of the propositions with the same output transition. Therefore, we present a new type of FPNs, called 2-dimensional uncertain linguistic Petri nets (2DULPNs). The 2-dimensional uncertain linguistic variables (2DULVs) and Choquet integral are combined for knowledge representation and reasoning for the first time. The truth degrees of propositions, thresholds and certainty values of linguistic production rules are denoted as 2DULVs. Some new aggregated operators based on Choquet integral are proposed and used in the approximate reasoning to capture the interactions among antecedent propositions. Finally, an equipment fault diagnosis example is provided to illustrate the correctness and effectiveness of the proposed 2DULPN model.

Process Knowledge Graph Construction Method for Process Reuse

Aiming at the problems of process knowledge reuse and sharing led by the difficulty of unified representation of complex and diverse process knowledge, a process knowledge graph construction method for process reuse is proposed. Firstly, to ensure the accuracy and universality of data schema for process knowledge, the basic schema of process knowledge graph is constructed based on step-nc. Secondly, to improve process knowledge graph basic schema, the process knowledge graph extensive schema is established through process knowledge analysis and process knowledge combination. Meanwhile, to construct the process knowledge graph schema, the existing rules of experience are represented by SWRL language. Moreover, to instantiate the process knowledge graph schema, the process cases are analyzed under the guidance of process knowledge graph schema and the similarity between process cases is computed by latent semantic analysis technology. And then, process cases are transformed into the structured process knowledge graph representation, and process knowledge graph data is obtained. Finally, the process knowledge graph construction application platform is developed to verify the feasibility of the proposed method.

SAMURAI: A batch and streaming context architecture for large-scale intelligent applications and environments

Over the past decade intelligent environments have grown in sophistication. Many recent paradigm shifts − such as the Internet of Things (IoT), Ambient Assisted Living (AAL), e-health and telemedicine − envision large distributed networks of intelligent devices, applications and services that are sensitive to the presence of people and responsive to their needs. Cutting edge technologies will autonomously and collectively operate on a growing volume of information arriving at ever increasing velocities to transparently and non-intrusively support users during their activities. Especially the escalating variety of information that applications have to deal with is a non-trivial concern. Making sense out of heterogeneous and pervasive streams of sensor events to anticipate and address the needs of users is a ubiquitous challenge that many interactive context-aware applications in intelligent environments frequently face. Furthermore, software solutions that continuously interpret the tasks and contexts of a variety of individuals with different needs are often faced with scalability concerns. We present SAMURAI, a batch and streaming context architecture that integrates and exposes well-known components for complex event processing, machine learning, and knowledge representation. SAMURAI builds upon key concepts of the Lambda architecture and big data enabling technologies to achieve horizontal scalability and responsive interaction with its users. Two application cases validate the feasibility and performance of our context architecture, demonstrating near-linear scalability, flexible elasticity and smooth interaction capabilities.

Ontology models of the impacts of agriculture and climate changes on water resources: Scenarios on interoperability and information recovery

Agriculture is both highly dependent on water resources, and impacting on these resources. Regardless of advances in the area, the impacts of water scarcity and climatic changes on agriculture, as well as the impacts of agriculture on water resources, remain uncertain. Potentially, collaborative systems can support the management and information sharing of multifaceted and large scale data sources, providing valuable and indispensable information for research. However, these solutions rely on semantic interoperability, the construction of complex knowledge representation models, as well as information recovery. This work describes interoperability issues in the engineering process of the OntoAgroHidro, an ontology that represents knowledge about impacts of agricultural activities and climatic changes on water resources. The paper presents representative scenarios and questions, and discusses the reuse and integration of concepts using knowledge visualization techniques. Experiments on the information recovery scenario point out the potential and limitations of the OntoAgroHidro.

An Ontological Approach for Representation of Educational Program

The research of ontology application in knowledge sharing in current information society has attracted much effort in recent years. This paper studies expressive power of description logic OWL-DL ontology to express complicated knowledge in practical situation and constructs an ontology to represent complicated knowledge of educational program to help students to understand the requirements and build their study plans that fulfill these requirements. This ontology demonstrates the applicability of expressive ontology in representation of complex knowledge for knowledge sharing. Therefore, this paper shows the promising possibility of extending ontology utilization to knowledge sharing research and application by employing rich semantic features of recent ontology specification.

Ontology-driven execution of clinical guidelines

Clinical guidelines (CG) contain general descriptions, defined by health care organisations, of the way in which a particular pathology should be treated. Their adoption in daily care offers several benefits to both patients and practitioners, such as the standardisation of the delivered care and the reduction of errors, but, at the same time, there are several issues that limit their application. CGs are designed to cover a disease taking into account the available evidence but are not designed to be deployed in a particular hospital or healthcare institution. CGs include general recommendations that should be translated according the particular settings before adoption in daily care. This adoption should also specify accountable information about the responsible actors of performing actions in healthcare teams in order to avoid errors arising during delegation/assignment of tasks. In addition, this enactment is not performed taking into account a central knowledge base or a single actor. This paper proposes the combination of a multi-agent system modelling complex healthcare organisations and knowledge representation techniques in order to build a general framework for enabling the enactment of CGs in the context of a medical centre. As a main contribution, the ontological paradigm and the expressiveness of modern ontology languages are used to design, implement and exploit a medico-organisational ontology aimed to provide the semantics required to support the execution of clinical guidelines. The knowledge-driven guideline enactment is managed by a multi-agent system modelling in a distributed fashion the clinical entities involved in the care delivery.

A new cognitive model: Cloud model

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.

An autonomic problem determination system using adaptive multi-levels dictionaries and singular value decomposition techniques

An autonomic Problem Determination (PD) system can adapt to changing environments, react to existing or new error conditions and predict possible problems from a collection of seemingly unrelated events and take actions proactively. In this paper, we propose such a system with the use of the Singular Value Decomposition (SVD) technique and dynamic and adaptive multi-levels dictionaries. Our proposed PD system applies an iterative method that enables a dynamic interaction between a set of sparsely related events and the current dictionaries, with its entries being continuously updated to reflect the relative importance of each event. Updating the dictionaries triggers an update of the SVD matrix, thereby accelerating the convergence of the SVD matrix. Our PD system can capture knowledge in a hierarchical form for complex knowledge representation. It does not require a formal knowledge model or intensive training by examples. It is an efficient system with sufficient accuracy for autonomic and adaptive problem determination.

Applying inductive logic programming to predicting gene function

One of the fastest advancing areas of modern science is functional genomics. This science seeks to understand how the complete complement of molecular components of living organisms (nucleic acid, protein, small molecules and so on) interact together to form living organisms. Functional genomics is of interest to AI because the relationship between machines and living organisms is central to AI and because the field is an instructive and fun domain to apply and sharpen AI tools and ideas, requiring complex knowledge representation, reasoning, learning, and so on. This article describes two machine learning (inductive logic programming [ILP])-based approaches to the bioinformatic problem of predicting protein function from amino acid sequence. The first approach is based on using ILP as a way of bootstrapping from conventional sequence-based homology methods. The second approach used protein-functional ontologies to provide function classes and a hybrid ILP method to predict function directly from sequence. Both ILP approaches were successful in producing accurate prediction rules that could biologically be interpreted. The work was also of interest to machine learning research because it highlighted the flexibility of ILP systems in dealing with heterogeneous data, the importance of problems where classes are related hierarchically, and problems where examples have more than one functional class.

Emergence of self-learning fuzzy systems by a new virus DNA-based evolutionary algorithm

In this article, we propose a new approach to the virus DNA–based evolutionary algorithm (VDNA-EA) to implement self-learning of a class of Takagi-Sugeno (T-S) fuzzy controllers. The fuzzy controllers use T-S fuzzy rules with linear consequent, the generalized input fuzzy sets, Zadeh fuzzy logic and operators, and the generalized defuzzifier. The fuzzy controllers are proved to be nonlinear proportional-integral (PI) controllers with variable gains. The fuzzy rules are discovered automatically and the design parameters in the input fuzzy sets and the linear rule consequent are optimized simultaneously by the VDNA-EA. The VDNA-EA uses the VDNA encoding method that stemmed from the structure of the VDNA to encode the design parameters of the fuzzy controllers. We use the frameshift decoding method of the VDNA to decode the DNA chromosome into the design parameters of the fuzzy controllers. In addition, the gene transfer operation and bacterial mutation operation inspired by a microbial evolution phenomenon are introduced into the VDNA-EA. Moreover, frameshift mutation operations based on the DNA genetic operations are used in the VDNA-EA to add and delete adaptively fuzzy rules. Our encoding method can significantly shorten the code length of the DNA chromosomes and improve the encoding efficiency. The length of the chromosome is variable and it is easy to insert and delete parts of the chromosome. It is suitable for complex knowledge representation and is easy for the genetic operations at gene level to be introduced into the VDNA-EA. We show how to implement the new method to self-learn a T-S fuzzy controller in the control of a nonlinear system. The fuzzy controller can be constructed automatically by the VDNA-EA. Computer simulation results indicate that the new method is effective and the designed fuzzy controller is satisfactory. © 2003 Wiley Periodicals, Inc.

Visual art and metaphors in complex knowledge representation

L'Intelligence Artificielle s'interesse aux modeles adequats de representation des connaissances. Il existe des modeles, qui ont prouve leur utilite, bases sur des cadres structures ou logiques. Toutefois, dans le cas de systemes tres complexes, un manque existe concernant des modeles qui nous permettraient d'eclairer toutes les relations et de devoiler les structures cachees de la realite de reference

Design knowledge representation and control for the structural design of ships

Our main concern is to identify the major issues related to representing and controlling design knowledge for large scale structures such as ships in routine parametric redesign. In our view, the most difficult problems can occur in the complexity of representing the design object and the need for a formal way of describing rules for classification that play the most important role in providing design expertise used for the structural design of ships. Hence, we present a complex object-based knowledge representation whose key feature is that the contents of all the knowledge-base, including the design process, are expressed only by objects and classes. Also, we devise an inference mechanism, called the CORE (complex object-based inference) mechanism, that is based on a hypothesis-object. To illustrate how the CORE mechanism works in a realistic domain, design systems for the deck structure and midship section of bulk cargo ships are implemented, and some running examples are presented.

Modelling the student in Pitagora 2.0

With the aim to individualise human-computer interaction, an Intelligent Tutoring System (ITS) has to keep track of what and how the student has learned. Hence, it is necessary to maintain a Student Model (SM) dealing with complex knowledge representation, such as incomplete and inconsistent knowledge and belief revision. With this in view, the main objective of this paper is to present and discuss the student modelling approach we have adopted to implement Pitagora 2.0, an ITS based on a co-operative learning model, and designed to support teaching-learning activities in a Euclidean Geometry context. In particular, this approach has led us to develop two distinct modules that cooperate to implement the SM of Pitagora 2.0. The first module resembles a “classical” student model, in the sense that it maintains a representation of the current student knowledge level, which can be used by the teacher in order to tune its teaching strategies to the specific student needs. In addition, our system contains a second module that implements a virtual partner, called companion. This module consists of a computational model of an “average student” which cooperates with the student during the learning process. The above mentioned module calls for the use of machine learning algorithms that allow the companion to improve in parallel with the real student. Computational results obtained when testing this module in simulation experiments are also presented.

Knowledge-based interpretation of seismic data in the Intelligent Monitoring System

Abstract The Intelligent Monitoring System (IMS) provides a new capability for automated and interactive analysis of data to detect and locate seismic events recorded by a network of seismic stations. IMS integrates emerging technologies in artificial intelligence, database management, computer graphics, and distributed processing into an operational system used for routine bulletin production and associated research tasks. The first version of IMS (Bache et al., 1990a,b; Bratt et al., 1990) was designed for detection and location of regional events recorded by the two high-frequency arrays in Norway (NORESS and ARCESS). This has been extensively revised and expanded to become IMS Version 2 which is designed to detect and locate all seismic events recorded by an arbitrary seismic network. Since March 1991 it has been operated continuously to process the data from four high-frequency arrays (adding FINESA in Finland and GERESS in Germany). For some periods data from as many as seven 3-component stations in Eurasia have also been included in the processing. The most important new element is ESAL (Expert System for Association and Location) which interprets signal detections to form and locate seismic events. It is programmed in the ART expert system shell which provides the knowledge representation framework and inference mechanisms for complex and knowledge-rich rule-based reasoning. The current version of ESAL represents knowledge through approximately 200 ART rules that are configured through about 300 user-specified parameters and tables. The IMS architecture and operational procedures are designed to facilitate acquisition of new knowledge for ESAL. Knowledge acquisition methods being used include: Bayesian analysis, training neural-nets, statistical analysis to estimate parameters configuring rules, computing fuzzy-logic membership functions, and formulating new rules. Only the Bayesian probabilities are discussed in detail here. They provide a compact representation of complex knowledge about station-specific differences in phase characteristics. As an example, we describe the rules used for automated identification of detected regional Sn, Lg, and Rg phases. Using a Bayesian analysis technique, we quantify the differences in S-phase characteristics. The data show that they fall into two classes with GERESS distinct from the three Fennoscandia arrays.

Knowledge-based control in manufacturing automation

Abstract This paper is an introduction to some current research work into knowledge-based control techniques in manufacturing, highlighting developments which are likely to be applicable in the near future. Examples of such work dealing with the integration of manufacturing facilities and diagnosis and error recovery are described. It also addresses more speculative work requiring more complex knowledge representation methods. The applicability of this work to task level programming of robot manipulators and more general planning tasks in manufacturing automation is discussed.