Flexible Knowledge Representation Research Articles

An open source knowledge graph ecosystem for the life sciences.

Translational research requires data at multiple scales of biological organization. Advancements in sequencing and multi-omics technologies have increased the availability of these data, but researchers face significant integration challenges. Knowledge graphs (KGs) are used to model complex phenomena, and methods exist to construct them automatically. However, tackling complex biomedical integration problems requires flexibility in the way knowledge is modeled. Moreover, existing KG construction methods provide robust tooling at the cost of fixed or limited choices among knowledge representation models. PheKnowLator (Phenotype Knowledge Translator) is a semantic ecosystem for automating the FAIR (Findable, Accessible, Interoperable, and Reusable) construction of ontologically grounded KGs with fully customizable knowledge representation. The ecosystem includes KG construction resources (e.g., data preparation APIs), analysis tools (e.g., SPARQL endpoint resources and abstraction algorithms), and benchmarks (e.g., prebuilt KGs). We evaluated the ecosystem by systematically comparing it to existing open-source KG construction methods and by analyzing its computational performance when used to construct 12 different large-scale KGs. With flexible knowledge representation, PheKnowLator enables fully customizable KGs without compromising performance or usability.

A Schematic Review of Knowledge Reasoning Approaches Based on the Knowledge Graph

In the contemporary world, the Internet technology and its implementation mode are advancing at a swift pace, leading to an exponential growth in the scale of Internet data. This data contains a significant amount of valuable knowledge. The effective organization and articulation of knowledge, as well as the ability to conduct thorough calculations and analyses, have garnered significant attention and developments within a particular environmental context. The utilization of knowledge graphs for knowledge reasoning has emerged as a prominent area of focus within the realm of knowledge graph research. It holds substantial significance in the realm of vertical search, intelligent answering, and various other applications. This article will be centered on fundamental principles of reasoning. The approach of knowledge reasoning oriented towards knowledge graphs is focused on the derivation of novel knowledge or the detection of erroneous knowledge through the utilization of pre-existing knowledge. In contrast to conventional knowledge reasoning approaches, the knowledge reasoning technique employed in knowledge graphs is characterized by greater diversity, owing to the succinct, adaptable, and flexible representation of knowledge.

Modeling scientometric indicators using a statistical data ontology

Scientometrics is the field of study and evaluation of scientific measures such as the impact of research papers and academic journals. It is an important field because nowadays different rankings use key indicators for university rankings and universities themselves use them as Key Performance Indicators (KPI). The purpose of this work is to propose a semantic modeling of scientometric indicators using the ontology Statistical Data and Metadata Exchange (SDMX). We develop a case study at Tecnologico de Monterrey following the Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology. We evaluate the benefits of storing and querying scientometric indicators using linked data as a mean for providing flexible and quick access knowledge representation that supports indicator discovery, enquiring and composition. The semi-automatic generation and further storage of this linked data in the Neo4j graph database enabled an updatable and quick access model.

New trends in scientific knowledge graphs and research impact assessment

New trends in scientific knowledge graphs and research impact assessment

Flexible knowledge representation and new similarity measure: Application on case based reasoning for waste treatment

In Case Based Reasoning the representation of a case and the similarity measures are two difficult steps in the conception of a system. Often, these steps are developed to resolve one kind of problem. However, in some of them such as recovery treatment processes generation, it is necessary for the system to be able to modify and adapt the representation of a case and the similarity measures with respect of the context and also the kind of solutions proposed. In this paper, authors introduce a new method to represent cases with a flexibility based on a structure in a connectionist model. This flexibility is needed due to the complexity of cases, the number of possible options and to ensure the durability of the system. In a second main contribution, authors introduce a method for the selection of source cases using abstraction, conceptualisation and inference mechanisms. Finally, authors test their system in a CBR developed on SWI-Prolog with different problems. The CBR is applied to find new recovery processes and try to estimate the new upgraded product generated.

A knowledge representation meta-model for rule-based modelling of signalling networks

The study of cellular signalling pathways and their deregulation in disease states, such as cancer, is a large and extremely complex task. Indeed, these systems involve many parts and processes but are studied piecewise and their literatures and data are consequently fragmented, distributed and sometimes--at least apparently--inconsistent. This makes it extremely difficult to build significant explanatory models with the result that effects in these systems that are brought about by many interacting factors are poorly understood. The rule-based approach to modelling has shown some promise for the representation of the highly combinatorial systems typically found in signalling where many of the proteins are composed of multiple binding domains, capable of simultaneous interactions, and/or peptide motifs controlled by post-translational modifications. However, the rule-based approach requires highly detailed information about the precise conditions for each and every interaction which is rarely available from any one single source. Rather, these conditions must be painstakingly inferred and curated, by hand, from information contained in many papers--each of which contains only part of the story. In this paper, we introduce a graph-based meta-model, attuned to the representation of cellular signalling networks, which aims to ease this massive cognitive burden on the rule-based curation process. This meta-model is a generalization of that used by Kappa and BNGL which allows for the flexible representation of knowledge at various levels of granularity. In particular, it allows us to deal with information which has either too little, or too much, detail with respect to the strict rule-based meta-model. Our approach provides a basis for the gradual aggregation of fragmented biological knowledge extracted from the literature into an instance of the meta-model from which we can define an automated translation into executable Kappa programs.

Applying Decisional DNA to Internet of Things: The Concept and Initial Case Study

In this article, we present a novel approach utilizing Decisional DNA to help the Internet of Things capture decisional events and reuse them for decision making in future operations. The Decisional DNA is a domain-independent, standard and flexible knowledge representation structure that allows its domains to acquire, store, and share experiential knowledge and formal decision events in an explicit way. We apply this approach to our current work—SmartBike, a sensor-equipped bicycle built under the concept of Internet of Things. By using Decisional DNA and machine learning algorithms, the SmartBike is able to distinguish its user's patterns based on past riding data. The presented conceptual approach demonstrates how Decisional DNA can be applied to the Internet of Things and bring to them intelligence required by forthcoming semantic networks.

Modelling Concept Prototype Competencies using a Developmental Memory Model

AbstractThe use of concepts is fundamental to human-level cognition, but there remain a number of open questions as to the structures supporting this competence. Specifically, it has been shown that humans use concept prototypes, a flexible means of representing concepts such that it can be used both for categorisation and for similarity judgements. In the context of autonomous robotic agents, the processes by which such concept functionality could be acquired would be particularly useful, enabling flexible knowledge representation and application. This paper seeks to explore this issue of autonomous concept acquisition. By applying a set of structural and operational principles, that support a wide range of cognitive competencies, within a developmental framework, the intention is to explicitly embed the development of concepts into a wider framework of cognitive processing. Comparison with a benchmark concept modelling system shows that the proposed approach can account for a number of features, namely concept-based classification, and its extension to prototype-like functionality.

Learn More about Your Data: A Symbolic Regression Knowledge Representation Framework

In this paper, we propose a flexible knowledge representation framework which utilizes Symbolic Regression to learn and mathematical expressions to represent the knowledge to be captured from data. In this approach, learning algorithms are used to generate new insights which can be added to domain knowledge bases supporting again symbolic regression. This is used for the generalization of the well-known regression analysis to fulfill supervised classification. The approach aims to produce a learning model which best separates the class members of a labeled training set. The class boundaries are given by a separation surface which is represented by the level set of a model function. The separation boundary is defined by the respective equation. In our symbolic approach, the learned knowledge model is represented by mathematical formulas and it is composed of an optimum set of expressions of a given superset. We show that this property gives human experts options to gain additional insights into the application domain. Furthermore, the representation in terms of mathematical formulas (e.g., the analytical model and its first and second derivative) adds additional value to the classifier and enables to answer questions, which sub-symbolic classifier approaches cannot. The symbolic representation of the models enables an interpretation by human experts. Existing and previously known expert knowledge can be added to the developed knowledge representation framework or it can be used as constraints. Additionally, the knowledge acquisition framework can be repeated several times. In each step, new insights from the search process can be added to the knowledge base to improve the overall performance of the proposed learning algorithms.

GAINING KNOWLEDGE THROUGH EXPERIENCE: DEVELOPING DECISIONAL DNA APPLICATIONS IN ROBOTICS

In this article, we explore an approach that integrates Decisional DNA, a domain-independent, flexible, and standard knowledge representation structure, with robots in order to test the usability and suitability of this novel knowledge representation structure. Core issues in using this Decisional DNA–based method include capturing of knowledge, storage and indexing of knowledge, organization of the knowledge base memory, and retrieval of knowledge from memory according to current problems. We demonstrate our approach in a set of experiments in which the robots capture knowledge from their tasks and are able to reuse such knowledge in subsequent tests.

ABSTRACT STRUCTURE OF CONFIGURATION SYSTEM FOR PRODUCT DATA MANAGEMENT SYSTEM

A product configurator is a system providing questions and sets of possible answers about products with sets of possible answers. This paper proposes a new idea for a configurator in which information about products are expressed as a structured net of interconnected classes of different types. Classes hold information about assemblies, product structural links, and logical constraints. Some classes have references to technical data on a product data management (PDM) system. Such a system allows flexible representation of knowledge about product configurations for production of aviation equipment. Santrauka Gaminio konfigūratorius yra programinė sistema, pateikianti klausimus apie produktą su galimais pasirinkimų variantais. Šiame darbe pasiūlyta nauja konfigūratoriaus idėja ir pateiktas konfigūracijų aprašo modelis. Šis modelis buvo kuriamas taip, kad juo būtų galima aprašyti realius, dažnai tobulinamus ir sudėtingos sandaros gaminius. Modelyje informacija apie gaminį pateikiama kaip tarpusavyje sujungtų įvairių klasių tinklas. Modelio klasėse saugoma informacija apie gaminio junginius, komponentų ryšius ir loginius apribojimus. Siūlomas konfigūracijų aprašo modelis leidžia lanksčiai perteikti inžinerines žinias apie įvairių gaminių, tarp jų ir aviacijos reikmėms skirtų gaminių, variantus.

A flexible representation of omic knowledge for thorough analysis of microarray data

BackgroundIn order to understand microarray data reasonably in the context of other existing biological knowledge, it is necessary to conduct a thorough examination of the data utilizing every aspect of available omic knowledge libraries. So far, a number of bioinformatics tools have been developed. However, each of them is restricted to deal with one type of omic knowledge, e.g., pathways, interactions or gene ontology. Now that the varieties of omic knowledge are expanding, analysis tools need a way to deal with any type of omic knowledge. Hence, we have designed the Omic Space Markup Language (OSML) that can represent a wide range of omic knowledge, and also, we have developed a tool named GSCope3, which can statistically analyze microarray data in comparison with the OSML-formatted omic knowledge data.ResultsIn order to test the applicability of OSML to represent a variety of omic knowledge specifically useful for analysis of Arabidopsis thaliana microarray data, we have constructed a Biological Knowledge Library (BiKLi) by converting eight different types of omic knowledge into OSML-formatted datasets. We applied GSCope3 and BiKLi to previously reported A. thaliana microarray data, so as to extract any additional insights from the data. As a result, we have discovered a new insight that lignin formation resists drought stress and activates transcription of many water channel genes to oppose drought stress; and most of the 20S proteasome subunit genes show similar expression profiles under drought stress. In addition to this novel discovery, similar findings previously reported were also quickly confirmed using GSCope3 and BiKLi.ConclusionGSCope3 can statistically analyze microarray data in the context of any OSML-represented omic knowledge. OSML is not restricted to a specific data type structure, but it can represent a wide range of omic knowledge. It allows us to convert new types of omic knowledge into datasets that can be used for microarray data analysis with GSCope3. In addition to BiKLi, by collecting various types of omic knowledge as OSML libraries, it becomes possible for us to conduct detailed thorough analysis from various biological viewpoints. GSCope3 and BiKLi are available for academic users at our web site .

AppBuilder for DSSTools: an application development environment for developing decision support systems in Prolog

A programming environment for developing complex decision support systems (DSSs) should support rapid prototyping and modular design, feature a flexible knowledge representation scheme and sound inference mechanisms, provide project management, and be domain-independent. We have previously developed DSSTools (Decision Support System Tools), a reusable, domain-independent, and open-ended toolkit for developing DSSs in Prolog. DSSTools provides modular design, a flexible knowledge representation scheme, and sound inference mechanisms to support development of any knowledge based system components of a DSS. It also provides tools for building the DSS interface and for integrating other non-Prolog components of a DSS such as simulation models, databases, or geographical information system, into a multi-component DSS. DSSTools does not provide project management, and its complex syntax makes rapid prototyping difficult. AppBuilder for DSSTools is a GUI-based application development environment for developing DSSs in DSSTools that supports rapid prototyping and project management. AppBuilder’s easy-to-use dialogues for managing and building knowledge based and top-level control components of a DSS free developers from having to memorize complex syntax and reduce development time without sacrificing the flexibility of the underlying toolkit. AppBuilder has been used to develop the Regeneration DSS, a system for predicting the regeneration of southern Appalachian hardwoods. AppBuilder is an application development environment for both prototyping and developing a complete DSS.

A framework for the comparative analysis and evaluation of knowledge representation schemes

Abstract This article sets forth a framework for comparing and evaluating knowledge representation schemes based on the requirements for “good” representation discussed in extant literature. The dimensions of the framework suggest that knowledge representation schemes should possess a suitable mix of four basic considerations: Representational adequacy (Variety of Expressiveness, Modularity, Semantics, and Organization of Knowledge); Inference Methods (Reasoning Strategies, Data, Control and Search Strategies); and Inference Requirements (Computational Efficiency, Transparency of line of control, Completeness, and Consistency). A comparative analysis and evaluation of four popular knowledge representation schemes—Logic, Production Rules, Semantic Nets, Frames—highlighting their strengths and weaknesses in the context of the framework is furnished as evidence of its inherent validity and usefulness. In conclusion, it is submitted that incorporating an appropriate blend of the various dimensions elucidated in this article could be a step towards developing more flexible, dynamic, and valuable knowledge representation schemes.

From knowledge bases to decision models

AbstractIn recent years there has been a growing interest among AI researchers in probabilistic and decision modelling, spurred by significant advances in representation and computation with network modelling formalisms. In applying these techniques to decision support tasks, fixed network models have proven to be inadequately expressive when a broad range of situations must be handled. Hence many researchers have sought to combine the strengths of flexible knowledge representation languages with the normative status and well-understood computational properties of decision-modelling formalisms and algorithms. One approach is to encode general knowledge in an expressive language, then dynamically construct a decision model for each particular situation or problem instance. We have developed several systems adopting this approach, which illustrate a variety of interesting techniques and design issues.

Efficient knowledge representation systems

AbstractFrame systems occupy an important place among formalisms for computer-based knowledge representation. A common concern about frame systems, however, is that they are not efficient enough. We argue that this is not necessarily true of all possible systems, and that the trade-off between generality and efficiency has not been fully explored. While many systems provide generality at the expense of performance, systems closer to the low end of the spectrum have not been investigated nearly as much. Those systems are well suited for applications that need flexible knowledge representation but cannot afford the high performance price.We describe in detail KR, a very efficient frame system that provides mechanisms for knowledge representation including user-defined inheritance and relations, object-oriented programming, and constraint maintenance. The system is simple and compact and does not include some of the more complex functionality, but it is highly optimized and offers excellent performance for a variety of applications.

A system for the representation of theorems and proofs

In previous work, the authors (Hausen-Tropper and Finley, 1988) have developed an extension of classical frame systems using generalized interframe relationships and concepts borrowed from modal logic. Such extended frame systems, termed modal frame systems by the authors, for which a non-deterministic query paradigm was presented, were intended to provide a flexible knowledge representation mechanism that would handle effectively some of the problems arising from exceptions, conflictual information, and defaults in the encoded knowledge. In the present work, an example of a knowledge base using the full richness of the extended frame representation system is presented. The example chosen is taken from an axiomatic development of the existence of real numbers with the intention of representing theorems and their proofs in the extended frames. The intent is to bring out the "deep structure" of the proofs, which might help us to better understand the proof discovery process. Thus, a simple syntactic encoding is not sufficient, rather the choice of a deep representation is sought. With such a representation in hand, the acquisition process should be fairly straight forward. A knowledge base built up in this way will then contain a corpus of knowledge about the given field of mathematics and could be interrogated in order to display the theorems of the system, their proofs, and the interrelationships between the proofs, theorems, axioms, and definitions of the system.

Multiple-problem subspaces in the knowledge-design process

Designing a knowledge base is viewed as a problem-solving task in which the skilled individual's knowledge and behavior must be mapped into the system, preserving the compiled knowledge acquired by experience. The expert's problem space is complex, but its breakdown into three major subspaces allows one to formalize this approach. Selective interfaces and high-level primitives as well as a flexible knowledge representation not only elicit knowledge and the learning of the design task by the expert. High-level programming, stressing the importance of the psychological as well as the physical descriptions, should allow the expert to bypass the current bottleneck of having to decompile the knowledge into a low-level language and then reconstruct the control structures to recover the expertise. Hence, knowledge design becomes a function available to domain experts themselves. The following reflections aim at the construction of a comprehensive theory of knowledge acquisition and transfer, in the context of a direct relation between the domain expert and the machine. This work is linked to the development and use of the NEXPERT™ hybrid knowledge-based system.

An Artificial Intelligence program to advise physicians regarding antimicrobial therapy

An antimicrobial therapy consultation system has been developed which utilizes a flexible representation of knowledge. The novel design facilitates interactive advice-giving sessions with physicians. An ability to display reasons for making decisions at the request of the user permits the program to serve a tutorial as well as consultative role. The feasibility of the judgmental rule approach which the program uses has been demonstrated with a limited knowledge base of approximately 100 rules. Its ultimate success as a clinically useful tool depends upon acquisition of additional rules and thus upon co-operation of infectious disease experts willing to improve the program's knowledge base. The techniques for acquisition, representation, and utilization of knowledge, plus considerations of natural language processing, draw upon and contribute to current Artificial Intelligence research.