Knowledge Representation Formalisms Research Articles

Computational complexity of hybrid interval temporal logics

Interval logics are very expressive temporal formalisms, but reasoning with them is often undecidable or has high computational complexity. As a result, a vast number of approaches limiting their expressive power—in order to obtain better computational behaviour—have been introduced. Unfortunately, due to such restrictions, interval logics often lose referentiality, that is, the capacity to refer to specific time intervals, which is crucial for temporal representation and reasoning. The computational price that needs to be paid in order to regain referentiality is not well studied and our research aims to fill this gap. In particular we study the main interval temporal logic, called the Halpern-Shoham logic, and its low complexity modifications. To regain referentiality in these modifications, we extend the language with the hybrid machinery—nominals and satisfaction operators—and classify the obtained logics according to their computational complexity. We show that such a hybridisation often makes tractable logics intractable but not undecidable. This allows us to construct hybrid interval temporal logics which are referential as well as maintain a good compromise between expressiveness and complexity; it makes them valuable formalisms for temporal knowledge representation. We also introduce a class of models which, due to a specific interplay between the interpretation of modal operators and a structure of time, makes reasoning in interval logics computationally hard even in the absence of the hybrid machinery.

A Question Answering Framework Based on Hybridization of Deep Learning and Semantic Web Techniques

The question answering (QA) system has been existing for several years. QA systems are divided into different processes such as question processing, document processing, paragraph extraction, answer extraction, question analysis, phrase mapping, disambiguation, query construction, querying the Knowledge Base (KB), and result ordering on user response respectively. Based on these processes, many models have been developed using approaches ranging from linguistic, statistical, and pattern matching. Popular models are Feedback, Refinement and Extended VocabularY Aggregation (FREyA), PowerAqua, SemSek, Semantic Interpretation of User Queries for QA on Interlinked Data (SINA), DEep Answers for maNy Naturally Asked questions (DEANNA), gAnswer, SemGraph, OKBQA (Open Knowledge Base and Question Answering) and Semantic Question Answering (SQA), for performance evaluation, these mostly focus on higher precision, recall, and/or F-measure. However, most of these models are constrained in the following operations: the combination of knowledge bases from different sources, formalism for knowledge representation to support interoperability, optimization of query construction and generation, ranking of responses, and support for set operations (union, sorting, comparison, and aggregation on user query) during query generation. This research proposes a hybrid of recurrent neural network and semantic-web-based question answering as the (RNNSQA) framework that combines heterogeneous knowledge sources and improves on state-of-the-art query generation mechanisms to allow for integration of comprehensive question-type operations, and set-based operations listed above. First, there would be a combination of techniques that is Natural Language Processing (NLP) and Recurrent Neural Network (RNN) techniques in classifying questions into types. Secondly, the semantic web technique is then employed in generating (SPARQL Protocol and RDF (Resource Description Framework) Query Language) SPARQL-based candidate queries. The result of this study is an enhanced QA framework with improved query translation and construction capability.

Equivalence in Argumentation Frameworks with a Claim-Centric View – Classical Results with Novel Ingredients

A common feature of non-monotonic logics is that the classical notion of equivalence does not preserve the intended meaning in light of additional information. Consequently, the term strong equivalence was coined in the literature and thoroughly investigated. In the present paper, the knowledge representation formalism under consideration are claim-augmented argumentation frameworks (CAFs) which provide a formal basis to analyze conclusion-oriented problems in argumentation by adapting a claim-focused perspective. CAFs extend Dung AFs by associating a claim to each argument representing its conclusion. In this paper, we investigate both ordinary and strong equivalence in CAFs. Thereby, we take the fact into account that one might either be interested in the actual arguments or their claims only. The former point of view naturally yields an extension of strong equivalence for AFs to the claim-based setting while the latter gives rise to a novel equivalence notion which is genuine for CAFs. We tailor, examine and compare these notions and obtain a comprehensive study of this matter for CAFs. We conclude by investigating the computational complexity of naturally arising decision problems.

Compilation of Aggregates in ASP Systems

Answer Set Programming (ASP) is a well-known declarative AI formalism for knowledge representation and reasoning. State-of-the-art ASP implementations employ the ground&solve approach, and they were successfully applied to industrial and academic problems. Nonetheless there are classes of ASP programs whose evaluation is not efficient (sometimes not feasible) due to the combinatorial blow-up of the program produced by the grounding step. Recent researches suggest that compilation-based techniques can mitigate the grounding bottleneck problem. However, no compilation-based technique has been developed for ASP programs that contain aggregates, which are one of the most relevant and commonly-employed constructs of ASP. In this paper, we propose a compilation-based approach for ASP programs with aggregates. We implement it on top of a state-of-the-art ASP system, and evaluate the performance on publicly-available benchmarks. Experiments show our approach is effective on ground-intensive ASP programs.

An Occurrence Description Logic


 
 
 Description Logics (DLs) are a family of well-known terminological knowledge representation formalisms in modern semantics-based systems. This research focuses on analysing how our developed Occurrence Logic (OccL) can conceptually and logically support the development of a description logic. OccL is integrated into the alternative theory of natural language syntax in Deviational Syntactic Structures under the label ‘EFA(X)3’ (or the third version of Epi-Formal Analysis in Syntax, EFA(X), which is a radical linguistic theory). From the logical point of view, OccL is a formal logic that mainly deals with the occurrences of symbols as well as with their priorities within linguistic descriptions, i.e. natural language syntax, semantics and phonology. In this article—based on our OccL-based definitions of the concepts of strong implication and occurrence value as well as of the logical concept Identical Occurrence Constructor (IDOC) that is the most fundamental logical concept in our formalism—we will model Occurrence Description Logic (ODL). Accordingly, we will formally-logically analyse ‘occurrence(s) of symbol(s)’ within descriptions of the world in ODL. In addition, we will analyse and assess the logical concepts of occurrence and occurrence priority in ODL. This research can make a strong logical background for our future research in the development of a Modal Occurrence Description Logic.
 
 

How knowledge becomes a technical object: epistemic practices 
 in information technology

The article analyzes the practices of production and improving the efficiency of technical knowledge. The field of information technologies was chosen as the base of research. A distinctive feature of the modern stage of development of technology is that knowledge itself becomes a technical object. The requirements of efficiency, high performance, security are beginning to apply to knowledge itself. The authors concept of epistemic practices is used as a methodological basis for the present study. Epistemic practices are defined as methods and techniques for working with technical objects, the result of which is to increase the efficiency of technical knowledge implemented into these objects. The high level of competition and the direct dependence of the profits of IT companies on the efficiency of knowledge production makes the information technology industry a kind of testing ground for the production of epistemic practices. It has been proved that epistemic practices in the field of information technology are the forms of knowledge representation in information systems, the selection of data visualization methods, the improvement of multifactorial mathematical models and the determination of the hardware load configuration. It is concluded that the production of epistemic practices is a condition for the competitiveness of IT companies operating in the sphere of high technologies.

ОНТОЛОГИЧЕСКИЙ ПОДХОД В СОЗДАНИИ РОБОТОТЕХНИЧЕСКИХ КОМПЛЕКСОВ С ПОВЫШЕННОЙ СТЕПЕНЬЮ АВТОНОМНОСТИ

The aspects necessary for the implementation of robotic complexes with an increased degree ofautonomy (RC with IDA) in practical work are considered. The distinctive features of such complexes,the needs of the corresponding intelligent information control systems (IIСS) are indicated. The requirementof situational awareness is highlighted and, as a consequence, the need for a diverse systemof knowledge representation, means of perception of the external environment and comparison of operationalinformation with models and a priori information about this environment. In addition, it is pointedout the need to automate the processes of creating RC with IDA, accessibility, and simplification of theiruse. In order to answer these questions, the paper proposes to use the concept and mechanisms of ontologiesin relation to autonomous robotics. Examples of existing solutions in this area are given. In robotics,ontologies are used to define and conceptualize knowledge accepted by the community, using aformal description that is machine-readable, shared, and contains the flexibility to justify this knowledgein order to derive additional information. Ontologies are of considerable interest for multi-agent systemsfor organizing interaction between agents and with other systems in heterogeneous environments,the possibility of reuse and support for the development of new RCs. The author describes the constructionof an ontology proposed by the author in such an applied field as information support for targetedmovements of autonomous ground vehicles based on technical vision systems. All consideration is conductedin the configuration space of the information and control systems of the RC with IDA. This spaceallows you to aggregate a large number of different technologies used in the construction of RC. Theembodiment of a particular system in this space corresponds to the "assembly point". The coordinationof the forms of knowledge representation in the IICS is ensured by the consistent consideration of planesin this space. As a connecting link – a means for automated translation of descriptions of descriptiveontologies into descriptions of functional, machine-readable ontologies, the use of the language of information-motor actions and interpretive navigation commands is proposed. In conclusion, the shorttermprospects for the development of the described approach are considered, and wishes are expressedto the domestic community of roboticists.

A Method for Expanding Predicates and Rules in Automated Geometry Reasoning System

Predicates and rules are usually enclosed as built-in functions in automated geometry reasoning systems, meaning users cannot add any predicate or rule, thus resulting in a limited reasoning capability of the systems. A method for expanding predicates and rules in automated geometry reasoning systems is, thus, proposed. Specifically, predicate and rule descriptions are transformed to knowledge trees and forests based on formal representations of geometric knowledge, and executable codes are dynamically and automatically generated by using “code templates”. Thus, a transformation from controlled natural language descriptions to mechanization algorithms is completed, and finally, the dynamic expansion of predicates and rules in the reasoning system is achieved. Moreover, the method has been implemented in an automated geometry reasoning system for Chinese college entrance examination questions, and the practicality and effectiveness of the method were tested. In conclusion, the enclosed setting, which is a shortcoming of traditional reasoning systems, is avoided, the user-defined dynamic expansion of predicates and rules is realized, the application scope of the reasoning system is extended, and the reasoning capability is improved.

Developing an Intuitive Graph Representation of Knowledge for Nonpharmacological Treatment of Psychotic Symptoms in Dementia.

Applying person-centered, nonpharmacological interventions to manage psychotic symptoms of dementia is promoted for health care professionals, particularly gerontological nurses, who are responsible for care of older adults in nursing homes. A knowledge graph is a graph consisting of a set of concepts that are linked together by their interrelationship and has been widely used as a formal representation of domain knowledge in health. However, there is lack of a knowledge graph for nonpharmacological treatment of psychotic symptoms in dementia. Therefore, we developed a comprehensive, human- and machine-understandable knowledge graph for this domain, named Dementia-Related Psychotic Symptom Nonpharmacological Treatment Ontology (DRPSNPTO). This graph was built by adopting the established NeOn methodology, a knowledge graph engineering method, to meet the quality standards for biomedical knowledge graphs. This intuitive graph representation of the domain knowledge sets a new direction for visualizing and computerizing gerontological knowledge to facilitate human comprehension and build intelligent aged care information systems. [Journal of Gerontological Nursing, 48(4), 49-55.].

Strong Equivalence and Program Structure in Arguing Essential Equivalence between Logic Programs

AbstractAnswer set programming is a prominent declarative programming paradigm used in formulating combinatorial search problems and implementing different knowledge representation formalisms. Frequently, several related and yet substantially different answer set programs exist for a given problem. Sometimes these encodings may display significantly different performance. Uncovering precise formal links between these programs is often important and yet far from trivial. This paper presents formal results carefully relating a number of interesting program rewritings. It also provides the proof of correctness of system projector concerned with automatic program rewritings for the sake of efficiency.

Multi-level Image Classification Using Fuzzy Petri Net

For a multi-level image classification, a knowledge representation scheme based on Fuzzy Petri Net with fuzzy inference algorithms is used. A simple graphical Petri net notation and a welldefined semantics displaying the process of reasoning through inference trees are used for visualization of the knowledge base and explanations of derived conclusion. Used knowledge representation formalism has the ability to show a probability of concepts and relations. The procedures of image multi-level classification using fuzzy recognition and inheritance algorithms on a knowledge representation scheme, as well as experimental results of image semantic interpretation, are presented.

The third AI summer: AAAI Robert S. Engelmore Memorial Lecture

The third AI summer: AAAI Robert S. Engelmore Memorial Lecture

An abstract, logical approach to characterizing strong equivalence in non-monotonic knowledge representation formalisms

Two knowledge bases are strongly equivalent if and only if they are mutually interchangeable in arbitrary contexts. This notion is of high interest for any logical formalism, since it allows to locally replace parts of a given theory without changing its meaning. In contrast to classical logic, where strong equivalence coincides with standard equivalence (having the same models), it is possible to find ordinary but not strongly equivalent objects for any nonmonotonic formalism available in the literature. Consequently, much effort has been devoted to characterizing strong equivalence for knowledge representation formalisms such as logic programs under the stable model semantics, Reiter's default logic, or Dung's argumentation frameworks. For example, strong equivalence for logic programs under stable models can be characterized by so-called HT-models. More precisely, two logic programs are strongly equivalent if and only if they are standard equivalent in the logic of here and there. This means that the logic of here and there can be seen as a characterizing formalism for logic programs under stable model semantics.The aim of this article is to study whether the existence of such characterization logics can be guaranteed for any logic. One main result is that every knowledge representation formalism that allows for a notion of strong equivalence on its finite knowledge bases also possesses a canonical characterizing formalism. In particular, we argue that those characterizing formalisms can be seen as classical, monotonic logics. Moreover, we will not only show the existence of characterizing formalism, but even that the model theory of any characterizing logic is uniquely determined (up to isomorphism).

Round Trip: An Automated Pipeline for Experimental Design, Execution, and Analysis.

Synthetic biology is a complex discipline that involves creating detailed, purpose-built designs from genetic parts. This process is often phrased as a Design-Build-Test-Learn loop, where iterative design improvements can be made, implemented, measured, and analyzed. Automation can potentially improve both the end-to-end duration of the process and the utility of data produced by the process. One of the most important considerations for the development of effective automation and quality data is a rigorous description of implicit knowledge encoded as a formal knowledge representation. The development of knowledge representation for the process poses a number of challenges, including developing effective human-machine interfaces, protecting against and repairing user error, providing flexibility for terminological mismatches, and supporting extensibility to new experimental types. We address these challenges with the DARPA SD2 Round Trip software architecture. The Round Trip is an open architecture that automates many of the key steps in the Test and Learn phases of a Design-Build-Test-Learn loop for high-throughput laboratory science. The primary contribution of the Round Trip is to assist with and otherwise automate metadata creation, curation, standardization, and linkage with experimental data. The Round Trip's focus on metadata supports fast, automated, and replicable analysis of experiments as well as experimental situational awareness and experimental interpretability. We highlight the major software components and data representations that enable the Round Trip to speed up the design and analysis of experiments by 2 orders of magnitude over prior ad hoc methods. These contributions support a number of experimental protocols and experimental types, demonstrating the Round Trip's breadth and extensibility. We describe both an illustrative use case using the Round Trip for an on-the-loop experimental campaign and overall contributions to reducing experimental analysis time and increasing data product volume in the SD2 program.

Context-Aware Explainable Recommendation Based on Domain Knowledge Graph

With the rapid growth of internet data, knowledge graphs (KGs) are considered as efficient form of knowledge representation that captures the semantics of web objects. In recent years, reasoning over KG for various artificial intelligence tasks have received a great deal of research interest. Providing recommendations based on users’ natural language queries is an equally difficult undertaking. In this paper, we propose a novel, context-aware recommender system, based on domain KG, to respond to user-defined natural queries. The proposed recommender system consists of three stages. First, we generate incomplete triples from user queries, which are then segmented using logical conjunction (∧) and disjunction (∨) operations. Then, we generate candidates by utilizing a KGE-based framework (Query2Box) for reasoning over segmented logical triples, with ∧, ∨, and ∃ operators; finally, the generated candidates are re-ranked using neural collaborative filtering (NCF) model by exploiting contextual (auxiliary) information from GraphSAGE embedding. Our approach demonstrates to be simple, yet efficient, at providing explainable recommendations on user’s queries, while leveraging user-item contextual information. Furthermore, our framework has shown to be capable of handling logical complex queries by transforming them into a disjunctive normal form (DNF) of simple queries. In this work, we focus on the restaurant domain as an application domain and use the Yelp dataset to evaluate the system. Experiments demonstrate that the proposed recommender system generalizes well on candidate generation from logical queries and effectively re-ranks those candidates, compared to the matrix factorization model.

Financial fraud detection using the related-party transaction knowledge graph

Financial fraud detection has gained constant attention from researchers, practitioners, and regulators. Because of its concealment and ease of manipulation, related-party transactions (RPTs) among firms have become a usual way to implement financial fraud. However, the traditional quantitative analysis methods regard each firm as an independent individual, failing to mine the intricate relation and transactions among related parties. A knowledge graph can mine valuable hidden knowledge from large-scale associated data as a new form of knowledge representation. Therefore, in this paper, the RPT knowledge graph is utilized to detect financial fraud, where the feature of the transaction’s scale and category is obtained. The experiment on the Chinese listed companies from 2000 to 2019 shows that these features enhance financial fraud detection performance, suggesting that type, size, and frequency of RPTs may imply fraud. More details, the feature importance indicates that regulators should pay more attention to the loan-based RPTs and the total number of RPTs.

Онтология иерархического проектирования интеллектуальных систем

A system of concepts used in the technology of designing and application of intelligent systems formalized models is presented. The technology is based on concepts that reflect the commonality of theoretical and empirical ideas about the structural and functional characteristics of knowledge formed in different areas involved in the study of thought processes and memory organization structures. Concept images are implemented as formalized descriptions of the basic elements of intelligent systems, divided into three separate groups: knowledge representation formalisms, multidimensional knowledge and memory structures, and cybernetic elements of knowledge and memory structures control. The transformation of abstract concepts from the general mathematical model of an intelligent system into descriptions of the entities of applied models of such systems is carried out using homomorphic extension operations. These operations are based on symbolic transformations that specify the content of general mathematical expressions. The application of unified classes of basic concepts of intelligent systems elements and their description formats provides flexibility in the development of models developed out the initial abstract model. The concepts of formalized models of intelligent systems constitute a fragment of the ontology of design management of such systems, used to model design processes.

Methodology for the design of didactic tools for the future transport engineers’ competencies formation on the ontology basis

The change in the essence of engineers’ functional activity activates the problem of creating new didactic tools adequate to the conditions of digital transformation of production and university education. The authors consider the ontological-semantic approach based on the use of ontological models and semantic technologies, as well as the principles of interoperability and interdisciplinarity corresponding to this approach as the methodological basis for the development of such tools. The feasibility of developing didactic tools on the ontology basis was determined using the questionnaire survey method, which was conducted among future railway transport engineers from 2016 to 2021. The results of the data analysis indicated a tendency for respondents to use compression tools for better assimilation of new learning material. Among such means students indicated not only tables, drawings and diagrams, but also a fundamentally new form of educational knowledge presentation - ontology. The conclusions made on the results of the questionnaire served as a basis for the development of didactic tools for the formation of communicative competence of future railway transport engineers. The novelty of this educational solution consisted in the complex use of textual, hypertext and ontological forms of branch knowledge representation, including the foreign language studied by students.

Applications of artificial intelligence systems

Nowadays artificial intelligence is a rapidly developing technology that encompasses the development of intelligent algorithms and machines capable of learning. Therefore, it is relevant and timely to examine the topic. These artificial intelligence algorithms and machines have the ability to perform tasks that traditionally relied on human intelligence in the past. This study provides an in-depth exploration of artificial intelligence systems and their key components. It examines various aspects of artificial intelligence systems, including natural language processing, machine learning, detection and pattern recognition, and knowledge representation and other form of artificial intelligence systems. Natural language processing enables machines to understand and generate human language, while machine learning empowers systems to learn from data and improve their performance over time. Detection and pattern recognition allow artificial intelligence systems to interpret and understand complex sensory inputs, while knowledge representation enables the storage and utilization of information. Furthermore, other form of artificial intelligence systems will be also discussed. This study sheds light on the fundamental elements of artificial intelligence systems, paving the way for their practical applications and advancements.

Application of selected heuristics in associative classification task

Decision and association rules are known and popular forms of knowledge representation. They are used in many areas of data mining and have different applications. One of them is associative classification which finds association rules that have only class label in the consequent part and satisfies the minimum support and confidence thresholds. The aim of this paper is to merge set of association rules induced by Apriori algorithm with the set of decision rules induced by selected heuristics, forming a combined classifier. Experimental results concern parameters of induced sets of rules as number of unique rules, their length and support, and classification accuracy involving two voting strategies as decision list and standard voting. It was shown, that for considered parameters of Apriori algorithm, the combined classifier allows to obtain improved classification accuracy compared to the classifier based only on association rules. Therefore selected heuristic for induction of decision rules can be applied in associative classification tasks.