Logic-based Representation Research Articles

A Hardware Approach For Accelerating Inductive Learning In Description Logic

The employment of Machine Learning (ML) techniques in embedded systems has seen constant growth in recent years, especially for black-box ML techniques (such as Artificial Neural Networks (ANNs)). However, despite the successful employment of ML techniques in embedded environments, their performance potential is constrained by the limited computing resources of their embedded computers. Several hardware-based approaches were developed (e.g., using FPGAs and ASICs) to address the constraints of limited computing resources. The scope of this work focuses on improving the performance for Inductive Logic Programming (ILP) on embedded environments. ILP is a powerful logic-based ML technique that uses logic programming to construct human-interpretable ML models, where those logic-based ML models are capable of describing complex and multi-relational concepts. In this work, we present a hardware-based approach that accelerates the hypothesis evaluation task for ILPs in embedded environments that use Description Logic (DL) languages as their logic-based representation. In particular, we target the \(\mathcal {ALCQ}^{\mathcal {(D)}}\) language. According to experimental results (through an FPGA implementation), our presented approach has achieved speedups up to 48.7-fold for a disjunction of 32 concepts on 100 M individuals, where the baseline performance is the sequential CPU performance of the Raspberry Pi 4. For role and concrete role restrictions, the FPGA implementation achieved speedups up to 2.4-fold (for MIN cardinality role restriction on 1M role assertions); all FPGA implemented role and concrete role restrictions have achieved similar speedups. In the worst-case scenario, the FPGA implementation achieved either a similar or slightly better performance than the baseline (for all DL operations); the worst-case scenario resulted from using small datasets such as: using conjunction and disjunction on < 100 individuals, and using role and concrete (float/string) role restrictions on < 100,000 assertions.

On the Expressive Power of Negated Conditions and Negative Authorizations in Access Control Models

Access control policies specify which access requests should be allowed or denied in a system. Many access control policy models have used the concept of “negation” as part of their policy language, to enable fine-grained specification of authorizations. We identify two forms of this concept in the literature, namely, negated conditions and negative authorizations (deny rules). We argue that the choice of supporting negated conditions or negative authorizations can affect the expressive power of a policy model. Understanding their differences is crucial for designing an appropriate policy model for an intended application. However, no prior work has concretely analyzed them.In this work, we formally analyze the expressive power of negated conditions and negative authorizations. We formulate two abstract policy models that support negated conditions and negative authorizations (including consideration of different meta-policies). Then, using a logic-based representation of policies, we prove the relative expressive power of those models in the context of a formal access control expressiveness analysis framework. The main result of our analysis is that models which support negated conditions are more expressive than models that support negative authorizations. That is, using negated conditions, we can represent all policies that can be expressed using negative authorizations. However, the converse is not true, i.e., negative authorizations cannot fully represent policies supporting negated conditions.

Depth-bounded Belief functions

This paper introduces and investigates Depth-bounded Belief functions, a logic-based representation of quantified uncertainty. Depth-bounded Belief functions are based on the framework of Depth-bounded Boolean logics [4], which provide a hierarchy of approximations to classical logic. Similarly, Depth-bounded Belief functions give rise to a hierarchy of increasingly tighter lower and upper bounds over classical measures of uncertainty. This has the rather welcome consequence that “higher logical abilities” lead to sharper uncertainty quantification. In particular, our main results identify the conditions under which Dempster-Shafer Belief functions and probability functions can be represented as a limit of a suitable sequence of Depth-bounded Belief functions.

A Survey and Classification of Arabic Logic-Based Meaning Representations (ALMR)

A Survey and Classification of Arabic Logic-Based Meaning Representations (ALMR)

Logical Formalizations of Commonsense Reasoning: A Survey

Commonsense reasoning is in principle a central problem in artificial intelligence, but it is a very difficult one. One approach that has been pursued since the earliest days of the field has been to encode commonsense knowledge as statements in a logic-based representation language and to implement commonsense reasoning as some form of logical inference. This paper surveys the use of logic-based representations of commonsense knowledge in artificial intelligence research.

A logic-based representation and tree-based visualization method for building regulatory requirements

BackgroundMany research and development efforts have been made for automated compliance checking of building designs with regulatory requirements, but there is a lack of a non-proprietary and user-understandable representation of building regulations to support automated compliance checking in the construction domain that is experimentally tested for understandability and reading speed.MethodsThis research investigates a logic-based representation and tree-based visualization method for building regulatory requirements. The logic-based representation is based on classic logic programming language and can directly support automated compliance checking. The tree-based visualization is expected to improve the understandability and reading speed of the logic-based representation. Therefore, this method attempts to add to the limited research in non-proprietary and user-understandable representation of building regulations that is experimentally tested for understandability and reading speed. To test the understandability and reading speed of regulatory requirements using this representation and visualization method, a survey was conducted to compare different representations, namely, text, logic-based, and tree-based.ResultsStatistical analysis of the survey results shows that the proposed tree-based visualization method can significantly improve the understandability and reading speed of the logic-based regulatory requirement representation and this visualization method is at a comparable status with the original text representation of regulatory requirements in terms of understandability and reading speed.Conclusions(1) The investigated logic-based representation and tree-based visualization method for regulatory requirements serves as one potential non-proprietary and user-understandable representation of building regulations; (2) this research shows that computable representations of regulatory requirements can achieve understandability and reading speed that are comparable to the original text representation through tree-based visualization; and (3) this research reveals that the tree-based visualization of regulatory requirements improves the understandability and reading speed of regulatory requirements when such use is compared to the computable logic-based representation.

Representing Meaning with a Combination of Logical and Distributional Models

NLP tasks differ in the semantic information they require, and at this time no single semantic representation fulfills all requirements. Logic-based representations characterize sentence structure, but do not capture the graded aspect of meaning. Distributional models give graded similarity ratings for words and phrases, but do not capture sentence structure in the same detail as logic-based approaches. It has therefore been argued that the two are complementary.We adopt a hybrid approach that combines logical and distributional semantics using probabilistic logic, specifically Markov Logic Networks. In this article, we focus on the three components of a practical system:11) Logical representation focuses on representing the input problems in probabilistic logic; 2) knowledge base construction creates weighted inference rules by integrating distributional information with other sources; and 3) probabilistic inference involves solving the resulting MLN inference problems efficiently. To evaluate our approach, we use the task of textual entailment, which can utilize the strengths of both logic-based and distributional representations. In particular we focus on the SICK data set, where we achieve state-of-the-art results. We also release a lexical entailment data set of 10,213 rules extracted from the SICK data set, which is a valuable resource for evaluating lexical entailment systems.2

Quadratic and nonlinear programming problems solving and analysis in fully fuzzy environment

This paper presents a comprehensive methodology for solving and analyzing quadratic and nonlinear programming problems in fully fuzzy environment. The solution approach is based on the Arithmetic Fuzzy Logic-based Representations, previously founded on normalized fuzzy matrices. The suggested approach is generalized for the fully fuzzy case of the general forms of quadratic and nonlinear modeling and optimization problems of both the unconstrained and constrained fuzzy optimization problems. The constrained problems are extended by incorporating the suggested fuzzy logic-based representations assuming complete fuzziness of all the optimization formulation parameters. The robustness of the optimal fuzzy solutions is then analyzed using the recently newly developed system consolidity index. Four examples of quadratic and nonlinear programming optimization problems are investigated to illustrate the efficacy of the developed formulations. Moreover, consolidity patterns for the illustrative examples are sketched to show the ability of the optimal solution to withstand any system and input parameters changes effects. It is demonstrated that the geometric analysis of the consolidity charts of each region can be carried out based on specifying the type of consolidity region shape (such as elliptical or circular), slope or angle in degrees of the centerline of the geometric, the location of the centroid of the geometric shape, area of the geometric shape, lengths of principals diagonals of the shape, and the diversity ratio of consolidity points. The overall results demonstrate the consistency and effectiveness of the developed approach for incorporation and implementation for fuzzy quadratic and nonlinear optimization problems. Finally, it is concluded that the presented concept could provide a comprehensive methodology for various quadratic and nonlinear systems’ modeling and optimization in fully fuzzy environments.

Real-Time Action Model Learning with Online Algorithm 3SG

An action model, as a logic-based representation of action’s effects and preconditions, constitutes an essential requirement for planning and intelligent behavior. Writing these models by hand, especially in complex domains, is often a time-consuming and error-prone task. An alternative approach is to let the agents learn action models from their own observations. We introduce a novel action learning algorithm called 3SG (Simultaneous Specification, Simplification, and Generalization), analyze and prove some of its properties, and present the first experimental results (using real-world robots of the SyRoTek platform and simulated agents in action computer game Unreal Tournament 2004). Unlike the majority of available alternatives, 3SG produces probabilistic action models with conditional effects and deals with action failures, sensoric noise, and incomplete observations. The main difference, however, is that 3SG is an online algorithm, which means it is rather fast (polynomial in the size of the input) but potentially less precise.

A Comparison of Paraconsistent Description Logics

Description logics (DLs) are a family of logic-based knowledge representation formalisms with a number of computer science applications. DLs are especially well-known to be valuable for obtaining logical foundations of web ontology languages (e.g., W3C’s ontology language OWL). Paraconsistent (or inconsistency-tolerant) description logics (PDLs) have been studied to cope with inconsistencies which may frequently occur in an open world. In this paper, a comparison and survey of PDLs is presented. It is shown that four existing paraconsistent semantics (i.e., four-valued semantics, quasi-classical semantics, single-interpretation semantics and dual-interpretation semantics) for PDLs are essentially the same semantics. To show this, two generalized and extended new semantics are introduced, and an equivalence between them is proved.

Logic-based topological representation of vague moving regions: computational models for well-behaved GIS solutions

This study proposes a solution for the topological representation and interpretation of moving regions under vagueness in a geographic information system (GIS). The problem is multifaceted, and different aspects will be discussed. We investigate the impact of several types of fuzzy logic on results and address which is more convenient for the region connection calculus under fuzziness (FRCC). By recognising that the main and basic relation to evaluate topological relations is the connection relation, we adopt a qualitative strategy based on fuzzy inference and resemblance relations. We also define topological differences and introduce fuzzy transition relations (FTRs) to distinguish how a transition from one topological situation to another occurs for a pair of objects during a time period. As pure topological relations are not sufficient for a complete qualitative perception, an orientation-based method (OBM) will be proposed. Using this method, we can interpret, for example, a region that partially overlaps another region mostly on its northern part. Most importantly, some step-by-step algorithms and constructs are proposed and evaluated such that the model can apply to regions stored in vector data models.

Logic-based event recognition

AbstractToday's organizations require techniques for automated transformation of their large data volumes into operational knowledge. This requirement may be addressed by using event recognition systems that detect events/activities of special significance within an organization, given streams of ‘low-level’ information that is very difficult to be utilized by humans. Consider, for example, the recognition of attacks on nodes of a computer network given the Transmission Control Protocol/Internet Protocol messages, the recognition of suspicious trader behaviour given the transactions in a financial market and the recognition of whale songs given a symbolic representation of whale sounds. Various event recognition systems have been proposed in the literature. Recognition systems with a logic-based representation of event structures, in particular, have been attracting considerable attention, because, among others, they exhibit a formal, declarative semantics, they have proven to be efficient and scalable and they are supported by machine learning tools automating the construction and refinement of event structures. In this paper, we review representative approaches of logic-based event recognition and discuss open research issues of this field. We illustrate the reviewed approaches with the use of a real-world case study: event recognition for city transport management.

Semantic Applications Enabling Reasoning in Product Assembly Ontologies—Moving Past Modeling

In this paper, the authors postulate an approach to introduce reasoning capabilities into ontologies in the product assembly domain in order to truly exploit these logic-based and formal representations for product data. A model containing semantic applications with multiple reasoning units in logic reasoning layer of a layered semantic application architecture is proposed. Retrieval specifications and inference rules in SWRL/SQWRL are defined in these reasoning units. Besides, local-based user interfaces have been developed to allow product engineers to submit reasoning tasks and view querying retrievals or inference results. The approach and semantic applications are illustrated with two case studies in the product assembly domain. It is demonstrated that this approach not only enables existing product data to be queried and retrieved but also enables new product data, which is not explicitly expressed in the ontology models, to be derived. It is concluded that the reasoning mechanism exploits and extends the semantic representation made possible through the ontology and thus holds promise for improved knowledge discovery and understanding.

Tightly integrated probabilistic description logic programs for representing ontology mappings

Creating mappings between ontologies is a common way of approaching the semantic heterogeneity problem on the Semantic Web. To fit into the landscape of Semantic Web languages, a suitable, logic-based representation formalism for mappings is needed. We argue that such a formalism has to be able to deal with uncertainty and inconsistencies in automatically created mappings. We analyze the requirements for such a formalism, and we propose a novel approach to probabilistic description logic programs as such a formalism, which tightly combines normal logic programs under the well-founded semantics with both tractable ontology languages and Bayesian probabilities. We define the language, and we show that it can be used to resolve inconsistencies and merge mappings from different matchers based on the level of confidence assigned to different rules. Furthermore, we explore the semantic and computational aspects of probabilistic description logic programs under the well-founded semantics. In particular, we show that the well-founded semantics approximates the answer set semantics. We also describe algorithms for consistency checking and tight query processing, and we analyze the data and general complexity of these two central computational problems. As a crucial property, the novel tightly integrated probabilistic description logic programs under the well-founded semantics allow for tractable consistency checking and for tractable tight query processing in the data complexity, and they even have a first-order rewritable (and thus LogSpace data complexity) special case, which is especially interesting for representing ontology mappings.

Development of new Consolidity Theory for systems’ analysis and design in fully fuzzy environment

This paper establishes the foundation of new systems’ Consolidity Theory using the Arithmetic Fuzzy Logic-Based Representation approach for investigating the internal behavior of systems operating in fully fuzzy environment. Consolidated systems are defined as being stable at the original state, but due to fuzzy variations in their inputs or parameters tend to react accordingly in a manner leading to maintaining their consolidity and strength, or vice versa. Under the new theory, systems are classified into consolidated, neutrally consolidated or unconsolidated type based on their output fuzziness reaction to combined input and parameters fuzziness action. The systems’ Consolidity Theory is demonstrated by several examples of mathematical functions of different dimensionalities, control theory and Predator-Prey populations’ dynamics. The suggested Consolidity Theory is illustrated to be an effective tool for revealing the inner property of systems and predicting their hidden behavior when operating in fully fuzzy environment. Monitoring and control of systems’ consolidity through forward and backward fuzziness tracking are suggested during systems’ operation, for avoiding their drifting to possible unwanted unconsolidated domains. It is shown that the analysis will lead at the end to determining the system’s consolidity index that could be regarded as a general basic internal property of the system. Such systems’ consolidity concept can also be defined far from fuzzy logic, and is applicable to the analysis and design of various types of linear, nonlinear, multivariable, dynamic, etc., systems in real life in the fields of basic sciences, evolutionary systems, engineering, biology, medicine, economics, finance, political and management sciences, social sciences, humanities, and education.

Chemical ontologies: what are they, what are they for and what are the challenges

Ontologies encode human knowledge in computationally accessible forms. They are designed to narrow the gap between the knowledge of human experts and the functionality available in computer systems, by expressing expert knowledge in a manner computers can manipulate and reason over. With the ever-growing deluge of data in modern scientific domains, researchers need intelligent tools able to filter out irrelevant and automatically organise relevant information into meaningful categories. The Chemoinformatics and Metabolism team at the EBI is developing chemical ontologies for structure-based chemical classification, role or bioactivity-based chemical classification, and chemical information entities such as descriptors and algorithms. Our ontologies provide collections of names and synonyms which are useful for text mining, stable identifiers which are essential to semantic integration of data, and a semantically rich encoding of many aspects of the chemical domain. But for such ontologies to be maximally useful for diverse users and interoperable with other ontologies in the scientific domain, similar-sounding things have to be disentangled in our language and our ontology. Recent work addresses the distinguishing of structures from chemicals [1], and of bioactivity from drug uses [2]. Ontologies are backed by logical formalisms such as the Web Ontology Language, OWL. One of the challenges of chemical ontology is representing complex chemical structures in the underlying formalism. Cyclic structures prove particularly challenging for logicbased representation. Recent research in our group investigated the inclusion of chemical graphs in OWL [3]. Integrating chemoinformatics tools with chemical ontology is the subject of ongoing research.

Reasoning with bio-ontologies: using relational closure rules to enable practical querying

Ontologies have become indispensable in the Life Sciences for managing large amounts of knowledge. The use of logics in ontologies ranges from sound modelling to practical querying of that knowledge, thus adding a considerable value. We conceive reasoning on bio-ontologies as a semi-automated process in three steps: (i) defining a logic-based representation language; (ii) building a consistent ontology using that language; and (iii) exploiting the ontology through querying. Here, we report on how we have implemented this approach to reasoning on the OBO Foundry ontologies within BioGateway, a biological Resource Description Framework knowledge base. By separating the three steps in a manual curation effort on Metarel, a vocabulary that specifies relation semantics, we were able to apply reasoning on a large scale. Starting from an initial 401 million triples, we inferred about 158 million knowledge statements that allow for a myriad of prospective queries, potentially leading to new hypotheses about for instance gene products, processes, interactions or diseases. SPARUL code, a query end point and curated relation types in OBO Format, RDF and OWL 2 DL are freely available at http://www.semantic-systems-biology.org/metarel.

Analogy between Arithmetic Fuzzy Logic-based Representation Approach and Conventional Fuzzy Theory for Modeling and Optimization in Fully Fuzzy Environment

This paper presents further development of the arithmetic fuzzy logic-based representation. The concept was originally proposed by Gabr and Dorrah for linear and nonlinear system using the notion of the normalized fuzzy matrices. The investigation is based on the dual cell representation, expressed by replacing each parameter with a pair of parentheses, the first is the actual value and the second is corresponding fuzzy level. The analogy of the proposed Arithmetic Fuzzy Logic-based Representation technique with the Conventional Fuzzy Theory are derived for various cases of operations ofaddition, subtraction, multiplication and division. It is shown that the suggested approach is identical to that of the conventional fuzzy theory for addition operations and gives average weighted fuzziness interval results of the subtraction operations. Moreover, it yields similar results of multiplications and divisions operations after ignoring the second order relative variations terms. Finally, the suggested approach is demonstrated to offer additional advantages of linearity, reversibility, simplicity, and applicability.

DSI

Considerable research has been done on the content-based multimedia delivery and access in distributed data repositories. As noted in the literature, there is always a trade-off between multimedia quality and access speed. In addition, the overall performance is greatly determined by the distribution of the multimedia data. In this article, an unsupervised multimedia semantic integration approach for a distributed infrastructure, the Distributed Semantic Indexing (DSI), is presented that addresses both the data quality and search performance. With the ability of summarizing content information and guiding data distribution, the proposed approach is distinguished by: (1) logic-based representation and concise abstraction of the semantic contents of multimedia data, which are further integrated to form a general overview of a multimedia data repository—content signature; (2) application of linguistic relationships to construct a hierarchical metadata based on the content signatures allowing imprecise queries; and (3) achieving the optimal performance in terms of search cost. The fundamental structure of the proposed model is presented. The proposed scheme has been simulated and the simulation results are analyzed and compared against several other approaches that have been advocated in the literature.

Possibilistic Information Fusion Using Maximal Coherent Subsets

When multiple sources provide information about the same unknown quantity, their fusion into a synthetic interpretable message is often a tricky problem, especially when sources are conflicting. In this paper, we propose to use possibility theory and the notion of maximal coherent subsets (MCSs), often used in logic-based representations, to build a fuzzy belief structure that will be instrumental both for extracting useful insight about various features of the information conveyed by the sources and for compressing this information into a unique possibility distribution. Extentions and properties of the basic fusion rule are also studied.