Knowledge Representation Paradigm Research Articles

GEnI: A framework for the generation of explanations and insights of knowledge graph embedding predictions

Knowledge Graphs (KGs) are among the most commonly used knowledge representation paradigms, being at the core of tasks such as question answering or recommendation systems. Knowledge Graph Completion (KGC) is one of the key tasks concerning KGs, where the goal is to extract new elements from the existing information. Different approaches have been proposed through the years to tackle this challenge. Among them, two analogous categories can be distinguished: rule-learning and Knowledge Graph Embeddings (KGE). Different methods have been subsequently proposed to unify both types under a single framework, such that the benefits of both proposals can be exploited. However, most of these methods consider using rule-learning models as a boosting agent for KGE models, but not as an explainability tool. This work presents GEnI11https://github.com/oeg-upm/GEnI, a framework capable of generating insights and explanations for KGE models. GEnI follows a three-phase sequential process, generating a feasible explanation for a given prediction. Possible outcomes are rules, correlations, and influence detection. Moreover, the output is expressed in natural language to further extend the explainability of the proposal. GEnI has been successfully evaluated under three criteria: coherence, the meaningfulness of the output, and reliability. Moreover, it can be used by both translational and bilinear KGE models, offering broad coverage. Furthermore, this work also presents an in-depth review of existing integrative approaches between rule-learning and embedding models, providing a comparative framework between them.

An efficient algorithm for reasoning over OWL EL ontologies with nominal schemas

Abstract Nominal schemas have been proposed as an extension to Description Logics (DL), the knowledge representation paradigm underlying the Web Ontology Language (OWL). They provide for a very tight integration of DL and rules. Nominal schemas can be understood as syntactic sugar on top of OWL. However, this naive perspective leads to inefficient reasoning procedures. In order to develop an efficient reasoning procedure for the language ${\mathcal {E}\mathcal {L}\mathcal {V}^{++}}$, which results from extending the OWL profile language OWL EL with nominal schemas, we propose a transformation from ${\mathcal {E}\mathcal {L}\mathcal {V}^{++}}$ ontologies into Datalog-like rule programs that can be used for satisfiability checking and assertion retrieval. The use of this transformation enables the use of powerful Datalog engines to solve reasoning tasks over ${\mathcal {E}\mathcal {L}\mathcal {V}^{++}}$ ontologies. We implement and then evaluate our approach on several real-world, data-intensive ontologies, and find that it can outperform state-of-the-art reasoners such as Konclude and ELK. As a lesser side result we also provide a self-contained description of a rule-based algorithm for ${\mathcal {E}\mathcal {L}^{++}}$, which does not require a normal form transformation.

Aggregate Semantics for Propositional Answer Set Programs

AbstractAnswer set programming (ASP) emerged in the late 1990s as a paradigm for knowledge representation and reasoning. The attractiveness of ASP builds on an expressive high-level modeling language along with the availability of powerful off-the-shelf solving systems. While the utility of incorporating aggregate expressions in the modeling language has been realized almost simultaneously with the inception of the first ASP solving systems, a general semantics of aggregates and its efficient implementation have been long-standing challenges. Aggregates have been proposed and widely used in database systems, and also in the deductive database language Datalog, which is one of the main precursors of ASP. The use of aggregates was, however, still restricted in Datalog (by either disallowing recursion or only allowing monotone aggregates), while several ways to integrate unrestricted aggregates evolved in the context of ASP. In this survey, we pick up at this point of development by presenting and comparing the main aggregate semantics that have been proposed for propositional ASP programs. We highlight crucial properties such as computational complexity and expressive power, and outline the capabilities and limitations of different approaches by illustrative examples.

Graph Based Answer Set Programming Solver Systems

Answer set programming (ASP) is a popular nonmonotonic-logic based paradigm for knowledge representation and solving combinatorial problems. Computing the answer set of an ASP program is NP-hard in general, and researchers have been investing significant effort to speed it up. The majority of current ASP solvers employ SAT solver-like technology to find these answer sets. As a result, justification for why a literal is in the answer set is hard to produce. There are dependency graph based approaches to find answer sets, but due to the representational limitations of dependency graphs, such approaches are limited. This paper proposes a novel dependency graph-based approach for finding answer sets in which conjunction of goals is explicitly represented as a node which allows arbitrary answer set programs to be uniformly represented. Our representation preserves causal relationships allowing for justification for each literal in the answer set to be elegantly found. In this paper, we explore two different approaches based on the graph representation: bottom-up and top-down. The bottom-up approach finds models by assigning truth values along with the topological order, while the top-down approach generates models starting from the constraints.

Advancing Lazy-Grounding ASP Solving Techniques – Restarts, Phase Saving, Heuristics, and More

AbstractAnswer-Set Programming (ASP) is a powerful and expressive knowledge representation paradigm with a significant number of applications in logic-based AI. The traditional ground-and-solve approach, however, requires ASP programs to be grounded upfront and thus suffers from the so-called grounding bottleneck (i.e., ASP programs easily exhaust all available memory and thus become unsolvable). As a remedy, lazy-grounding ASP solvers have been developed, but many state-of-the-art techniques for grounded ASP solving have not been available to them yet. In this work we present, for the first time, adaptions to the lazy-grounding setting for many important techniques, like restarts, phase saving, domain-independent heuristics, and learned-clause deletion. Furthermore, we investigate their effects and in general observe a large improvement in solving capabilities and also uncover negative effects in certain cases, indicating the need for portfolio solving as known from other solvers.

An Extended Intuitionistic Fuzzy Cognitive Map via Dempster-Shafer Theory

Fuzzy cognitive map has gradually emerged as a powerful paradigm for uncertain knowledge representation and a simulation mechanism that is applicable in dealing with complex artificial reasoning problems. To better model uncertain inference reasoning problems, we propose an extended intuitionistic fuzzy cognitive map via Dempster-Shafer theory. First of all, some new operations on IFSs are introduced from the perspective of Dempster-Shafer theory. Then, the extended intuitionistic fuzzy cognitive map is established via the proposed new operations. Next, we investigate the problem of modeling complex system from multiple decision makers using extended intuitionistic fuzzy cognitive maps and present a method to aggregate a number of maps. Particular emphases are put on defining the augmented connection matrices, determining the importance levels of different extended intuitionistic fuzzy cognitive maps and aggregating them. Finally, the performances of extended intuitionistic fuzzy cognitive maps have been validated through a number of simulations. The simulations indicate that the theory of extended intuitionistic fuzzy cognitive map not only provides much more choices to model complex system but also reduces the computational complexity by comparison with intuitionistic fuzzy cognitive map.

Reasoning with Probabilistic and Deterministic Graphical Models: Exact Algorithms, Second Edition

Graphical models (e.g., Bayesian and constraint networks, influence diagrams, and Markov decision processes) have become a central paradigm for knowledge representation and reasoning in both artificial intelligence and computer science in general. These models are used to perform many reasoning tasks, such as scheduling, planning and learning, diagnosis and prediction, design, hardware and software verification, and bioinformatics. These problems can be stated as the formal tasks of constraint satisfaction and satisfiability, combinatorial optimization, and probabilistic inference. It is well known that the tasks are computationally hard, but research during the past three decades has yielded a variety of principles and techniques that significantly advanced the state of the art. This book provides comprehensive coverage of the primary exact algorithms for reasoning with such models. The main feature exploited by the algorithms is the model's graph. We present inference-based, message-passing schemes (e.g., variable-elimination) and search-based, conditioning schemes (e.g., cycle-cutset conditioning and AND/OR search). Each class possesses distinguished characteristics and in particular has different time vs. space behavior. We emphasize the dependence of both schemes on few graph parameters such as the treewidth, cycle-cutset, and (the pseudo-tree) height. The new edition includes the notion of influence diagrams, which focus on sequential decision making under uncertainty. We believe the principles outlined in the book would serve well in moving forward to approximation and anytime-based schemes. The target audience of this book is researchers and students in the artificial intelligence and machine learning area, and beyond.

Discovering State‐Parameter Mappings in Subsurface Models Using Generative Adversarial Networks

AbstractA fundamental problem in geophysical modeling is related to the identification and approximation of causal structures among physical processes. However, resolving the bidirectional mappings between physical parameters and model state variables (i.e., solving the forward and inverse problems) is challenging, especially when parameter dimensionality is high. Deep learning has opened a new door toward knowledge representation and complex pattern identification. In particular, the recently introduced generative adversarial networks (GANs) hold strong promises in learning cross‐domain mappings for image translation. This study presents a state‐parameter identification GAN (SPID‐GAN) for simultaneously learning bidirectional mappings between a high‐dimensional parameter space and the corresponding model state space. SPID‐GAN is demonstrated using a series of representative problems from subsurface flow modeling. Results show that SPID‐GAN achieves satisfactory performance in identifying the bidirectional state‐parameter mappings, providing a new deep‐learning‐based, knowledge representation paradigm for a wide array of complex geophysical problems.

Testing Multi-Threaded Applications Using Answer Set Programming

We introduce a technique to formally represent and specify race conditions in multithreaded applications. Answer set programming (ASP) is a logic-based knowledge representation paradigm to formally express belief acquired through reasoning in an application domain. The transparent and expressiveness representation of problems along with powerful non-monotonic reasoning power enable ASP to abstractly represent and solve some certain classes of NP hard problems in polynomial times. We use ASP to formally express race conditions and thus represent potential data races often occurred in multithreaded applications with shared memory models. We then use ASP to generate all possible test inputs and thread interleaving, i.e. scheduling, whose executions would result in deterministically exposing thread interleaving failures. We evaluated the proposed technique with some moderate sized Java programs, and our experimental results confirm that the proposed technique can practically expose common data races in multithreaded programs with low false positive rates. We conjecture that, in addition to generating threads scheduling whose execution order leads to the exposition of data races, ASP has several other applications in constraint-based software testing research and can be utilized to express and solve similar test case generation problems where constraints play a key role in determining the complexity of searches.

A new paradigm for uncertain knowledge representation by Plausible Petri nets

This paper presents a new model for Petri nets (PNs) which combines PN principles with the foundations of information theory for uncertain knowledge representation. The resulting framework has been named Plausible Petri nets (PPNs). The main feature of PPNs resides in their efficiency to jointly consider the evolution of a discrete event system together with uncertain information about the system state using states of information. The paper overviews relevant concepts of information theory and uncertainty representation, and presents an algebraic method to formally consider the evolution of uncertain state variables within the PN dynamics. To illustrate some of the real-world challenges relating to uncertainty that can be handled using a PPN, an example of an expert system is provided, demonstrating how condition monitoring data and expert opinion can be modelled.

Privacy-respecting auctions and rewarding mechanisms in mobile crowd-sensing applications

Mobile Crowdsensing (MCS) has emerged as a new paradigm for data collection and knowledge representation, where people use their devices to interact with the environment and create a more accurate picture of their surroundings. In many MCS scenarios it is desirable to give micro-payments to contributors as an incentive for their participation. However, to further encourage participants to use the system, one important requirement is protection of user privacy. In this work we present a multi-attribute reverse auction mechanism as an efficient way to offer incentives to users by allowing them to determine their own price for the data they provide, but also as a way to motivate them to submit better quality data. Our auction protocol guarantees bidders' anonymity and suggests a new rewarding mechanism that enables winners to claim their reward without being linked to the data they contributed. We have analyzed our protocol and showed that it offers strong security and privacy guarantees in all phases of the auction process, from bidding to rewarding. Additionally, we have implemented the protocol using off-the-shelf cryptographic primitives; our experiments show that the protocol is scalable, it can be applied to a large class of auctions and remains efficient from both a computation and communication point of view so that it can be run to the users' mobile devices.

Low-Dose Lung CT Image Restoration Using Adaptive Prior Features From Full-Dose Training Database

The valuable structure features in full-dose computed tomography (FdCT) scans can be exploited as prior knowledge for low-dose CT (LdCT) imaging. However, lacking the capability to represent local characteristics of interested structures of the LdCT image adaptively may result in poor preservation of details/textures in LdCT image. This paper aims to explore a novel prior knowledge retrieval and representation paradigm, called adaptive prior features assisted restoration algorithm, for the purpose of better restoration of the low-dose lung CT images by capturing local features from FdCT scans adaptively. The innovation lies in the construction of an offline training database and the online patch-search scheme integrated with the principal components analysis (PCA). Specifically, the offline training database is composed of 3-D patch samples extracted from existing full-dose lung scans. For online patch-search, 3-D patches with structure similar to the noisy target patch are first selected from the database as the training samples. Then, PCA is applied on the training samples to retrieve their local prior principal features adaptively. By employing the principal features to decompose the noisy target patch and using an adaptive coefficient shrinkage technique for inverse transformation, the noise of the target patch can be efficiently removed and the detailed texture can be well preserved. The effectiveness of the proposed algorithm was validated by CT scans of patients with lung cancer. The results show that it can achieve a noticeable gain over some state-of-the-art methods in terms of noise suppression and details/textures preservation.

Ontology: A Case for Disease and Drug Knowledge Discovery

A number of medical conditions are still incurable today, some are even auto-immune. When people come down with disease conditions like lupus, Alzheimer, multiple sclerosis, schizophrenia, diabetes, cancer, asthma, Creutzfeldt-Jakob, AIDS and a host of others, they become incapacitated, lose major functions and most just wait until they die. These conditions are usually very cruel to those who suffer them. The lack of cure for these conditions is partly due to the fact that their causative agents are not clearly known or understood. One might be tempted to presume that with the completion of the Human Genome Project, solutions would have been derived for such disease conditions. It is only wise to think that such conditions have so far remained major challenges to medical researchers because they have multiple causes. Ontology, a widely accepted Knowledge Representation (KR) paradigm is therefore proposed as a KR technique to holistically attempt to address the gaps by first identifying all the causative elements, and then being able to proffer viable solutions to such conditions.

A general framework for intelligent recommender systems

In this paper, we propose a general framework for an intelligent recommender system that extends the concept of a knowledge-based recommender system. The intelligent recommender system exploits knowledge, learns, discovers new information, infers preferences and criticisms, among other things. For that, the framework of an intelligent recommender system is defined by the following components: knowledge representation paradigm, learning methods, and reasoning mechanisms. Additionally, it has five knowledge models about the different aspects that we can consider during a recommendation: users, items, domain, context and criticisms. The mix of the components exploits the knowledge, updates it and infers, among other things. In this work, we implement one intelligent recommender system based on this framework, using Fuzzy Cognitive Maps (FCMs). Next, we test the performance of the intelligent recommender system with specialized criteria linked to the utilization of the knowledge in order to test the versatility and performance of the framework.

Semantic Technologies and Big Data Analytics for Cyber Defence

The Governments, military forces and other organisations responsible for cybersecurity deal with vast amounts of data that has to be understood in order to lead to intelligent decision making. Due to the vast amounts of information pertinent to cybersecurity, automation is required for processing and decision making, specifically to present advance warning of possible threats. The ability to detect patterns in vast data sets, and being able to understanding the significance of detected patterns are essential in the cyber defence domain. Big data technologies supported by semantic technologies can improve cybersecurity, and thus cyber defence by providing support for the processing and understanding of the huge amounts of information in the cyber environment. The term big data analytics refers to advanced analytic techniques such as machine learning, predictive analysis, and other intelligent processing techniques applied to large data sets that contain different data types. The purpose is to detect patterns, correlations, trends and other useful information. Semantic technologies is a knowledge representation paradigm where the meaning of data is encoded separately from the data itself. The use of semantic technologies such as logic-based systems to support decision making is becoming increasingly popular. However, most automated systems are currently based on syntactic rules. These rules are generally not sophisticated enough to deal with the complexity of decisions required to be made. The incorporation of semantic information allows for increased understanding and sophistication in cyber defence systems. This paper argues that both big data analytics and semantic technologies are necessary to provide counter measures against cyber threats. An overview of the use of semantic technologies and big data technologies in cyber defence is provided, and important areas for future research in the combined domains are discussed.

An ontology-based decision support tool for optimizing domestic solar hot water system selection

In an effort to tackle climate change most countries utilize renewable energy sources. This is more pronounced in the building sector, which is currently one of the major consumers of energy, mostly in the form of heat. In order to further promote the use of domestic solar hot water systems in buildings, an ontology-based decision support tool has been developed and is presented in this paper. The proposed tool aids non-technical consumers to select a domestic solar hot water system tailored to their needs, containing up-to-date information on its components and interrelationships, installation costs etc., in the form of an ontology formulated in OWL (Web Ontology Language). The optimum system configurations are computed based on various specific parameters, such as number of occupants, daily hot water requirements and house location. The backbone of the proposed system is an ontology that represents the application domain and contains information regarding the various domestic solar hot water system components along with their interrelationships. Ontologies are a rapidly evolving knowledge representation paradigm that offers various advantages and, when deployed specifically in the domestic solar hot water systems domain, deliver improved representation, sharing and re-use of the relevant information. As a conclusion, this paper presents an ontology-driven decision support system for facilitating the selection of domestic solar hot water system, which delivers certain advantages, such as sustainability of the decision support system itself, due to its open and interoperable knowledge-base, and its adaptability/flexibility in decision making policies, due to is semantic (ontological) nature.

On updates of hybrid knowledge bases composed of ontologies and rules

Throughout the last decade, two distinct knowledge representation paradigms have been standardised to capture rich metadata on the Web: ontology languages based on Classical Logic and reasoning rules based on Logic Programming. Both offer important features for knowledge representation and the interest in their integration has recently resulted in frameworks for hybrid knowledge bases that consist of an ontology and a rule component.Instead of the usual static view of hybrid knowledge, in this paper we address its dynamics and in particular focus on updates. We develop two hybrid update semantics that fit the needs of particular use cases of hybrid knowledge and provide the expected results when used in specific application domains. The first semantics uses a given ontology update operator to update the ontology component of a hybrid knowledge base in the presence of static rules. Inspired by a realistic application, and based on a generalised notion of splitting, known from Logic Programming, the second semantics offers a way to modularly combine an ontology update operator with a rule update semantics. It can be used for performing updates of hybrid knowledge bases consisting of ontology and rule layers that share information through a rule-based interface.Both of these developments constitute solutions to the problem of hybrid updates for restricted classes of hybrid knowledge bases. We examine their fundamental formal properties and show that despite the different ideas behind each of them, they are fully compatible with one another, i.e. when both are applicable, they lead to the same result.

Study on Digital Content Representation from Direct Label Graph to RDF/OWL Language into Semantic Web

An increasing number of publication and consumptions of media data on the social and dynamic web has allowed ontology technology to grow up unpredictable. News agencies, cultural heritage sites, social media companies and ordinary users contribute a large portion of media contents across web community. These huge amounts of media contents are generally accessed via standardized and proprietary metadata formats through semantic web. But nearly all cases need specific, standardized, and more expressive methods to represent media data into the knowledge representation paradigm. This paper proposes the proper methods to express media ontology based on the nature of media data. At first RDF graph representation model is used to show the expressive power of domain classification with direct label graph concepts. Secondly, events and object class domain are used to express relational properties of media content. Finally, the events and object class domain is expressed into RDF/OWL language, as preferable and standardized language to represent media data in the semantic web.

Reasoning with Probabilistic and Deterministic Graphical Models: Exact Algorithms

Graphical models (e.g., Bayesian and constraint networks, influence diagrams, and Markov decision processes) have become a central paradigm for knowledge representation and reasoning in both artificial intelligence and computer science in general. These models are used to perform many reasoning tasks, such as scheduling, planning and learning, diagnosis and prediction, design, hardware and software verification, and bioinformatics. These problems can be stated as the formal tasks of constraint satisfaction and satisfiability, combinatorial optimization, and probabilistic inference. It is well known that the tasks are computationally hard, but research during the past three decades has yielded a variety of principles and techniques that significantly advanced the state of the art. In this book we provide comprehensive coverage of the primary exact algorithms for reasoning with such models. The main feature exploited by the algorithms is the model's graph. We present inference-based, message-passing schemes (e.g., variable-elimination) and search-based, conditioning schemes (e.g., cycle-cutset conditioning and AND/OR search). Each class possesses distinguished characteristics and in particular has different time vs. space behavior. We emphasize the dependence of both schemes on few graph parameters such as the treewidth, cycle-cutset, and (the pseudo-tree) height. We believe the principles outlined here would serve well in moving forward to approximation and anytime-based schemes. The target audience of this book is researchers and students in the artificial intelligence and machine learning area, and beyond. Table of Contents: Preface / Introduction / What are Graphical Models / Inference: Bucket Elimination for Deterministic Networks / Inference: Bucket Elimination for Probabilistic Networks / Tree-Clustering Schemes / AND/OR Search Spaces and Algorithms for Graphical Models / Combining Search and Inference: Trading Space for Time / Conclusion / Bibliography / Author's Biography

An end-user approach to business process modeling

This paper discusses the main differences between humanistic and mechanistic business process modeling. While the mechanistic approach requires strict process formalization, emphasizes technical details, and constrains the modeling task to technology experts, the humanistic approach is more centered on the end-user. We developed a modeling approach and a collaborative tool supporting end-user business process modeling. Design storyboards were adopted as a paradigm for knowledge representation and visual composition. The main contributions from this research include the knowledge representation structure and a collaborative tool supporting visual composition of business process models.