Knowledge Representation Language Research Articles

A Semantical Analysis of Second-Order Propositional Modal Logic

This paper is aimed as a contribution to the use of formal modal languages in Artificial Intelligence. We introduce a multi-modal version of Second-order Propositional Modal Logic (SOPML), an extension of modal logic with propositional quantification, and illustrate its usefulness as a specification language for knowledge representation as well as temporal and spatial reasoning. Then, we define novel notions of (bi)simulation and prove that these preserve the interpretation of SOPML formulas. Finally, we apply these results to assess the expressive power of SOPML.

Zero-Suppressed Sentential Decision Diagrams

The Sentential Decision Diagram (SDD) is a prominent knowledge representation language that subsumes the Ordered Binary Decision Diagram (OBDD) as a strict subset. Like OBDDs, SDDs have canonical forms and support bottom-up operations for combining SDDs, but they are more succinct than OBDDs. In this paper we introduce an SDD variant, called the Zero-suppressed Sentential Decision Diagram (ZSDD). The key idea of ZSDD is to employ new trimming rules for obtaining a canonical form. As a result, ZSDD subsumes the Zero-suppressed Binary Decision Diagram (ZDD) as a strict subset. ZDDs are known for their effectiveness on representing sparse Boolean functions. Likewise, ZSDDs can be more succinct than SDDs when representing sparse Boolean functions. We propose several polytime bottom-up operations over ZSDDs, and a technique for reducing ZSDD size, while maintaining applicability to important queries. We also specify two distinct upper bounds on ZSDD sizes; one is derived from the treewidth of a CNF and the other from the size of a family of sets. Experiments show that ZSDDs are smaller than SDDs or ZDDs for a standard benchmark dataset.

An XML-based machining energy estimation tool from process plans

Excessive energy consumption has become a worldwide issue in today’s manufacturing industry. Software tools for estimating manufacturing energy consumption that could later be used to integrate with CAD/CAM systems are in demand. This research develops software tools for estimating manufacturing energy consumption in machining operations based on manufacturing process plans. This study first reviews literature and background in related areas and then presents the method to calculate manufacturing energy in machining operations. eXtended Markup Language (XML) has been adopted as the knowledge representation language for machining process plans and forms one of the inputs for energy estimation. Since XML plays an important role in estimating machining energy, this article introduces XML validation and parsing. The method is demonstrated via case studies for machining energy estimation in machining operations. The method will assist users who have little knowledge about energy computations to estimate energy consumption during the manufacturing phase. Such energy estimation can be used to redesign the part and assemblies, leading to products with reduced machining energy.

PAGOdA: Pay-As-You-Go Ontology Query Answering Using a Datalog Reasoner

Answering conjunctive queries over ontology-enriched datasets is a core reasoning task for many applications. Query answering is, however, computationally very expensive, which has led to the development of query answering procedures that sacrifice either expressive power of the ontology language, or the completeness of query answers in order to improve scalability. In this paper, we describe a hybrid approach to query answering over OWL 2 ontologies that combines a datalog reasoner with a fully-fledged OWL 2 reasoner in order to provide scalable `pay-as-you-go' performance. The key feature of our approach is that it delegates the bulk of the computation to the datalog reasoner and resorts to expensive OWL 2 reasoning only as necessary to fully answer the query. Furthermore, although our main goal is to efficiently answer queries over OWL 2 ontologies and data, our technical results are very general and our approach is applicable to first-order knowledge representation languages that can be captured by rules allowing for existential quantification and disjunction in the head; our only assumption is the availability of a datalog reasoner and a fully-fledged reasoner for the language of interest, both of which are used as `black boxes'. We have implemented our techniques in the PAGOdA system, which combines the datalog reasoner RDFox and the OWL 2 reasoner HermiT. Our extensive evaluation shows that PAGOdA succeeds in providing scalable pay-as-you-go query answering for a wide range of OWL 2 ontologies, datasets and queries.

Concept Implicate Tree for Description Logics

Description logics are a class of knowledge representation languages with high expressive power, and the com- putational complexities of the queries of these expressive description logics are PSPACE-complete. Moreover, knowledge compilation can be regarded as a new direction of research for dealing with the computational intractable reasoning prob- lems. In fact, knowledge compilation based on description logic has been investigated in recent years. However, when the compiled knowledge base is exponential in the size of original knowledge base, the queries are not fast enough. Therefore, we propose a new knowledge compilation method for description logic so that the queries can be done in linear time in the size of the query. In this paper, we first introduce concept implicate tree for ALC concept. Then, we present an algorithm, which can transform an ALC concept into an equivalent concept implicate tree, and prove that each branch of the tree is an implicate of this concept. Finally, we prove that the queries are computable in linear time. Our method has an im- portant property that no matter how large the concept implicate tree is, any query can be done in linear time in the size of the query.

A Survey on Uyghur Ontology

Ontology has become a hot research topic in the fields of artificial intelligence such as knowledge representation, knowledge engineering and natural language processing (NLP) etc.. In this paper, according to the application requirements in the intelligent Uyghur information retrieval system, by giving the brief description about the ontology and its construction rules, methods, tools and descriptive languages, have conducted the contrastive analysis the current research status about the ontology in domestic and abroad, and then sum up some key issues in Uyghur ontology construction procedures and some early achievements. After all, the further research directions are also proposed in this paper.

The complexity of reasoning with FODD and GFODD

Recent work introduced Generalized First Order Decision Diagrams (GFODD) as a knowledge representation language that is useful in mechanizing decision theoretic planning in relational domains. GFODDs generalize function-free first order logic and include numerical values and numerical generalizations of existential and universal quantification. Previous work presented heuristic inference algorithms for GFODDs and implemented these heuristics in systems for decision theoretic planning. In this paper, we study the complexity of the computational problems addressed by such implementations. In particular, we study the evaluation problem, the satisfiability problem, and the equivalence problem for GFODDs under the assumption that the size of the intended model is given with the problem, a restriction that guarantees decidability. Our results provide a complete characterization placing these problems within the polynomial hierarchy. The same characterization applies to the corresponding restriction of problems in first order logic, giving an interesting new avenue for efficient inference when the number of objects is bounded. Our results show that for Σk formulas, and for corresponding GFODDs, evaluation and satisfiability are ΣkP complete, and equivalence is Πk+1P complete. For Πk formulas evaluation is ΠkP complete, satisfiability is one level higher and is Σk+1P complete, and equivalence is Πk+1P complete.

ОНТОЛОГІЇ У СТРАХОВІЙ МЕДИЦИНІ

<p>Introduction of ontological representations in insurance medicine is difficult due to lack of skilled professionals in this area of knowledge and ontology representation languages are complex and far from the knowledge of medical insurance. In addition, medical insurance is based on a large volume of procedural knowledge, which are also difficult to generalize and order. At the same time, harmonization of risk medical insurance and the desire of the population to purchase medical services urgently require a certain systematization. Outlines ways to create an ontology of health insurance are identified.</p>

Lazy Model Expansion: Interleaving Grounding with Search

Finding satisfying assignments for the variables involved in a set of constraints can be cast as a (bounded) model generation problem: search for (bounded) models of a theory in some logic. The state-of-the-art approach for bounded model generation for rich knowledge representation languages like ASP and FO(.) and a CSP modeling language such as Zinc, is ground-and-solve: reduce the theory to a ground or propositional one and apply a search algorithm to the resulting theory. An important bottleneck is the blow-up of the size of the theory caused by the grounding phase. Lazily grounding the theory during search is a way to overcome this bottleneck. We present a theoretical framework and an implementation in the context of the FO(.) knowledge representation language. Instead of grounding all parts of a theory, justifications are derived for some parts of it. Given a partial assignment for the grounded part of the theory and valid justifications for the formulas of the non-grounded part, the justifications provide a recipe to construct a complete assignment that satisfies the non-grounded part. When a justification for a particular formula becomes invalid during search, a new one is derived; if that fails, the formula is split in a part to be grounded and a part that can be justified. Experimental results illustrate the power and generality of this approach.

A semantic approach for enhancing assistive services in ubiquitous robotics

The Ambient Intelligence (AmI) technologies have the potential to create intelligent environments with new generation of assistive services, enhanced with ubiquitous robots. These environments have the ability to be anticipatory, responsive and intelligent providers of assistive services anytime and anywhere. These services can assist frail persons effectively in their daily tasks. One of the main challenging research problems in assistive robotics is to endow ubiquitous robots with ability to pro-actively taking on some tasks to help humans in performing complex activities, by participating with them just as other humans do, in normal societies or organizations.In this paper, we propose a collective intelligence framework based on narrative reasoning and natural language processing. In the proposed approach, we propose a hybrid model that bridges together the Narrative Knowledge Representation Language (NKRL), from natural language processing field, and the HARMS (Humans, software Agents, Robots, Machines and Sensors) model, from multi-agent systems engineering field. This model is able to (i) drive the dialogues between humans, robots and smart devices, (ii) understand a complex situation, and (iii) trigger reactive actions, in the ubiquitous environment, according to given contexts.Two scenarios dedicated to the assistance of a frail person in a smart home equipped with a companion robot and smart objects are implemented and discussed for validation purposes of the proposed framework.

Multimodal Fusion, Fission and Virtual Reality Simulation for an Ambient Robotic Intelligence

In this paper, we present an architecture that demonstrates multimodal fusion and fission using semantic agents and web services that interacts with worldwide-web computers, services and users. This solution extracts the meaning of a situation using the semantic memory of agents to manage the interaction process involved. The fusion of values from different sensors produces an event that needs implementation. The fission process suggests a detailed set of actions that are for implementation. Before such actions are implemented by actuators, these actions are first evaluated in a virtual environment which mimics the real-world environment. If no danger arises from such virtual evaluation, then implementation is feasible. Otherwise, there might be a need to add one or more smaller actions to render the action safe and free from danger. Our work presents the following contributions: (i) a design of agent memory and a model of the world environment using a knowledge representation language that is compatible with the current standards, (ii) creation of a pervasive architecture with several scenarios of composition and adaptation, (iii) presentation of how agents and services interact to provide support in a real-world, and (iv) simulation of an event in a virtual environment to assess the feasibility of the event's implementation.

A structured and in-depth representation of the semantic content of elementary and complex events

'Events' are understood here, following the standard philosophical acceptation and in agreement with a Davidsonian and neo–Davidsonian approach, as the explicit, 'narrative' accounts of (spatio–temporal constrained) behaviours, states, actions and mutual interactions that concern animate/inanimate, human/non–human, abstract/concrete etc. well–identified 'entities'. Within this 'explicit' and 'symbolic' framework, we describe then how to isolate and represent in a computer–suitable form 'elementary events' inside a stream of narrative information, how to connect together these elementary events to give rise to 'complex events' through, e.g., 'causal' or 'conditional' relationships and how to make use of these formal structures for querying and inference operations. In this context, we will make use of NKRL (the Narrative Knowledge Representation Language), a language/environment expressly specified and implemented for dealing with narratives and temporal information, as formal support for our line of reasoning.

개념그래프 도구의 기능 및 특성 조사

Abstract Intelligent systems are systems that mainly use knowledge rather than data or information. Therefore, knowledge representation is an important factor for intelligent system construction. Conceptual graph is a logical knowledge representation language which has graphical form and it can represent knowledge efficiently. It is, however, cumbersome to use conceptual graphs directly for programming. Various tools were developed to overcome this difficulties. In this paper, we survey on functions and characteristics of conceptual graph tools that can be utilized for constructing intelligent systems by using conceptual graphs. The result of this survey will be very helpful to use conceptual graphs for development of intelligent systems. Keywords : Conceptual Graph, Intelligent System, Knowledge Representation, Conceptual Graph Tool Received 12 September 2014, Revised 25 October 2014Accepted 20 December 2014Corresponding Author: Gi-Chul Yang(Mokpo National University) Email: gcyang@mokpo.ac.krⒸ The Society of Digital Policy & Management. All rights reserved. This is an open-access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is ISSN: 1738-1916 properly cited.

Integration of an OWL-DL knowledge base with an EHR prototype and providing customized information.

When clinicians use electronic health record (EHR) systems, their ability to obtain general knowledge is often an important contribution to their ability to make more informed decisions. In this paper we describe a method by which an external, formal representation of clinical and molecular genetic knowledge can be integrated into an EHR such that customized knowledge can be delivered to clinicians in a context-appropriate manner.Web Ontology Language-Description Logic (OWL-DL) is a formal knowledge representation language that is widely used for creating, organizing and managing biomedical knowledge through the use of explicit definitions, consistent structure and a computer-processable format, particularly in biomedical fields. In this paper we describe: 1) integration of an OWL-DL knowledge base with a standards-based EHR prototype, 2) presentation of customized information from the knowledge base via the EHR interface, and 3) lessons learned via the process. The integration was achieved through a combination of manual and automatic methods. Our method has advantages for scaling up to and maintaining knowledge bases of any size, with the goal of assisting clinicians and other EHR users in making better informed health care decisions.

Vicious Circle Principle and Logic Programs with Aggregates

AbstractThe paper presents a knowledge representation language $\mathcal{A}log$ which extends ASP with aggregates. The goal is to have a language based on simple syntax and clear intuitive and mathematical semantics. We give some properties of $\mathcal{A}log$, an algorithm for computing its answer sets, and comparison with other approaches.

Sentiments analysis at conceptual level making use of the Narrative Knowledge Representation Language

This paper illustrates some of the knowledge representation structures and inference procedures proper to a high-level, fully implemented conceptual language, NKRL (Narrative Knowledge Representation Language). The aim is to show how these tools can be used to deal, in a sentiment analysis/opinion mining context, with some common types of human (and non-human) “behaviors”. These behaviors correspond, in particular, to the concrete, mutual relationships among human and non-human characters that can be expressed under the form of non-fictional and real-time “narratives” (i.e., as logically and temporally structured sequences of “elementary events”).

Formalizing Negotiations Using Logic Programming

The article introduces a logical framework for negotiation among dishonest agents. The framework relies on the use of abductive logic programming as a knowledge representation language for agents to deal with incomplete information and preferences. The article shows how intentionally false or inaccurate information of agents can be encoded in the agents' knowledge bases. Such disinformation can be effectively used in the process of negotiation to have desired outcomes by agents. The negotiation processes are formulated under the answer set semantics of abductive logic programming, and they enable the exploration of various strategies that agents can employ in their negotiation. A preliminary implementation has been developed using the ASP-Prolog platform.

Constructing a meta-model for assembly tolerance types with a description logic based approach

There is a critical requirement for semantic interoperability among heterogeneous computer-aided tolerancing (CAT) systems with the sustainable growing demand of collaborative product design. But current data exchange standard for exchanging tolerance information among these systems can only exchange syntaxes and cannot exchange semantics. Semantic interoperability among heterogeneous CAT systems is difficult to be implemented only with this standard. To address this problem, some meta-models of tolerance information supporting semantic interoperability and an interoperability platform based on these meta-models should be constructed and developed, respectively. This paper mainly focuses on the construction of a meta-model for assembly tolerance types with a description logic ALC(D) based approach. Description logics, a family of knowledge representation languages for authoring ontologies, are well-known for having rigorous logic-based semantics which supports semantic interoperability. ALC(D) can provide a formal method to describe the research objects and the relations among them. In this formal method, constraint relations among parts, assembly feature surfaces and geometrical features are defined with some ALC(D) assertional axioms, and the meta-model of assembly tolerance types is constructed through describing the spatial relations between geometrical features with some ALC(D) terminological axioms. Besides, ALC(D) can also provide a highly efficient reasoning algorithm to automatically detect the inconsistency of the knowledge base, a finite set of assertional and terminological axioms. With this reasoning algorithm, assembly tolerance types for each pair of geometrical features are generated automatically through detecting the inconsistencies of the knowledge base. An application example is provided to illustrate the process of generating assembly tolerance types.

A Formalism and Method for Representing and Reasoning with Process Models Authored by Subject Matter Experts

Enabling Subject Matter Experts (SMEs) to formulate knowledge without the intervention of Knowledge Engineers (KEs) requires providing SMEs with methods and tools that abstract the underlying knowledge representation and allow them to focus on modeling activities. Bridging the gap between SME-authored models and their representation is challenging, especially in the case of complex knowledge types like processes, where aspects like frame management, data, and control flow need to be addressed. In this paper, we describe how SME-authored process models can be provided with an operational semantics and grounded in a knowledge representation language like F-logic to support process-related reasoning. The main results of this work include a formalism for process representation and a mechanism for automatically translating process diagrams into executable code following such formalism. From all the process models authored by SMEs during evaluation 82 percent were well formed, all of which executed correctly. Additionally, the two optimizations applied to the code generation mechanism produced a performance improvement at reasoning time of 25 and 30 percent with respect to the base case, respectively.

Detection and exploitation of functional dependencies for model generation

AbstractRecent work in Answer Set Programming has integrated ideas from Constraint Programming. This has led to a new field called ASP Modulo CSP (CASP), in which the ASP language is enriched with constraint atoms representing constraint satisfaction problems. These constraints have a more compact grounding and are handled by a new generation of search algorithms. However, the burden is on the modeler to exploit these new constructs in his declarative problem specifications. Here, we explore how to remove this burden by automatically generating constraint atoms. We do so in the context of FO(·)IDP, a knowledge representation language that extends first-order logic with, among others, inductive definitions, arithmetic and aggregates. We uncover functional dependencies in declarative problem specifications with a theorem prover and exploit them with a transformation that introduces functions. Experimental evaluation shows that we obtain more compact groundings and better search performance.