Knowledge Representation System Research Articles

The Mathematical Abstraction Theory, The Fundamentals for Knowledge Representation and Self-Evolving Autonomous Problem Solving Systems

The study represents a new way to describe knowledge with generalized universal algebra allowing loop structures so very important in AI languages and which gives an extensive variety of notional relations between net entities without restricting the semantic use. Consequently a new syntax model for solving problems defined by said nets is established flexibly utilizing notional similarities with original problems to further match solutions in memory data banks thus additionally creating transducer graphs of solving rewrite systems and thereof closure system of solving classes. The study introduces universal partitioning to widen environmental attachments subject to abstraction relations, yielding universal macros from parallel TD-solutions. Net NUO-presentations are delivered providing more general coverage enabling net block homomorphism to be used for TD-solution generation. A special attention is given to cardinalities of basic solutions. Second order parallel relation is introduced for distinct solution set bases. Finally direct products of power sets in abstraction relations serve as ingredients for multiple level abstraction algebra which is taken in account for determining self-evolving solving systems. This is reached by tree different stages offering combinational approach in multiple power solution families and iterative solving, thus creating solution basis for evolutional levels.

Incremental Tabling in Support of Knowledge Representation and Reasoning

AbstractResolution-based Knowledge Representation and Reasoning (KRR) systems, such as Flora-2, Silk or Ergo, can scale to tens or hundreds of millions of facts, while supporting reasoning that includes Hilog, inheritance, defeasibility theories, and equality theories. These systems handle the termination and complexity issues that arise from the use of these features by a heavy use of tabled resolution. In fact, such systems table by default all rules defined by users, unless they are simple facts.Performing dynamic updates within such systems is nearly impossible unless the tables themselves can be made to react to changes. Incremental tabling as first implemented in XSB (Saha 2006) partially addressed this problem, but the implementation was limited in scope and not always easy to use. In this paper, we introducetransparent incremental tablingwhich at the semantic level supports updates in the 3-valued well-founded semantics, while guaranteeing full consistency of all tabled queries. Transparent incremental tabling also has significant performance improvements over previous implementations, including lazy recomputation, and control over the dependency structures used to determine how tables are updated.

A Discretization Method of Continuous Attributes Based on Rough Set

when the rough set be used to deal with Knowledge representation system, the data in decision table should be expressed in discrete data, if some conditions or decision attribute is continuous value, which should be discrete Before process.Discretization is not specific data processing only by rough set theory , people have conducted extensive research on discretization problem before the rough set theory put forward , and Made a lot of progress ,but the discretization technique is can not be completely in common used in every subject, different areas have their own unique requirements and handling .This paper proposes a discretization algorithm based on regular conditional entropy.

Monads for the formalization of a pattern matching procedure

Pattern matching, if understood in a broad enough sense, includes a variety of operations, from simple pattern matching operations implemented in a number of programming languages to matching a frame pattern to a frame example, which underlies some knowledge representation systems in artificial intelligence. In this paper, a mathematical language is presented that is based on the concepts of category theory and allows one to formally describe various types of pattern matching from a unified point of view. A typical example of a pattern is given by an expression containing variables that can be substituted by other expressions. The category-theoretical description of such patterns presented in the paper is close to the Kleisli construction, which is often used for the formalization of a substitution of expressions for variables. However, the category presented in the paper is not the dual of the Kleisli category, and each pattern in it is assigned a morphism; this allows one to more clearly describe the idea of matching. In particular, the language proposed allows one to describe rules based on pattern matching. Such rules are often used in computer systems.

A knowledge extraction and representation system for narrative analysis in the construction industry

Many researchers advocate that the real-world narratives shared by experts or knowledge workers are helpful in teaching and educating novices to learn new knowledge and skills. Narrative analysis is a useful method for experts to understand narratives. However, it does not produce any clear or explicit layouts. This is not easy for a new learner without prior knowledge to glean the right messages from narratives within a short time. In this paper, a narrative knowledge extraction and representation system (NKERS) is presented to extract and represent narrative knowledge in an effective manner. The NKERS is composed of a narrative knowledge element extraction algorithm, a narrative knowledge representation method and a narrative knowledge database. A prototype system has been built and trial implemented in the construction industry. The results show that the domain experts agree that the narrative maps generated by the NKERS can effectively represent narrative elements and flows. Three-quarters of respondents expressed that they will use the produced narrative maps in their training courses to facilitate students’ learning.

The application of granular computing in electric vehicle fault diagnosis

In this paper, granular computing algorithm is studied and a granular computing application scheme for electric vehicle fault diagnosis is developed. It is shown that the granular computing has significant advantages on depicting the importance of different attributes for a knowledge representation system. A granular computing-back propagation algorithm can then be designed based on the information of the acceleration failure fault of a parallel-series hybrid electric vehicle including accelerator pedal position, vehicle speed, and so on, in the sense that not only the amount of fault data is effectively reduced, but also better accuracy and efficiency for the electric vehicle fault diagnosis can be obtained. Simulation results are presented to demonstrate the validity and effectiveness of the proposed application scheme.

The Application of Granular Computing in Electric Vehicle Fault Diagnosis

In this paper, granular computing algorithm is studied and a granular computing application scheme for electric vehicle fault diagnosis is developed. It is shown that the granular computing has significant advantages on depicting the importance of different attributes for a knowledge representation system. A granular computing-back propagation algorithm can then be designed based on the information of the acceleration failure fault of a parallel-series hybrid electric vehicle including accelerator pedal position, vehicle speed, and so on, in the sense that not only the amount of fault data is effectively reduced, but also better accuracy and efficiency for the electric vehicle fault diagnosis can be obtained. Simulation results are presented to demonstrate the validity and effectiveness of the proposed application scheme.

Constructing Knowledge Representation Systems with First-Order Formulas as Atoms

Constructing Knowledge Representation Systems with First-Order Formulas as Atoms

Rough set approach to incomplete multiscale information system.

Multiscale information system is a new knowledge representation system for expressing the knowledge with different levels of granulations. In this paper, by considering the unknown values, which can be seen everywhere in real world applications, the incomplete multiscale information system is firstly investigated. The descriptor technique is employed to construct rough sets at different scales for analyzing the hierarchically structured data. The problem of unravelling decision rules at different scales is also addressed. Finally, the reduct descriptors are formulated to simplify decision rules, which can be derived from different scales. Some numerical examples are employed to substantiate the conceptual arguments.

An Effective Reasoning Algorithm for Question Answering System

Knowledge representation (KR) is the most desirable area of research to make the system intelligent. Today is the era of knowledge that requires articulations, semantic, syntax etc. These requirements, forced to design a general system which is applicable to represent declarative as well as procedural knowledge. Without effective inference/reasoning mechanism, the strength and utility of knowledge representation technique fulfill the partial requirement for an intelligent system. The objective of this research work is to present the effective/ appropriate knowledge representation technique for representing the general knowledge and a reasoning algorithm for Question Answering system (QAS) work as story reader, so that appropriate knowledge can be infer from the system. The architecture of knowledge representation system is capable to integrate different type of knowledge and it is cost effective also.

Transdisciplinarity Problems and the Role of Informatics

Some methods and tools of information-technological support of transdisciplinary scientific investigations are proposed and reviewed during their formation and development. Emphasis is placed on ontology-driven service-oriented systems of knowledge representation in a global network environment.

Concurrent Inference Graphs

Since their popularity began to rise in the mid-2000s there has been significant growth in the number of multi-core and multi-processor computers available. Knowledge representation systems using logical inference have been slow to embrace this new technology. We present the concept of inference graphs, a natural deduction inference system which scales well on multi-core and multi-processor machines. Inference graphs enhance propositional graphs by treating propositional nodes as tasks which can be scheduled to operate upon messages sent between nodes via the arcs that already exist as part of the propositional graph representation. The use of scheduling heuristics within a prioritized message passing architecture allows inference graphs to perform very well in forward, backward, bi-directional, and focused reasoning. Tests demonstrate the usefulness of our scheduling heuristics, and show significant speedup in both best case and worst case inference scenarios as the number of processors increases.

Concurrent Reasoning with Inference Graphs

Since their popularity began to rise in the mid-2000s there has been significant growth in the number of multi-core and multi-processor computers available. Knowledge representation systems using logical inference have been slow to embrace this new technology. We present the concept of inference graphs, a natural deduction inference system which scales well on multi-core and multi-processor machines. Inference graphs enhance propositional graphs by treating propositional nodes as tasks which can be scheduled to operate upon messages sent between nodes via the arcs that already exist as part of the propositional graph representation. The use of scheduling heuristics within a prioritized message passing architecture allows inference graphs to perform very well in forward, backward, bi-directional, and focused reasoning. Tests demonstrate the usefulness of our scheduling heuristics, and show significant speedup in both best case and worst case inference scenarios as the number of processors increases.

Representing concepts in formal ontologies. Compositionality vs. typicality effects

The problem of concept representation is relevant for many subfields of cognitive research, including psychology and philosophy, as well as artificial intelligence. In particular, in recent years it has received a great deal of attention within the field of knowledge representation, due to its relevance for both knowledge engineering as well as ontology-based technologies. However, the notion of a concept itself turns out to be highly disputed and problematic. In our opinion, one of the causes of this state of affairs is that the notion of a concept is, to some extent, heterogeneous, and encompasses different cognitive phenomena. This results in a strain between conflicting requirements, such as compositionality, on the one hand and the need to represent prototypical information on the other. In some ways artificial intelligence research shows traces of this situation. In this paper, we propose an analysis of this current state of affairs. Since it is our opinion that a mature methodology with which to approach knowledge representation and knowledge engineering should also take advantage of the empirical results of cognitive psychology concerning human abilities, we outline some proposals for concept representation in formal ontologies, which take into account suggestions from psychological research. Our basic assumption is that knowledge representation systems whose design takes into account evidence from experimental psychology (and which, therefore, are more similar to the human way of organizing and processing information) may therefore give better results in many applications (e.g. in the fields of information retrieval and semantic web).

Basic-level categories: A review

This paper analyses selected literature on basic-level categories, explores related theories and discusses theoretical explanations of the phenomenon of basic-level categories. A substantial body of research has proposed that basic-level categories are the first categories formed during perception of the environment, the first learned by children and those most used in language. Experimental studies suggest that high-level (or superordinate) categories lack informativeness because they are represented by only a few attributes and low-level (or subordinate) categories lack cognitive economy because they are represented by too many attributes. Studies in library and information science have demonstrated the prevalence of basic-level categories in knowledge organization and representation systems such as thesauri and in image indexing and retrieval; and it has been suggested that the universality of basic-level categories could be used for building crosswalks between classificatory systems and user-centred indexing. However, while there is evidence of the pervasiveness of basic-level categories, they may actually be unstable across individuals, domains or cultures and thus unable to support broad generalizations. This paper discusses application of Heidegger’s notion of handiness as a framework for understanding the relational nature of basic-level categories.

Inferring robot goals from violations of semantic knowledge

A growing body of literature shows that endowing a mobile robot with semantic knowledge and with the ability to reason from this knowledge can greatly increase its capabilities. In this paper, we present a novel use of semantic knowledge, to encode information about how things should be, i.e. norms, and to enable the robot to infer deviations from these norms in order to generate goals to correct these deviations. For instance, if a robot has semantic knowledge that perishable items must be kept in a refrigerator, and it observes a bottle of milk on a table, this robot will generate the goal to bring that bottle into a refrigerator. The key move is to properly encode norms in an ontology so that each norm violation results in a detectable inconsistency. A goal is then generated to bring the world back in a consistent state, and a planner is used to transform this goal into actions. Our approach provides a mobile robot with a limited form of goal autonomy: the ability to derive its own goals to pursue generic aims. We illustrate our approach in a full mobile robot system that integrates a semantic map, a knowledge representation and reasoning system, a task planner, and standard perception and navigation routines.

A Principled Approach to Grammars for Controlled Natural Languages and Predictive Editors

Controlled natural languages (CNL) with a direct mapping to formal logic have been proposed to improve the usability of knowledge representation systems, query interfaces, and formal specifications. Predictive editors are a popular approach to solve the problem that CNLs are easy to read but hard to write. Such predictive editors need to be able to "look ahead" in order to show all possible continuations of a given unfinished sentence. Such lookahead features, however, are difficult to implement in a satisfying way with existing grammar frameworks, especially if the CNL supports complex nonlocal structures such as anaphoric references. Here, methods and algorithms are presented for a new grammar notation called Codeco, which is specifically designed for controlled natural languages and predictive editors. A parsing approach for Codeco based on an extended chart parsing algorithm is presented. A large subset of Attempto Controlled English (ACE) has been represented in Codeco. Evaluation of this grammar and the parser implementation shows that the approach is practical, adequate and efficient.

The Answer Set Programming Competition

The Answer Set Programming (ASP) Competition is a biannual event for evaluating declarative knowledge representation systems on hard and demanding AI problems. The competition consists of two main tracks: the ASP system track and the model and solve track. The traditional system track compares dedicated answer set solvers on ASP benchmarks, while the model and solve track invites any researcher and developer of declarative knowledge representation systems to participate in an open challenge for solving sophisticated AI problems with their tools of choice. This article provides an overview of the ASP Competition series, reviews its origins and history, giving insights on organizing and running such an elaborate event, and briefly discusses the lessons learned so far.

Semantic networks as a component of the unified model of knowledge representation in intellectual systems

The article considering the use of semantic networks in a unified system of knowledge representation. Consideration of the general structure of the information loop, which provides process control. The necessity of unification of representation of knowledge management for building effective intelligent decision support systems in the information technology systems of the control loop.

Human reliability analysis data obtainment through fuzzy logic in nuclear plants

Human error has been recognized as an important factor for many industrial and nuclear accidents occurrence. Human error data is scarcely available for different reasons among which, lapses in historical database registry methodology is an important one. Human Reliability Analysis (HRA) is an usual tool employed to estimate the probability that an operator will reasonably perform a system required task in required time without degrading the system. This meta-analysis requires specific Human Error Probability estimates for most of its procedure. This work obtains Human Error Probability (HEP) estimates from operator's actions in response to emergency situations hypothesis on Research Reactor IEA-R1 from IPEN, Brazil. Through this proposed methodology HRA should be able to be performed even with shortage of related human error statistical data. A Performance Shaping Factors (PSF's) evaluation in order to classify and estimate their influence level onto the operator's actions and to determine their actual state over the plant was also done. Both HEP estimation and PSF evaluation were done based on expert judgment using interviews and questionnaires. Expert group was established based on selected IEA-R1 operators, and their evaluation were put into a knowledge representation system which used linguistic variables and group evaluation values that were obtained through Fuzzy Logic and Fuzzy Set theory. HEP obtained values show good agreement with literature published data corroborating the proposed methodology as a good alternative to be used on HRA.