Knowledge Representation Language Research Articles

Knowledge-Based Approach and System for Processof School/University Orientation

─The school / university orientation interests a broad and often badly informed public. Technically, it is an important multicriterion decision problem, which supposes the combination of much academic professional and/or lawful knowledge, which in turn justifies software resorting to the techniques of Artificial Intelligence. CORUS is an expert system of the Conseil et ORientation Universitaire et Scolaire, based on a knowledge representation language (KRL) with rules and objects, called/ known as Ibn Rochd. CORUS was developed thanks to DeGSE, a workshop of cognitive engineering which supports this LRC. CORUS works out many acceptable solutions for the case considered, and retains the most satisfactory among them. Several versions of CORUS have extended its services gradually. Keywords─Kknowledge Engineering, Multicriterion Decision, Knowledge-Based Systems..

A Knowledge Engineering Workshop: Application for Choise Car

This paper proposes a declarative language for knowledge representation (Ibn Rochd), and its environment of exploitation (DeGSE). This DeGSE system was designed and developed to facilitate Ibn Rochd writing applications. The system was tested on several knowledge bases by ascending complexity, culminating in a system for recognition of a plant or a tree, and advisors to purchase a car, for pedagogical and academic guidance, or for bank savings and credit. Finally, the limits of the language and research perspectives are stated. Keywords—Knowledge representation, declarative language, Ibn Rochd, DeGSE, facets, cognitive approach.

Reasoning with Very Expressive Fuzzy Description Logics

It is widely recognized today that the management of imprecision and vagueness will yield more intelligent and realistic knowledge-based applications. Description Logics (DLs) are a family of knowledge representation languages that have gained considerable attention the last decade, mainly due to their decidability and the existence of empirically high performance of reasoning algorithms. In this paper, we extend the well known fuzzy ALC DL to the fuzzy SHIN DL, which extends the fuzzy ALC DL with transitive role axioms (S), inverse roles (I), role hierarchies (H) and number restrictions (N). We illustrate why transitive role axioms are difficult to handle in the presence of fuzzy interpretations and how to handle them properly. Then we extend these results by adding role hierarchies and finally number restrictions. The main contributions of the paper are the decidability proof of the fuzzy DL languages fuzzy-SI and fuzzy-SHIN, as well as decision procedures for the knowledge base satisfiability problem of the fuzzy-SI and fuzzy-SHIN.

Using a Natural Language Understanding System to Generate Semantic Web Content

We describe our research on automatically generating rich semantic annotations of text and making it available on the Semantic Web. In particular, we discuss the challenges involved in adapting the OntoSem natural language processing system for this purpose. OntoSem, an implementation of the theory of ontological semantics under continuous development for over 15 years, uses a specially constructed NLP-oriented ontology and an ontological-semantic lexicon to translate English text into a custom ontology- motivated knowledge representation language, the language of text meaning representations (TMRs). OntoSem concentrates on a variety of ambiguity resolution tasks as well as processing unexpected input and reference. To adapt OntoSem’s representation to the Semantic Web, we developed a translation system, OntoSem2OWL, between the TMR language into the Semantic Web language OWL. We next used OntoSem and OntoSem2OWL to support SemNews, an experimental Web service that monitors RSS news sources, processes the summaries of the news stories, and publishes a structured representation of the meaning of the text in the news story.

Machine Translation for Manufacturing: A Case Study at Ford Motor Company

Machine translation (MT) was one of the first applications of artificial intelligence technology that was deployed to solve real-world problems. Since the early 1960s, researchers have been building and utilizing computer systems that can translate from one language to another without requiring extensive human intervention. In the late 1990s, Ford Vehicle Operations began working with Systran Software Inc. to adapt and customize its machine-translation technology in order to translate Ford's vehicle assembly build instructions from English to German, Spanish, Dutch, and Portuguese. The use of machine translation was made necessary by the vast amount of dynamic information that needed to be translated in a timely fashion. The assembly build instructions at Ford contain text written in a controlled language as well as unstructured remarks and comments. The MT system has already translated more than 7 million instructions into these languages and is an integral part of the overall manufacturing process-planning system used to support Ford's assembly plants in Europe, Mexico and South America. In this paper, we focus on how AI techniques, such as knowledge representation and natural language processing can improve the accuracy of machine translation in a dynamic environment such as auto manufacturing.

Spatial location and its relevance for terminological inferences in bio-ontologies.

BackgroundAn adequate and expressive ontological representation of biological organisms and their parts requires formal reasoning mechanisms for their relations of physical aggregation and containment.ResultsWe demonstrate that the proposed formalism allows to deal consistently with "role propagation along non-taxonomic hierarchies", a problem which had repeatedly been identified as an intricate reasoning problem in biomedical ontologies.ConclusionThe proposed approach seems to be suitable for the redesign of compositional hierarchies in (bio)medical terminology systems which are embedded into the framework of the OBO (Open Biological Ontologies) Relation Ontology and are using knowledge representation languages developed by the Semantic Web community.

Ontologies and reasoning techniques for (legal) intelligent information retrieval systems

An application of Narrative Knowledge Representation Language (NKRL) techniques on (declassified) 'terrorism in Southern Philippines' documents has been carried out in the context of the IST Parmenides project. This paper describes some aspects of this work: it is our belief, in fact, that the Knowledge Representation techniques and the Intelligent Information Retrieval tools used in this experiment can be of some interest also in an 'Ontological Modelling of Legal Events and Legal Reasoning' context.

Standardized Configuration Knowledge Representations as Technological Foundation for Mass Customization

The effective integration of configuration system development with industrial software development is crucial for a successful implementation of a mass customization strategy. On the one hand, configuration knowledge bases must be easy to develop and maintain due to continuously changing product assortments. On the other hand, flexible integrations into existing enterprise applications, e-marketplaces and different facets of supply chain settings must be supported. This paper shows how the model-driven architecture (MDA) as an industrial framework for model development and interchange can serve as a foundation for standardized configuration knowledge representation, thus enabling knowledge sharing in heterogeneous environments. Using UML/Object Constraint Language as standard configuration knowledge representation languages, the representation of configuration domain-specific modeling concepts within MDA is shown and a formal semantics for these concepts is provided which allows a common understanding and interpretation of configuration task descriptions

Transfer of problem-solving strategy using Covlan

Psychological evidence suggests that humans use visual knowledge and reasoning in solving complex problems. We present Covlan, a visual knowledge representation language for representing visual knowledge and supporting visual reasoning. We describe Galatea, a computer program that uses Covlan for analogical transfer of problem-solving procedures from known analogs to new problems. We present the use of Galatea to model analogical visual problem solving by four human experimental participants, and describe one of the four cases in detail. The Galatea model of human problem solving suggests that problem-solving procedures can be effectively represented with Covlan.

Kumbang: A domain ontology for modelling variability in software product families

Variability is the ability of a system to be efficiently extended, changed, customised or configured for use in a particular context. There is an ever-growing demand for variability of software. Software product families are an important means for implementing software variability. We present a domain ontology called Kumbang for modelling the variability in software product families. Kumbang synthesises previous approaches to modelling variability in software product families. In addition, it incorporates modelling constructs developed in the product configuration domain for modelling the variability in non-software products. The modelling concepts include components and features with compositional structure and attributes, the interfaces of components and connections between them, and constraints. The semantics of Kumbang is rigorously described using natural language and a UML profile. We provide preliminary proof of concept for Kumbang: the domain ontology has been provided with a formal semantics by implementing a translation into a general-purpose knowledge representation language with formal semantics and inference support. A prototype tool for resolving variability has been implemented.

Integration of Biological Data with Semantic Networks

In recent years, the broad utilization of high-throughput experimental techniques resulted in a vast amount of expression and interaction data, accompanied by information on metabolic, cell signaling and gene regulatory pathways accumulated in the literature and databases. Thus, one of the major goals of modern bioinformatics is to process and integrate heterogeneous biological data to provide an insight into the inner workings of a cell governed by complex interaction networks. The paper reviews the current development of semantic network (SN) technologies and their applications to the integration of genomic and proteomic data. We also elaborate on our own work that applies a semantic network approach to modeling complex cell signaling pathways and simulating the cause-effect of molecular interactions in human macrophages. The review is concluded with a discussion of the prospective use of semantic networks in bioinformatics practice as an efficient and general language for data integration, knowledge representation and inference. Keywords: Semantic networks, biological data integration, protein interactions, knowledge representation, ontology, semantic web

Representation of Mathematical Knowledge in National Knowledge Infrastructure

The representation of mathematical knowledge is an important aspect of knowledge representation. It is the foundation for knowledge-based automated theorem proving, mathematical knowledge retrieval and intelligent tutoring systems, etc. According to the problems that are encountered in designing the mathematical knowledge representation language in NKI (national knowledge infrastructure) and after the discussion of ontological assumptions for mathematical objects, two kinds of formalisms for the representation of mathematical knowledge are provided. One is a description logic in which the range of an attribute can be a formula in some logical language; and another is a first order logic in which an ontology represented by the description logic is a part of the logical language. In the former representation, if no restrictions are imposed on formulas, then there is no algorithm to realize the reasoning in the resulted knowledge base; in the latter representation, the reasoning in the ontology represented by the description logic is decidable, while in general, for mathematical knowledge described by the first order logic which contains the ontology represented by the description logic, there is no algorithm to realize its reasoning. Hence, in the representation of mathematical knowledge, it is necessary to distinguish conceptual knowledge (knowledge in an ontology) and non-conceptual knowledge (knowledge represented by a language containing the ontology). Frames and description logics can represent and reason effectively about conceptual knowledge, but the addition of non-conceptual knowledge to frames or knowledge bases may make the reasoning in the resulted knowledge bases not decidable and there is even no algorithm to reason about the knowledge bases. Therefore, it is suggested that in representing mathematical knowledge, frames or description logics are used to

Applying Logic of Multiple-Valued Argumentation to Eastern Arguments

In our former paper, we formalized a Logic of Multiple-valued Argumentation (LMA) on an expressive knowledge representation language, Extended Annotated Logic Programming (EALP), in order to make it possible to construct arguments under uncertain information. In this paper, We confirm expressivity and applicability by applying LMA to arguments reflecting Easterners' preference over argumentation as well as Eastern thought and philosophy. In doing so, we exploit a wide variety of complete lattices as truth-values, showing the flexibility and adaptability of LMA to various multiple-valuedness required in argumentation under uncertain information. In particular, we consider a significant specialization of LMA to Tetralemma with an Eastern mind. Through various argument examples, it is shown that LMA allows for a kind of pluralistic argumentation, or a fusion of Eastern and Western argumentation.

A layered controlled natural language for knowledge representation

PENG-L is a framework consisting of a set of controlled natural languages designed for knowledge representation that has the potential to bridge the gap between fragments of natural language and formal languages. PENG-L is bases on a canonical grammatical schema that can be adjusted to define various layers of controlled natural language with different expressive power. The weakest layer of PENG-L is equivalent to propositional logic and offers natural language constructions for describing propositional statements. The next layer is equivalent to a version of description logic and provides natural language constructions to make assertional, terminological, and conditional statements tha can be unambiguously translated into function-free logic programs. The most expressive layer is equivalent to first-order logic and consists of a well-defined subset of natural language that can be unambiguously translated into first-order logic via discourse representation theory. Writting specification texts in PENG-L is supported by a look-ahead text editor that indicates after each word form that the author enters how the text can be continued. The text editor communicates with a language processor that generates the logical representation for the text incrementally and interacts with third-party reasoning services for acceptability checking as well as for question answering.

Towards a cognitive foundation for knowledge representation

Abstract. Knowledge engineering, knowledge management and conceptual modelling are concerned with representing knowledge of business and organizational domains. These research areas use ontologies for knowledge representation. Ontologies are understood either in the philosophical sense as firm metaphysical commitments or in the looser sense of dictionaries or taxonomies.This paper critically examines the understanding and use of ontologies and knowledge representation languages in information systems (IS) research and application. As ontologies are intended to be conceptualizations of a perceived reality, they should reflect the empirically observed reality. This motivates proposing psychology of language as a reference discipline for knowledge engineering and knowledge management. Natural language is argued to reflect the cognitive concepts we use to think about and perceive the world around us. These cognitive concepts are the relevant terms with which to structure and represent knowledge about the world.Psychology of language can provide empirical justification for a particular set of concepts to represent knowledge. This paper draws on psycho‐linguistic research to develop a proposal for a system of cognitive structures. This is argued to provide the relevant concepts on which to found knowledge representation schemata for knowledge engineering, knowledge management and conceptual modelling.

A content-search information retrieval process based on conceptual graphs

An intelligent information retrieval system is presented in this paper. In our approach, which complies with the logical view of information retrieval, queries, document contents and other knowledge are represented by expressions in a knowledge representation language based on the conceptual graphs introduced by Sowa. In order to take the intrinsic vagueness of information retrieval into account, i.e. to search documents imprecisely and incompletely represented in order to answer a vague query, different kinds of probabilistic logic are often used. The search process described in this paper uses graph transformations instead of probabilistic notions. This paper is focused on the content-based retrieval process, and the cognitive facet of information retrieval is not directly addressed. However, our approach, involving the use of a knowledge representation language for representing data and a search process based on a combinatorial implementation of van Rijsbergen’s logical uncertainty principle, also allows the representation of retrieval situations. Hence, we believe that it could be implemented at the core of an operational information retrieval system. Two applications, one dealing with academic libraries and the other concerning audiovisual documents, are briefly presented.

A knowledge based approach for representing and reasoning about signaling networks.

In this paper we propose to use recent developments in knowledge representation languages and reasoning methodologies for representing and reasoning about signaling networks. Our approach is different from most other qualitative systems biology approaches in that it is based on reasoning (or inferencing) rather than simulation. Some of the advantages of our approach are, we can use recent advances in reasoning with incomplete and partial information to deal with gaps in signal network knowledge; and can perform various kinds of reasoning such as planning, hypothetical reasoning and explaining observations. Using our approach we have developed the system BioSigNet-RR for representation and reasoning about signaling networks. We use a NFkappaB related signaling pathway to illustrate the kinds of reasoning and representation that our system can currently do. The system is available on the Web at http://www.public.asu.edu/~cbaral/biosignet

Integrating knowledge representation and reasoning in Geographical Information Systems

We propose a formalism and a programming environment in which sophisticated spatio-temporal reasoning can be performed, while keeping the capabilities of manipulating and presenting large amounts of geographical data, typical of commercial Geographical Information Systems (GISs). The spatio-temporal knowledge representation language, named MuTACLP+, is based on constraint logic programming and is integrated via a middleware of commands and translation features with a commercial GIS. The paper presents the language, the architecture of the environment, and a few examples of its use in the field of event planning.

Frameworks for incorporating semantic relationships into object‐oriented database systems

AbstractA semantic relationship is a data modeling construct that connects a pair of classes or categories and has inherent constraints and other functionalities that precisely reflect the characteristics of the specific relationship in an application domain. Examples of semantic relationships include part–whole, ownership, materialization and role‐of. Such relationships are important in the construction of information models for advanced applications, whether one is employing traditional data‐modeling techniques, knowledge‐representation languages or object‐oriented modeling methodologies. This paper focuses on the issue of providing built‐in support for such constructs in the context of object‐oriented database (OODB) systems. Most of the popular object‐oriented modeling approaches include some semantic relationships in their repertoire of data‐modeling primitives. However, commercial OODB systems, which are frequently used as implementation vehicles, tend not to do the same. We will present two frameworks by which a semantic relationship can be incorporated into an existing OODB system. The first only requires that the OODB system support manifest type with respect to its instances. The second assumes that the OODB system has a special kind of metaclass facility. The two frameworks are compared and contrasted. In order to ground our work in existing systems, we show the addition of a part–whole semantic relationship both to the ONTOS DB/Explorer OODB system and the VODAK Model Language. Copyright © 2003 John Wiley & Sons, Ltd.

Reasoning about uniqueness constraints in object relational databases

Uniqueness constraints such as keys and functional dependencies in the relational model are a core concept in information systems technology. We consider uniqueness constraints suitable for object relational data models and identify a boundary between tractable and intractable varieties. The subclass that is tractable is still a strict generalization of both keys and relational functional dependencies. We present an efficient decision procedure for the logical implication problem of this subclass. The problem itself is formulated as an implication problem for a simple dialect of description logic (DL). DLs are a family of languages for knowledge representation that have many applications in information systems technology and for which model building procedures have been developed that can decide implication problems for dialects that are very expressive. Our own procedure complements this approach and can be integrated with these earlier procedures. Finally, to motivate our results, we review some applications of our procedure in query optimization.