Haut PDF Declarative modeling based on knowledge

Declarative modeling based on knowledge

Declarative modeling based on knowledge

This paper proposes, formalizes and implements a method creating VWs, using simple user inputs in the form of descriptions, with a formalization close to natural language, and exploiting[r]

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A knowledge-based perspective for software process modeling

A knowledge-based perspective for software process modeling

1. Introduction Software engineering is knowledge-intensive [1], and so the acquisition and sharing of knowledge form the conceptual backbone of software process modeling. However, the latest version of SPEM [2], the Object Management Group (OMG)’s de facto standard devoted to software process mod- eling, does not support this knowledge concern. We tackle the issue in this paper by presenting an extended SPEM framework which focuses on the knowledge-oriented perspective of process modeling. Our approach responds to the need to provide process engineers with the means to perform knowledge assessments within processes, and we do this by integrating knowledge con- cepts within SPEM-based software process mod- eling. This knowledge-oriented perspective has been integrated into our previous work related to the implementation of a domain-specific lan- guage for software process modeling (DSL4SPM tool) [3].
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Registration & Modeling of Shapes with Uncertainties: Contributions and Applications to Knowledge Based Segmentation

Registration & Modeling of Shapes with Uncertainties: Contributions and Applications to Knowledge Based Segmentation

Shape representation and modeling of its variations inter and intra-class is considered to be a universal problem with applications to knowledge-based segmentation (extraction from images of a particular structure), tracking (recovering successive positions and deformations of a structure in a number of consecutive images), recognition (classification decision for the presence or not of a class of objects in an observed image), etc. Extraction of information regarding the state of an organ from biomedical images is a challenging task. One has to deal first with content interpretation from sparse local signals and then with the fusion problem. Images, volumes, 4D volumes etc. correspond to sampled continuous functions where measurements are only correlated at a local scale, therefore recovering content often consists of describing the measurements using a simple mathematical model and then estimating the parameters of this model using the measurements. While such a process seems very trivial, given the complexity of biological systems the selection of a model which is a compromise between complexity and tractability is not straightforward. On one hand, one would like a model that is capable of explaining the state of the organ; on the other hand, one should be able to determine the parameters of this model from sparse signals. Let us consider a ”simple” example: the heart cycle and in particular the cycle of the left ventricle which pumps oxygenated blood to all human tissues (including the most distant one). The cardiac cycle contains two important phases alternating contraction and dilatation. End-systole and end-diastole are the instants where the ventricle has its lowest and its highest volume. The relative volumetric difference between these instants (called ejection fraction) is a reliable indicator about the heart function. In such a context the task of content extraction refers to the recovery of the volume of the left ventricle.
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From Declarative Knowledge to Process-based Crisis Resolution: application to Flood Management

From Declarative Knowledge to Process-based Crisis Resolution: application to Flood Management

The approach recommended in this paper is in line with the one described in [6]. As in [6], we advocate a knowledge-based deduction considering both facts observed in the field and business knowledge of crisis actors. More precisely business knowledge corresponds to already existing solution modelled as services offered by crisis actors and possible corresponding plans defining coordination between them. In addition, we also model relations of these services in terms of what do they require, what do they cause, why using one or another. These relations correspond to knowledge of crisis actors indicating how services must be performed in the field. Unlike [6], we do not model this knowledge as a set of hard- coded rules in an ontology but rather as data stored in a database. Thus our approach is declarative, making knowledge management easier. Moreover, we fully exploit this knowledge as we deduce choices, in addition to sequencing and parallelization of services. On the other hand, we are able to explain why services participate in the CRP as they deal with risk or damage, or as they are dependent from other participating services.
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Knowledge-based configuration : a contribution to generic modeling, evaluation and evolutionary optimization

Knowledge-based configuration : a contribution to generic modeling, evaluation and evolutionary optimization

Mots Clés : Évaluation, Modèle générique, Configuration de produit, Configuration de processus, Configuration concurrente, Optimisation évolutionnaire Abstract Knowledge-Based Configurat[r]

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Prior knowledge contribution to declarative learning.
A study in amnesia, aging and Alzheimer's disease

Prior knowledge contribution to declarative learning. A study in amnesia, aging and Alzheimer's disease

64 I.8.2.2. Conjunctive theory This account, largely based on animal research with rats, shows some similarity with the relational theory: in both cases, the hippocampus is supposed to support associative processes that underlie the formation of representations composed of distinct elements (O’Reilly & Rudy, 2001; Rudy & O’Reilly, 1999). However, these representations called “conjunctive” in that model include both the relationships between individual elements of an experience, and the unique individual features of the event that differ from the individual elements (e.g. in the negative patterning problem, the representation “AB” must be learned as a unique entity, that is, distinct from “A” and “B” presented individually). Put this way, individual elements and its relationships within a unique conjunctive representation, once stored, cannot be retrieved independently, which is a key difference with the relational theory. To some extent, these conjunctive representations are close to the perceptual “blend” concept in the relational theory. However, multiple conjunctive representations would be formed from different vantage points but for a single event, as well as individual elements representations. The process of pattern completion, uniquely associated with hippocampal computations, is thought to allowing incoming representations to be compared with stored representations. Pattern completion can achieve memory retrieval this way, even based on a partial cue. This account therefore keeps the idea of flexibility, which here emanates not from the relational encoding itself but from the pattern completion process that allows retrieval of the ad hoc conjunctive representation for the current behavioural goal (O’Reilly & Rudy, 2001). Finally, an important aspect of the conjunctive theory that differs from the relational theory is that hippocampal computations are supposed to allow the rapid encoding of conjunctive representations. However, the surrounding cortices also can support conjunctive representations encoding, but through extensive repetitions during a slow learning process. A strong prediction therefore is that damage to the hippocampus should prevent from rapid learning of associations: hippocampal amnesia should not obliterate associative learning, but it could take place only at a slow pace, incidentally, and in the service of specific situations like problem-solving tasks.
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Knowledge-based Sequence Mining with ASP

Knowledge-based Sequence Mining with ASP

model sequential patterns with non-contiguous items by wild- cards. In such a case, detecting embeddings is closer to string matching than subsequence finding. Unlike that, our encod- ings leave particular embeddings of a pattern implicit and fo- cus on sequences for which some embedding exists. More generally, SAT lacks a modeling language, which makes it hard for any SAT-based approach to achieve a similar level of extensibility. The CPSM system [Negrevergne and Guns, 2015] pursues a CP-based approach and tackles sequential pattern mining tasks similar to ours. While we rely on fully declarative encodings, CPSM uses dedicated propagators for embedding computation. Although such propagators can be implemented by space-efficient special-purpose algorithms, they cannot easily be extended to accommodate further con- straints. On the other hand, it will be interesting future work to equip ASP systems with similar constructs and to find a trade-off between declarativeness and effectiveness.
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Experience modeling with graphs encoded knowledge for construction industry

Experience modeling with graphs encoded knowledge for construction industry

6. Conclusion and related works The rationale for this study stemmed from the fact that knowledge is an important asset of an organization including AEC companies; many AEC companies are yet to tap on the full potential of knowledge from already executed projects. Companies are aware of situations in their organizations in which costly errors have been made because knowledge was not available when and where it was needed and because employees did not know how to interpret or use the information available to them. These situations are mainly due to the project-based nature of the construction industry and the fact that knowledge is embedded in social relations. Nevertheless, for those big companies who might be carrying out international projects, strategies to mobilize knowledge is critically important. In construction organizations, time is often associated with the need to deliver projects according to schedule. Many construction organizations believe that their organizational structure requires heavy procedures and long processing times to exploit domain knowledge. Employees may be willing to share knowledge, but the pressure to deliver projects under tight schedule and budget makes them have very low appetite to take on additional responsibility for knowledge management activi- ties. In this manuscript, it was revealed that useful knowledge on AEC projects and businesses or organizations can be helpful in resolving persistent, complex and workload problems. Eliciting experience feedback knowledge is core to re-using existing knowledge that can improve organizational efficiency in product (e.g., buildings) delivery. This is enshrined in the methodology for knowledge capitalization from semi-structured information and unstructured information in industrial engineering domain [10,11] . Graphical conceptual modeling is very powerful as it allows lessons learned to be captured and represented textually and graphically, facilitating visualization. This improves communication to users who may be interested in consuming knowledge about feedback from previous projects
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Introducing knowledge concepts in software process
modeling

Introducing knowledge concepts in software process modeling

Despite these different interests and perspectives, it follows that the integration of knowledge component can improves the efficiency of the software processes as well as their quality. However, the latest specification of Software & Systems Process Engineering Meta-model (SPEM 2.0) [6], the OMG‟s “de facto” standard devoted to software process modeling, does not supports this concern. It focuses on a structural view and does not define support for such behavior modeling. That‟s why there is a need to extend this Meta-model to support a knowledge-oriented modeling perspective on the base of activity-oriented one. A typical problem faced by project managers when starting a software project, either new or maintenance, relates to the question: Do we have necessary knowledge to complete the project? Data is required to support an informed decision: For all interrelated activities, which are the unit of work of a given process, is it possible to measure the knowledge required to carry out each task and to map this data to knowledge provided by Roles (primary and additional) as well as input artifacts ? Hence, there is a need for a dashboard that would helps to develop indices of knowledge discrepancies. So we propose a formalism that is based on: 1) the SPEM standard, which is used for building the syntactic structure, and so providing a standardized static structural view; and 2) an extension based on the relationships between components of that structural view, which is used to formalize the semantic relationships between SPEM elements, and so supporting a conceptual view of Knowledge. This formal approach allows process designers to create, as well as to represent, analyze and validate a Knowledge view of process model.
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A Declarative Approach to Modeling and Solving the View Selection Problem

A Declarative Approach to Modeling and Solving the View Selection Problem

Our fourth contribution aims at extending the constraint satisfaction model, which we have designed to address the view selection problem in a centralized con- text, in order to capture the distributed features. As mentioned before, the view selection problem becomes more challenging in a distributed environment. Indeed, the resource constraints that we have considered in a centralized context i.e., storage space constraint will be per machine (computer node) in a distributed scenario. The view selection will additionally constrained by maximum global maintenance costs. Furthermore, resource constraints such as network bandwidth and the location of materialized views will have to be taken into consideration. To the best of our knowledge, no past work has addressed the view selection problem under all these resource constraints. Our constraint programming based approach lls this gap. In- deed, all these resource constraints have easily been modeled with the rich constraint programming language. Experiment results have shown that our approach provides high performance resulting from evaluating the quality of the solutions found by our approach in terms of cost saving.
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Program Supervision: from Knowledge Modeling to Dedicated Engines

Program Supervision: from Knowledge Modeling to Dedicated Engines

On the contrary to knowledge-based systems for image interpretation which have been studied for about 15 years, knowledge-based systems for image process- ing program supervision are recent. In Japan, a lot of teams, belonging both to the research and to the industrial sectors, have devoted an important effort on this problem. [39], [31], (see the review made by Matsuyama in 89 [26] ). In the United States early work has been done by Johnston [22] and by Bailey [1]. In Europe work has been developed either as research general tools (see OCAPI [10] and [11]) or as industrial tools for a particular application (see the VIDIMUS Esprit project [6] and [3]). In the VIDIMUS project the aim was to develop a vision system environment for industrial inspection applications. The result was a knowledge-based system (VSDE) that can be used to first specify an inspection problem, and to automati- cally configure a vision based system to solve the problem.
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Local knowledge in ecological modeling

Local knowledge in ecological modeling

Insight Local knowledge in ecological modeling Annie Claude Bélisle 1 , Hugo Asselin 1 , Patrice LeBlanc 1 and Sylvie Gauthier 2 ABSTRACT. Local people and scientists both hold ecological knowledge, respectively stemming from prolonged day-to-day contact with the environment and from systematic inquiry based on the scientific method. As the complementarity between scientific ecological knowledge (SEK) and local ecological knowledge (LEK) is increasingly acknowledged, LEK is starting to be involved in all branches of ecology, including ecological modeling. However, the integration of both knowledge types into ecological models raises methodological challenges, among which (1) consistency between the degree of LEK involvement and modeling objectives, (2) combination of concepts and methods from natural and social sciences, (3) reliability of the data collection process, and (4) model accuracy. We analyzed how 23 published studies dealt with those issues. We observed LEK reaches its full potential when involved at all steps of the research process. The validity of a modeling exercise is enhanced by an interdisciplinary approach and is jeopardized when LEK elicitation lacks rigor. Bayesian networks and fuzzy rule-based models are well suited to include LEK.
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Expressing Constraint Satisfaction Problems in Declarative Modeling Using Natural Language and Fuzzy Sets

Expressing Constraint Satisfaction Problems in Declarative Modeling Using Natural Language and Fuzzy Sets

In this paper, properties are de"ned with vague or imprecise linguistic terms expressed in pseudo-natural language sentences. Therefore, we propose a new model using basic properties modi"ed by generic operators and deals with imprecise or vague properties based on cur- rent formalizations [7,16,25] and Zadeh's fuzzy sets [26]. This model allows us to improve the man}computer dialogue using qualitative and quantitative properties formulated with a more natural language. It also allows us to manage negative properties using linguistic studies instead of the logical negation usually used in Fuzzy Logic and Declarative Modeling. This process will try to "nd an implicit a$rmative property equivalent to the negative one.
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Knowledge modeling for eco-design

Knowledge modeling for eco-design

2.2 Support to the Design Activity in a Sustainable Development Context Eco-design is the main target of numerous studies dealing with taking into account environmental con- straints in design. In the literature, this concept, also known as design for environment (DFE) (ISO 14062) suggests approaches and general design principles allowing to minimize, with similar performance, the environmental impact of a product during its entire life cycle. Among the suggested tools the eco-design strategy wheel [4] is one of the best means for analyzing the performance of a product in relation with environmental criteria associated to each step of its life cycle. This visual tool, based on a spider diagram, allows especially to compare a product to an ‘ideal’ version on the point of view of environmental criteria, in order to identify the weak points requiring high attention. A detailed synthesis of the eco-design tools can be found in [33].
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Automated Negotiation from Declarative Contract Descriptions

Automated Negotiation from Declarative Contract Descriptions

Our approach uses a form of logic-based knowledge repre- sentation to represent contracts and extends this language to express and reason about partial contracts. The partial contract, or contract template, describes possible negotiable parameters and how they are interrelated, along with meta- level rules about the negotiation and about individual auc- tions. It combines all this with rules from agents about their constraints and preferences over the possible negotia- tion structures. From implications of the rules, it generates the appropriate auctions and determines the auction param- eters. Transactions in the auctions generate additional rules, which produce results for the nal contract. As part of this new framework, our approach allows for reuse of the infor- mation in multiple stages of the contracting process. 2.1 Contracting Language
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Using knowledge engineering for Modeling Mobile Learning Systems

Using knowledge engineering for Modeling Mobile Learning Systems

On the other side, one of the drawbacks of m-learning is the mobility feature itself because it results in potential loss in concentration and learning disciple due to surrounding noise and interruptions. Consequently, subject material should be set to be presented in small and concentrated pieces of information. It is highly recommended for the subject material to be interactive and good appealing to attract the learner's attention. It is also preferred to be displayed in wizard-based screens (successive guiding screens) and to apply graphical representations whenever possible. Moreover, the concentrated graphics based design and small wizard-based screens assist in reducing the negative impact of one of the most discouraging m-learning limitations i.e. small display screens. It is also recommended to reduce text usage whenever possible. If used, text material should be short, clear, and concentrated.
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Knowledge-based modelling applied to synucleinopathies

Knowledge-based modelling applied to synucleinopathies

graphs operations are used in the visual reasoning, semantic comparisons are deployed at every step, and the only difference between deduction and analogies is the nature of the orientations on the reasoning [21] . Particularly, one conceptual graph is similar to another if there is a semantic mapping (graph homomorphism) from the first graph to the second one. In this context, the case- based reasoning can be engaged to search some unknown features of a new case from its known features and previous cases stored in the cases base. In analogical reasoning, the conceptual structure that describes known features of the new case is compared with the matching features of the conceptual structures associated to
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XML-based knowledge management for DFM

XML-based knowledge management for DFM

4.1 Information structure using XML As previously explained in part 2, the capitalisation has to be done knowing the future uses of the information. As introduced in [19], it is like defining an ontological context described with Key Characteristics (KCs). This ontology is in our work related to manufacturing specifications. Those KCs are defined using DTDs (Document Type Definition). The most important work has been to well define every markup according to the knowledge management (cf. Part 5). Afterwards, the XML file that contains the information can be created. Figure 6 presents the methodology to create files using XML.
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Strategies to Reduce Knowledge Leakage: A Knowledge Absorptive Capacity-Based Framework

Strategies to Reduce Knowledge Leakage: A Knowledge Absorptive Capacity-Based Framework

2.2 Relationship between knowledge sharing and security in the organizational context Within organizations, IKS may be hacked as well as employees themselves (for tacit knowledge). Hackers may target employees by convincing them to make their knowledge explicit and eventually share it without their acknowledgement. According to Willison and Warkentin (2013), employee violations of the information security policies can be categorized as: (1) non-intentional, i.e. mistakes committed by careless or inexperienced employees; (2) intentional but not malicious, i.e. deliberate actions performed by employees obtaining a personal benefit with no intention to harm; and (3) intentional and malicious. Several authors have already addressed how non-malicious violations of information security policies may be avoided (Dhillon et al., 2017; Pfleeger and Pfleeger, 2002). In the same vein, but from a different perspective, we suggest that skill development through absorptive capacity (Ortiz et al., 2017; Todorova and Durisin, 2007) may be a way of mitigating non-malicious information security policy violations.
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Investigating Argument Relatedness Based on Linguistic Knowledge

Investigating Argument Relatedness Based on Linguistic Knowledge

Abstract—In this contribution, we explore the type of linguistic knowledge that is required to establish relatedness between a claim and a justification which may be distant or in different texts, within the framework of argument mining. We propose an original annotation method based on XML-Frames and a linguistic analysis of the main resources which are needed to establish relatedness on a linguistic basis.

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