knowledge representation and reasoning pdf

Knowledge Representation And Reasoning Pdf

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knowledge representation and reasoning

Show all documents Comparative analysis of knowledge representation and reasoning requirements across a range of life sciences textbooks Background: Using knowledge representation for biomedical projects is now commonplace.

In previous work, we represented the knowledge found in a college-level biology textbook in a fashion useful for answering questions. We showed that embedding the knowledge representation and question-answering abilities in an electronic textbook helped to engage student interest and improve learning.

To answer that question, we considered four different textbooks, ranging from a below-introductory college biology text to an advanced, graduate-level neuroscience textbook.

For these textbooks, we investigated the following questions: 1 To what extent is knowledge shared between the different textbooks? Results: Our existing modeling and reasoning methods apply especially well both to a textbook that is comparable in level to the text studied in our previous work i.

Even for the overlapping knowledge found across the textbooks, the level of detail covered in each textbook was different, which requires that the representations must be customized for each textbook. We also found that for advanced textbooks, representing models and scientific reasoning processes was particularly important. A Knowledge Representation and Reasoning System for Multimodal Neuroimaging Studies A soft computing approach to model the universe of discourse based on a CBR methodology for problem solving is now set.

Contrasting with other problem solving methodologies e. Undeniably, in almost all the situations the work is performed at query time. The main difference between this new approach and the typical CBR one relies on the fact that not only all the cases have their arguments set in the interval [0, 1], but it also caters for the handling of incomplete, unknown, or even self-contradictory data or knowledge. Thus, the classic CBR cycle was changed Fig.

Ontology learning as a use-case for neural-symbolic integration However, while symbolic knowledge representation is highly recursive and well understood from a declarative point of view, neural networks encode knowledge implicitly in their weights as a result of learning and generalisation from raw data which is usually characterized by simple feature vectors.

While significant theoretical progress has recently been made on knowledge representation and reasoning using neural net- works on the one side and direct processing of symbolic and structured data with neural methods on the other side, the in- tegration of neural computation and expressive logics such as first order logic is still in its early stages of methodolog- ical development.

As for knowledge extraction, neural net- works have been applied to a variety of real-world problems e. In order to advance the state of the art, we believe that it is necessary to look at the biological inspira- tion for neural-symbolic integration, to use more formal ap- proaches for translating between the connectionist and sym- bolic paradigms, and to pay more attention to potential appli- cation scenarios.

We will argue in the following that ontology learning provides such an application scenario with potential for success and high impact. However, the model relies on iterative successive backward and forward forecasting of the missing values. Ajlan has presented a comparative study of forward and backward reasoning methods for academic field. The knowledge base consists of 12 rules, and experimental results showed that forward reasoning is a better strategy than backward reasoning in terms of deriving goals.

Stephen has presented an artificial intelligence approach to investing in corporate bankruptcy. He designed expert system prototype for corporate bankruptcy analysis.

His system consists of various production rules based on indebtedness ratios i. The exsys corvid R tool is used to draw expert system prototype. The experimental results showed lower training time, prescription to refine performance. Kamley et al. Their expert system used to draw with twelve most promising rules of the stock market and rules contains of open price, close price, high price, low price, earning per share, dividend, interest rates, oil prices and the most important US dollar prices of shares.

Their studies consist of several fundamental, macroeconomic and technical factors. The expert system consists of 50 production rules. Finally, experimental results showed that backward reasoning strategy has performed better over forward reasoning strategy.

In this study, forward reasoning approach is considered for the stock market expert system design and development. Online Full Text Historically, uncertain reasoning has been associated with Probability Theory [8] but promising research have been done using other formalisms linking logic with probability theory. These formalisms include the theory of fuzzy sets [9], multi-valued logics [10], the Dempster-Shafer theory of evidence [11], hybrid i.

The Abductive Logic Programming ALP [17, 12, 4] is a promising computational paradigm and has been recognized as a way to solve some limitations of logic programming with respect to higher level knowledge representation and reasoning tasks. Abduction is a way of reasoning on incomplete or uncertain knowledge , in the form of hypothetical reasoning , more appropriate to model generation and satisfiability checking.

Pereira et al. Pharmacogenomic knowledge representation, reasoning and genome-based clinical decision support based on OWL 2 DL ontologies OWL reasoning helped identify some CDS rules with overlapping target populations but differing treatment recommendations. For example, the reasoner highlighted an overlap in the patient populations targeted by two treatment recommendations for azathioprine issued by CPIC and the Dutch Pharmacogenomics Working Group, which made one patient population a subset of the other patient population Table 2.

This is not an error in the data—such discrepancies between guidelines from different groups are to be expected. In total, 57 out of the phenotype of decision support rules in the ontology were targeting equivalent patient popula- tions or patient populations that were subgroups of other patient populations i. However, finding adequate ways to communicate CSR linked sponsorship is challenging. This research examines the relative effectiveness of three message sources from which CSR-linked sponsorship information can be communicated to consumers: the sponsor, the sponsored property, and the news media.

The results of an experimental study show that CSR-linked sponsorship information from both the sponsor and the sponsored property result in higher persuasion knowledge activation than when this information comes from the news media.

However, the small sample size which reduced statistical power. This limitation offers another potential explanation for the failure to provide full support for the first hypothesis that complements the conceptual arguments presented above. Variational Knowledge Graph Reasoning Variational Auto-Encoder Kingma and Welling, is a very popular algorithm to perform ap- proximate posterior inference in large-scale sce- narios, especially in neural networks.

Recently, VAE has been successfully applied to various complex machine learning tasks like image gen- eration Mansimov et al. Zhang et al. In contrast, our model uses a variational framework to cope with the complex link connections in large KG.

Un- like the previous research in VAE, both Zhang et al. More specifically, we view the generation of relation as a stochastic process controlled by a latent representation , i. Though the potential link paths are discrete and countable, its amount is still very large and poses challenges to direct optimization.

Therefore, we resort to variational auto-encoder as our approxi- mation strategy. The trustworthiness of knowledge -base statements have generally been ac- cepted without any proof [50]. Van Harmelen argues that some measurement of trust will be needed when dealing with distributed knowledge sources.

In [32] the authors highlight some of the issues that need to be incorporated into the SW service architec- ture to enable exchanging trust and reputation and to control policies and negotiations. There are several issues to consider with respect to trust and the SW. For example, how can trust be modelled and exchanged between agents and SW services?

Where should trust annotations be stored and made available? What kind of knowledge is required to measure trust and where will this knowledge come from? What trust features need to be considered e.

This was based on the assumption that the similarity based retrieval approach provided by the use of CBR would allow to capture and counter most of the vagueness still associated with the selection of the optimal process in the hydrometallurgical treatment of refractory ores domain.

For example, it was possible to model into the similarity measures such facts as that the ore does not need any more treatment if it contains gold grains greater than 15 micro meters in diameter. Such facts are easy to integrate into the similarity measure and thus are operational having an effect in the knowledge model. Hence, there is need to establish as whether moral knowledge is the source of moral reasoning among students.

This knowledge is organized in a special hierarchical structure that permits a diagnosis of knowledge independence. Frames are basically an application of object- oriented programming for artificial intelligence and Expert System. Frames provide a concise structural representation of knowledge in a natural manner. The knowledge in a frame is partitioned into slots.

A slot can describe declarative knowledge e. A frame includes two basic elements: slots and facets. A slot is a set of attributes that describe the object represented by the frame.

Each slot contains one or more facets. The facets subslots describe some knowledge or procedural information about the attribute in the slot. Most artificial intelligence systems use a collection of frames linked together in a certain manner to show their relationship.

This is called a hierarchy of frames. The hierarchical arrangement of frames permits inheritance frames. A script is a structured representation describing a stereotyped sequence of events in a particular context [5]. This sequence can be said to be script knowledge in the situation of 'eating at a restaurant'[6]. Medical Event Representation and Reasoning for Chinese Clinical Guideline Relevant conceptions and definitions about medical event are introduced in the second section of this paper.

In the third section, we propose a framework to represent event knowledge in clinical guideline. In forth section, we choose a segment of antimicrobial drugs guideline as the object to be handled, representing and reasoning events of it.

At last, the advantages and disadvantages of the framework are discussed and we put forward challenges about further work. Semantics-based plausible reasoning to extend the knowledge coverage of medical knowledge bases for improved clinical decision support Pseudocode 1 shows the implementation of analogical reasoning in our knowledge - based system.

As mentioned, this process is driven by failed justification premises. In this case, the process starts by looking for plausible rules that can resolve a missing premise line 1. At this step, the concept hierarchy is leveraged to expand the search space. In this example, the direct superclass of the missing premise firmLiver i.

The knowledge base is then searched for facts that unify the rule; i. For each found fact, the algorithm checks whether its instantiated knowledge -transfer condition matches the original entity line 3.

If the expert confirms the overall justifi- cation, this working memory will be materialized in the knowledge base see section Implementation of SeMS-KBS. Aspects of knowledge representation Basden. Aspects of Knowledge Representation 35 meaningful by reference to the spatial aspect, and linguistic pragmatics by reference to the social. Since some of the later aspects have yet to be opened fully, it is likely that there are things in earlier aspects whose full significance is yet to be recognised.

This means that when we implement a KRF module for any aspect possibly excepting the pistic , we should make it possible to extend its things, procedures, constraints, inferences, etc.

This is, of course, standard practice in reusable software today, but an awareness of aspects might be a useful guide to this practice. Reasoning with Sets to Solve Simple Word Problems Automatically While it has been presented that Magi is a good knowledge -based system, the question remains whether it is robust enough to have a recall comparable with empirical systems.

Knowledge Representation and Reasoning

This is a revised submission after an "accept pending minor revisions", which has now been accepted for publication. The reviews below are for the originally submitted version. The paper describes a very important and influential reasoner system, RacerPro, and thus is of interest for the journal. I am, however, not so sure about the suitability of the style: in particular, I think that, by 1 concentrating on Description Logics and mentioning OWL aspects only in passing and in an incomplete way , 2 adding a lot of historic information on design choices, and 3 discussion what seems like a plethora of features without distinguishing between 'core' and 'also available'; the authors have made the paper rather inaccessible to the semantic web community, and thus to the readers of this journal. Also, shouldn't Hermit be mentioned there? Also, regarding instance retrieval optimisation techniques, SHER and Requiem should probably be mentioned. I found the use cases slightly disappointing: they are mostly toy or own use cases, and sketched at such a high level of abstraction that it is hard to gain insights

Graph Based Knowledge Representation and Reasoning: Practical AI Applications

Knowledge representation is at the very core of a radical idea for understanding intelligence. Instead of trying to understand or build brains from the bottom up, its goal is to understand and build intelligent behavior from the top down, putting the focus on what an agent needs to know in order to behave intelligently, how this knowledge can be represented symbolically, and how automated reasoning procedures can make this knowledge available as needed. This landmark text takes the central concepts of knowledge representation developed over the last 50 years and illustrates them in a lucid and compelling way.

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Table of Contents

Skip to search form Skip to main content You are currently offline. Some features of the site may not work correctly. Trentelman Published Computer Science. Abstract : As part of the information fusion task we wish to automatically fuse information derived from the text extraction process with data from a structured knowledge base. This process will involve resolving, aggregating, integrating and abstracting information - via the methodologies of Knowledge Representation and Reasoning - into a single comprehensive description of an individual or event. Save to Library.

September , Details PDF. Reasoning with Contextual Knowledge and Influence Diagrams. Michael E. Mario Alviano. Francesco Belardinelli Vadim Malvone. Ordinal Polymatrix Games with Incomplete Information.

Foundations of Artificial Intelligence Knowledge Representation and Reasoning

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Show all documents A desiderata for modeling and reasoning with social knowledge Unfortunately, research in Knowledge Representation and Reasoning for- malisms has lagged behind this rapid evolution in how data is created and dissem- inated. The goal of this position paper is thus to explore a desiderata—a list of desirable characteristics—for the development of what we will call social knowl- edge bases SKBs, for short.

То есть к понедельнику, с самого утра.  - Она бросила пачку компьютерных распечаток ему на стол. - Я что, бухгалтер.

 У него есть охрана. - В общем-то. - Он прячется в укрытии. Стратмор пожал плечами.

2. Knowledge Representation and Reasoning.pdf

 Ты уверен, что его никто не купил. - Да вы все спятили.

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