Knowledge-based picture understanding of weather charts

This paper describes a knowledge-based weather chart understanding system named WERP, which is working as a picture processing part of our Information understanding System Of BAsic weather Report (ISOBAR). WERP is designed to extract necessary information from a weather chart for generating weather report sentences explaining the chart. This system is based on a structural model of the weather charts.Here, we study what problems are involved in weather chart understanding, how they are solved and how an actual system is organized. Also, picture-processing techniques for weather chart processing and some experimental studies are considered.     

Knowledge-based vector space model for text clustering

This paper presents a new knowledge-based vector space model (VSM) for text clustering. In the new model, semantic relationships between terms (e.g., words or concepts) are included in representing text documents as a set of vectors. The idea is to calculate the dissimilarity between two documents more effectively so that text clustering results can be enhanced. In this paper, the semantic relationship between two terms is defined by the similarity of the two terms. Such similarity is used to re-weight term frequency in the VSM. We consider and study two different similarity measures for computing the semantic relationship between two terms based on two different approaches. The first approach is based on the existing ontologies like WordNet and MeSH. We define a new similarity measure that combines the edge-counting technique, the average distance and the position weighting method to compute the similarity of two terms from an ontology hierarchy. The second approach is to make use of text corpora to construct the relationships between terms and then calculate their semantic similarities. Three clustering algorithms, bisecting k-means, feature weighting k-means and a hierarchical clustering algorithm, have been used to cluster real-world text data represented in the new knowledge-based VSM. The experimental results show that the clustering performance based on the new model was much better than that based on the traditional term-based VSM.     

Knowledge-based analysis and understanding of medical images

Knowledge-based image analysis and interpretation of radiological images is of significant interest for several reasons including a means to identify and label each part of the image for further automated diagnostic analysis. Also, there is a need to develop a knowledge-based biomedical image analysis system which can analyze and interpret the anatomical images (such as those obtained from X-ray computed tomography (CT) scanning) in order to help analysis of functional images (such as those obtained from positron emission tomography (PET) scanning) of the organ of the same patient. This paper deals with the design and implementation of a knowledge-based system to analyze and interpret CT anatomical images of the human chest. In the approach presented here, the emphasis has been on the development of a strong low-level analysis system with the capability of analyzing in both bottom-up and top-down modes; and on the use of hierarchical relational, spatial, and structural knowledge of human anatomy in the process of high-level analysis and recognition.     

一种文稿理解算法

提出一种模式识别方法，它能有效地用于有监督的连续语音识别、文稿理解和数据文件分析。在PⅡ350微机上的实际运行表明，无论是有监督的连续语音识别、还是文稿理解或数据文件分析，其结果都人脑的判别一致。     

Knowledge-based system for text classification using ID6NB algorithm

This paper presents a novel algorithm named ID6NB for extending decision tree induced by Quinlan’s non-incremental ID3 algorithm. The presented approach is aimed at suggesting the solutions for few unhandled exceptions of the Decision tree induction algorithms such as (i) the situation in which the majority voting makes incorrect decision (generating two different types of rules for same data), and (ii) in case of dimensionality reduction by decision tree induction algorithms, the determination of appropriate attribute at a node where two or more attributes have equal highest information gain. Exception due to majority voting is handled with the help of Naive Bayes algorithm and also novel solutions are given for dimensionality reduction. As a result, the classification accuracy has drastically improved. An extensive experimental evaluation on a number of real and synthetic databases shows that ID6NB is a state-of-the-art classification algorithm that outperforms well than other methods of decision tree learning.     

TextFlow: towards better understanding of evolving topics in text

Understanding how topics evolve in text data is an important and challenging task. Although much work has been devoted to topic analysis, the study of topic evolution has largely been limited to individual topics. In this paper, we introduce TextFlow, a seamless integration of visualization and topic mining techniques, for analyzing various evolution patterns that emerge from multiple topics. We first extend an existing analysis technique to extract three-level features: the topic evolution trend, the critical event, and the keyword correlation. Then a coherent visualization that consists of three new visual components is designed to convey complex relationships between them. Through interaction, the topic mining model and visualization can communicate with each other to help users refine the analysis result and gain insights into the data progressively. Finally, two case studies are conducted to demonstrate the effectiveness and usefulness of TextFlow in helping users understand the major topic evolution patterns in time-varying text data.     

Sense understanding of text conversation using temporal convolution neural network

Multimedia Tools and Applications - This paper proposes a model which uses Spatio Temporal features for real-time sense understanding of a text conversation. The proposed model uses CNN along with...     

Use of captions and other collateral text in understanding photographs

This research explores the interaction of textual and photographic information in image understanding. Specifically, it presents a computational model whereby textual captions are used as collateral information in the interpretation of the corresponding photographs. The final understanding of the picture and caption reflects a consolidation of the information obtained from each of the two sources and can thus be used in intelligent information retrieval tasks. The problem of building a general-purpose computer vision system withouta priori knowledge is very difficult at best. The concept of using collateral information in scene understanding has been explored in systems that use general scene context in the task of object identification. The work described here extends this notion by incorporating picture specific information. A multi-stage systemPICTION which uses captions to identify humans in an accompanying photograph is described. This provides a computationally less expensive alternative to traditional methods of face recognition. A key component of the system is the utilisation of spatial and characteristic constraints (derived from the caption) in labeling face candidates (generated by a face locator).This work was supported in part by ARPA Contract 93-F148900-000. I would like to thank William Rapaport for serving as my advisor in my doctoral work; Venu Govindaraju for his work on the face locator; and more recently, Rajiv Chopra, Debra Burhans and Toshio Morita for their work in the new implementation of PICTION as well as valuable feedback.     

Knowledge-based programs

Summary.  Reasoning about activities in a distributed computer system at the level of the knowledge of individuals and groups allows us to abstract away from many concrete details of the system we are considering. In this paper, we make use of two notions introduced in our recent book to facilitate designing and reasoning about systems in terms of knowledge. The first notion is that of a knowledge-based program. A knowledge-based program is a syntactic object: a program with tests for knowledge. The second notion is that of a context, which captures the setting in which a program is to be executed. In a given context, a standard program (one without tests for knowledge) is represented by (i.e., corresponds in a precise sense to) a unique system. A knowledge-based program, on the other hand, may be represented by no system, one system, or many systems. In this paper, we provide a sufficient condition for a knowledge-based program to be represented in a unique way in a given context. This condition applies to many cases of interest, and covers many of the knowledge-based programs considered in the literature. We also completely characterize the complexity of determining whether a given knowledge-based program has a unique representation, or any representation at all, in a given finite-state context. Received: October 1995 / Accepted: February 1997     

Knowledge-based organization evaluation

Knowledge has become the main value driver for modern organizations. In particular, knowledge-based organizations (KBOs) allocate resources to intangible assets (e.g., R&D) in the rapidly changing and highly competitive business environment in order to gain competitive advantages. Therefore, how to evaluate knowledge-based organizations has become one of the most important issues in knowledge management. The purpose of this paper is to provide a framework for the evaluation of KBOs under uncertainty, using the state-of-the-art methodology of Real Options. We define the unique features of KBOs and explain their value drivers. The present study's contribution is threefold: (1) it bridges the gaps in knowledge management literature related to evaluating knowledge capital; (2) it provides a systematic application of Real Options models in the context of knowledge-based organization evaluation; and, (3) it uses a real-world case to demonstrate the implications of the main findings for management.     

Knowledge-based part correspondence

This paper presents a direct method for finding corresponding pairs of parts between two shapes. Statistical knowledge about a large number of parts from many different objects is used to find a part correspondence between two previously unseen input shapes. No class membership information is required. The knowledge-based approach is shown to produce significantly better results than a classical metric distance approach. The potential role of part correspondence as a complement to geometric and structural comparisons is discussed.     

Knowledge-based information retrieval

A knowledge-based system is used as a front-end to a very large database to increase the relevance of the information being retrieved. The subject domain of the data base is modelled in a semantic network and the queries to the database are expanded according to the semantic model. An experiment has been performed on a bibliographic database, by developing the prototype KNOWIT, a knowledge-based front-end to the information retrieval system ESA-QUEST¹. An experimental evaluation shows that the number of relevant bibliographic references retrieved with the knowledge-based front-end is significantly improved, without compromising the precision of the retrieval.     

基于知识库的语言翻译系统研究

语言翻译是工作和学习中经常要做的事,传统的语言翻译是基于词法结构分析,然而自然语言并不是那么的规范,这导致了这种翻译方式存在本质上的缺陷,不管算法如何改进,翻译结果和人工翻译都会相差很远。本文以英汉翻译为例介绍了基于知识库的语言翻译,详细讲解了基于知识库翻译的原理,以及这种翻译方式与传统的基于词法结构分析的翻译方式相比具有的优越性,并实现了一个简单的基于知识库的翻译软件。     

Knowledge-based machine translation

This paper provides an overview of the KBMT-89 project at Carmegie Mellon University's Center for Machine Translation, as well therefore of the special number of this journal, which reports on the project. The knowledge-based approach to machine translation is presented and defended in a historical context. Various components of the system, key parts of which are described in subsequent papers of the issue, are introduced and paired with their computational motivations.     

Knowledge-based temporal interpolation

Temporal interpolation is the task of bridging gaps between time-oriented concepts in a context-sensitive manner. It is a subtask important for solving the temporal-abstraction task-abstraction of interval-based, higher-level concepts from time-stamped data. We present a knowledge-based approach to the temporal-interpolation task and discuss in detail the precise knowledge required by that approach, its theoretical foundations, and the implications of the approach. The temporal-interpolation computational mechanism we discuss relies, among other knowledge types, on a temporal-persistence model. The temporal-persistence model employs local temporal-persistence functions that are temporally bidirectional (i.e. extend a belief measure in a predicate both into the future and into the past) and global, maximal-gap temporal-persistence functions that bridge gaps between interval-based predicates. We investigate the quantitative and qualitative properties implied by both types of persistence functions. We have implemented our approach in the RÉSUMÉ program and evaluated it in several different medical and engineering domains. We discuss the implications of our conceptual and computational methodology for acquisition, maintenance, reuse, and sharing of temporal-abstraction knowledge.     

Knowledge-based program analysis

Automatic program analysis is regarded here as both the mechanized process of understanding high-level concepts from program text and the use of those concepts to guide program maintenance. A knowledge-based program analysis tool called PAT, which realizes this concept, is described. PAT uses an object-oriented framework to represent programming concepts and a heuristic-based concept-recognition mechanism to derive high-level functional concepts from the source code. As an example, a segment of a much larger program written in a Pascal-like language is analyzed using PAT