Innovative techniques for legal text retrieval

Legal text retrieval traditionally relies upon external knowledge sources such as thesauri and classification schemes, and an accurate indexing of the documents is often manually done. As a result not all legal documents can be effectively retrieved. However a number of current artificial intelligence techniques are promising for legal text retrieval. They sustain the acquisition of knowledge and the knowledge-rich processing of the content of document texts and information need, and of their matching. Currently, techniques for learning information needs, learning concept attributes of texts, information extraction, text classification and clustering, and text summarization need to be studied in legal text retrieval because of their potential for improving retrieval and decreasing the cost of manual indexing. The resulting query and text representations are semantically much richer than a set of key terms. Their use allows for more refined retrieval models in which some reasoning can be applied. This paper gives an overview of the state of the art of these innovativetechniques and their potential for legal text retrieval.     

An Analysis of Knowledge Acquisition Roles and Participants

Much recent research effort in the field of knowledge acquisition (KA) has focussed on extending knowledge acquisition techniques and processes to include a wider array of participants and knowledge sources in a variety of knowledge acquisition scenarios. As the domain of expert systems applications and research has expanded, techniques have been developed to acquire and incorporate knowledge from groups of experts and from various sources such as text, video, and audio tapes. However, the dominant participant-role model remains that of the knowledge engineer eliciting knowledge from one or more human experts. This conceptual gap has contributed to the major divisions in the KA field between researchers interested in manual KA and those developing tools for automated KA. This article considers the wide variety of possible KA scenarios and presents a meta-view of KA participants and the roles they may assume.We suggest that it is more appropriate to think of knowledge acquisition participants as playing one or more roles. These include knowledge sources, agents and targets for KA processes. We also present a participant model drawn from research in decision support systems that more accurately characterizes the diversity of the entities participating in the KA process. This view is more inclusive as it allows us to consider both human-human and human-computer KA interactions as well as the whole variety of knowledge sources and targets. A careful consideration of the meta-view and its associated role-participant mappings also yields the new ideas of the elemental and composite role and the multi-role entity. These new constructs are then used to identify areas where research is currently needed and to generate specific research issues. Taken altogether, this view allows a more flexible consideration of the many possible combinations that can and frequently do occur in actual KA situations.     

A new evolutionary algorithm combining simulated annealing and genetic programming for relevance feedback in fuzzy information retrieval systems

 Relevance feedback techniques have demonstrated to be a powerful means to improve the results obtained when a user submits a query to an information retrieval system as the world wide web search engines. These kinds of techniques modify the user original query taking into account the relevance judgements provided by him on the retrieved documents, making it more similar to those he judged as relevant. This way, the new generated query permits to get new relevant documents thus improving the retrieval process by increasing recall. However, although powerful relevance feedback techniques have been developed for the vector space information retrieval model and some of them have been translated to the classical Boolean model, there is a lack of these tools in more advanced and powerful information retrieval models such as the fuzzy one. In this contribution we introduce a relevance feedback process for extended Boolean (fuzzy) information retrieval systems based on a hybrid evolutionary algorithm combining simulated annealing and genetic programming components. The performance of the proposed technique will be compared with the only previous existing approach to perform this task, Kraft et al.'s method, showing how our proposal outperforms the latter in terms of accuracy and sometimes also in time consumption. Moreover, it will be showed how the adaptation of the retrieval threshold by the relevance feedback mechanism allows the system effectiveness to be increased.     

Fuzzy neural networks for intelligent design retrieval using associative manufacturing features

In the conceptual design stage, designers usually initiate a design concept through an association activity. The activity helps designers collect and retrieve reference information regarding a current design subject instead of starting from scratch. By modifying previous designs, designers can create a new design in a much shorter time. To computerize this process, this paper proposes an intelligent design retrieval system involving soft computing techniques for both feature and object association functions. A feature association method that utilizes fuzzy relation and fuzzy composition is developed to increase the searching spectrum. In the mean time, object association functions composed by a fuzzy neural network allow designers to control the similarity of retrieved designs. Our implementation result shows that the intelligent design retrieval system with two soft computing based association functions can retrieve target reference designs as expected.     

ACIRD: intelligent Internet document organization and retrieval

This paper presents an intelligent Internet information system, Automatic Classifier for the Internet Resource Discovery (ACIRD), which uses machine learning techniques to organize and retrieve Internet documents. ACIRD consists of a knowledge acquisition process, document classifier, and two-phase search engine. The knowledge acquisition process of ACIRD automatically learns classification knowledge from classified Internet documents. The document classifier applies learned classification knowledge to classify newly collected Internet documents into one or more classes. Experimental results indicate that ACIRD performs as well or better than human experts in both knowledge acquisition and document classification. By using the learned classification knowledge and the given class lattice, the ACIRD two-phase search engine responds to user queries with hierarchically structured navigable results (instead of a conventional flat ranked document list), which greatly aids users in locating information from numerous, diversified Internet documents     

Learning adaptation knowledge to improve case-based reasoning

Case-Based Reasoning systems retrieve and reuse solutions for previously solved problems that have been encountered and remembered as cases. In some domains, particularly where the problem solving is a classification task, the retrieved solution can be reused directly. But for design tasks it is common for the retrieved solution to be regarded as an initial solution that should be refined to reflect the differences between the new and retrieved problems. The acquisition of adaptation knowledge to achieve this refinement can be demanding, despite the fact that the knowledge source of stored cases captures a substantial part of the problem-solving expertise. This paper describes an introspective learning approach where the case knowledge itself provides a source from which training data for the adaptation task can be assembled. Different learning algorithms are explored and the effect of the learned adaptations is demonstrated for a demanding component-based pharmaceutical design task, tablet formulation. The evaluation highlights the incremental nature of adaptation as a further reasoning step after nearest-neighbour retrieval. A new property-based classification to adapt symbolic values is proposed, and an ensemble of these property-based adaptation classifiers has been particularly successful for the most difficult of the symbolic adaptation tasks in tablet formulation.     

基于实例的产品设计知识获取方法及实现

现代产品设计是面向市场、功能驱动以及基于新知识的设计，而知识获取是关键步骤。采用设计实例的形式对已有设计进行封装和组织，通过产品设计实例面向对象表达模型，将设计需求（包括性能需求、环境约束和成本约束）、设计任务、设计过程和设计方案封装在一个设计实例中，体现了功能驱动、面向市场的现代设计特征。在此基础上，利用Sybase公司的PowerDesigner8.0建模工具，进一步提出产品设计实例的获取模型，并对实例获取的关键技术进行了讨论。以滑动轴承设计为工程应用背景，选择应用于涡轮机的四油叶径向滑动轴承为设计实例，详细描述了集成于SolidWorks2000的滑动轴承设计实例获取模块的应用过程，验证了上述模型的正确性和可行性。通过对设计实例进行表达、获取和存储，为后续的基于实例推理的产品设计方法打下基础，同时以对外提供功能的形式对并行设计环境下的功能检索提供支持，推动现代产品概念设计自动化进程。     

The use of knowledge acquisition in instructional design

Expert systems and knowledge based systems have emerged from “esoteric” laboratory research in Artificial Intelligence (AI) to become an important tool for approaching real world problems. Expert systems are distinctive in that they are designed to address problems in a similar manner and with similar results as a human expert. The basic structure of an expert system is comprised of three functionally separate components: (a) knowledge base, which contains a representation of domain related facts; (b) means of knowledge base use to solve a problem, inference mechanism; and (c) working memory, which records the input data and progress for each problem. Given the complexity and cost of expert system construction, it is imperative that system developers and researchers attend to research issues which are critical to knowledge engineering. These questions can be categorized according to the parts of an expert system: (a) knowledge representation; (b) knowledge utilization; and (c) knowledge acquisition. A knowledge acquisition procedure is presented which displays the relationship between subject matter expert expertise consisting of declarative knowledge, procedural knowledge, heuristics, formal rules, and meta-rules. The knowledge engineer uses one or a combination of elicitation methods to gather relevant data to eventually build the components of an expert system. Further explained are the acquisition methods: (a) structured interview; (b) verbal reports; (c) teaching the subject matter; (d) observation; and (e) automated knowledge acquisition tools. The paper concludes with a discussion of the future research issues concerned with using knowledge mapping and task analysis vs. knowledge acquisition techniques.     

基于文档实例的中文信息检索

传统的信息检索系统基于关键词建立索引并进行信息检索.这些系统存在查询返回文档集大、准确率低和普通用户不便于构造查询等不足.为此,该文提出基于文档实例的信息检索,即以已有文档作为样本,在文档库中检索与样本文档相似的所有文档.文中给出了基于文档实例的中文信息检索的解决方法和实现技术.初步实验结果表明该方法是行之有效的.     

Scaffolding student online discussions using past discussions: PedaBot studies

We are developing instructional tools that will help students and instructors use discussion boards more effectively, with an emphasis on automatically assessing discussion activities and promoting student discussion participation and learning. In this paper, we present a discussion scaffolding tool that exploits natural language processing and information retrieval techniques. The PedaBot tool is designed to aid student knowledge acquisition, promote reflection about course topics and encourage student participation in discussions. It dynamically processes student discussions and presents related discussions and document from a knowledge base of past discussions and course materials. This paper describes the system and presents a comparative analysis of the information retrieval techniques used to respond to free-form student discussions, including a combination of topic profiling, term frequency-inverse document frequency, and latent semantic analysis. Responses are presented as annotated links that students can follow and rate for usefulness. The tool has been integrated into a live discussion board and has been used by an undergraduate computer science course for three semesters. We report current studies of PedaBot from its usages based on student viewings, student ratings, and a small survey. Initial results indicate that there is a high level of student interest in the feature and that its responses are moderately relevant to student discussions. We are exploring more opportunities to exposing the tool to students.     

Information visualizations for knowledge acquisition: The impact of dimensionality and color coding

Information visualizations – interactive graphical representations of large amounts of abstract data which do not have a natural visual representation – have mainly been used to support information retrieval. This article investigates whether information visualizations are also suitable for fostering knowledge acquisition as well as how information visualizations, from a cognitive perspective, have to be designed to be efficient learning tools. An experimental study provided evidence that information visualizations support knowledge acquisition. In addition, with regard to the appropriate design, the empirical results showed that two-dimensional information visualizations are better suited for supporting processes of knowledge acquisition than three-dimensional ones and that color-coded information visualizations slightly increase performance in a knowledge test compared to monochromatic ones.     

Perspective: A Standards-Based System for Manufacturing Information Integration

This paper presents an infrastructure and a prototype system for a manufacturing information system, which is distributed its nature and is able to store, index, manage, retrieve and present business data, inventory data, and manufacturing processes data. The system works with all kinds of information, such as continuous (i.e., stream oriented) data, production (e.g., decision support) data, legacy data, and multimedia data (say, drawings, pictures, audio signals, voice annotations, and video streams). A key criterion is support for content-based information retrieval across all application areas. The main objective is to provide support for automated information transactions. The prototype of our architecture uses JAVA, STEP (ISO 10303) standard, the Internet, and CORBA. A fully functional system, called Perspective, for retrieval of part and manufacturing process has been designed and implemented in a distributed environment. Among other capabilities, the system can retrieve a set of parts or manufacturing processes based on similarity to some desired criteria.     

Retrieval of forest canopy attributes based on a geometric-optical model using airborne LiDAR and optical remote-sensing data

In the retrieval of forest canopy attributes using a geometric-optical model, the spectral scene reflectance of each component should be known as prior knowledge. Generally, these reflectances were acquired by a foregone survey using an analytical spectral device. This article purposed to retrieve the forest structure parameters using light detection and ranging (LiDAR) data, and used a linear spectrum decomposition model to determine the reflectances of the spectral scene components, which are regarded as prior knowledge in the retrieval of forest canopy cover and effective plant area index (PAI_e) using a simplified Li–Strahler geometric-optical model based on a Satellites Pour l'Observation de la Terre 5 (SPOT-5) high-resolution geometry (HRG) image. The airborne LiDAR data are first used to retrieve the forest structure parameters and then the proportion of the SPOT pixel not covered by crown or shadow K_g of each pixel in the sample was calculated, which was used to extract the reflectances of the spectral scene components by a linear spectrum decomposition model. Finally, the forest canopy cover and PAI_e are retrieved by the geometric-optical model. As the acquired time of SPOT-5 image and measured data has a discrepancy of about 2 months, the retrieved result of forest canopy cover needs a further validation. The relatively high value of R ² between the retrieval result of PAI_e and the measurements indicates the efficiency of our methods.     

Medical knowledge management for specific hospital departments

Medical knowledge representation and management is concerned with how to organise the often vague clinical experience of medical staff required for computable models. However, few knowledge management and acquisition tools have entered routine use, since such tools are not perceived by physicians as part of the clinical information process. An attempt to partially solve this problem, we identify two key aspects of knowledge representation and management tasks. The first is to adopt a medical knowledge standardisation to provide a consistent terminology control and to simplify the integration between knowledge management tools and the health information system. The second is to establish an effective knowledge acquisition process in specific medical fields by adapting knowledge acquisition tools. Therefore, the main goal of this work is to define computational models and to design mechanisms for the effective acquisition and management of medical knowledge in real-life hospital departments. To this end, we analyse the representation of medical knowledge (based on deep-causal models) and the development of knowledge management tools (based on ontologies), integrated within the information processing activities of the clinical user. Finally, we illustrate its applicability in the Intensive Care Unit and Pediatry scenarios.     

基于模糊语言方法的信息检索系统的研究

该文提出了一个基于模糊语言方法的信息检索系统模型。该系统分为查询界面子系统、数据库子系统和检索子系统三大部分。在查询界面子系统,用布尔表达式表示用户的查询请求,并对每个查询关键词赋予了两种不同语义的语言值权重,该权重表达了用户的模糊检索要求;在数据库子系统,用索引词一文档模糊矩阵表示待检索的文档,对每个索引词。根据其在文档中的出现频率大小。引入了数值权重;在检索子系统,运用模糊语言方法,对用户输入的布尔查询表达式与索引词一文档模糊矩阵进行自底向上的模糊匹配,最后返回满足用户要求的检索结果。相对于传统的基于查询关键词精确匹配的检索系统而言,该系统能较好地满足用户查询要求中的灵活性。     

Hierarchy as a new data type for qualitative variables

The concept of hierarchy has being explored by the computer science communities during last few decades. Relatively simple hierarchical structures found extensive use in such diverse areas as data modeling, information retrieval, knowledge representation and processing, natural language, pattern recognition, and so on. Recent investigations in information retrieval and data integration have emphasized the use of ontologies and semantic similarity functions as a mechanism for comparing objects that can be retrieved or integrated across heterogeneous repositories. Hierarchies being a simpler, albeit very useful, version of ontologies, can perfectly contribute to model solutions of these problems. Present paper aims to illustrate above thesis by discussing a simple method of information retrieval that uses a hierarchical qualitative data organization. Its main goal is to retrieve objects from any database that are just close to a desired item and control the retrieval process up to a given error, called herein confusion. For doing this, we define a semantic dissimilarity (confusion) between objects to be retrieved as well as introduce a calculus of predicates based on the confusion function.     

Coupling semantic and statistical techniques for dynamically enriching web ontologies

With the development of the Semantic Web technology, the use of ontologies to store and retrieve information covering several domains has increased. However, very few ontologies are able to cope with the ever-growing need of frequently updated semantic information or specific user requirements in specialized domains. As a result, a critical issue is related to the unavailability of relational information between concepts, also coined missing background knowledge. One solution to address this issue relies on the manual enrichment of ontologies by domain experts which is however a time consuming and costly process, hence the need for dynamic ontology enrichment. In this paper we present an automatic coupled statistical/semantic framework for dynamically enriching large-scale generic ontologies from the World Wide Web. Using the massive amount of information encoded in texts on the Web as a corpus, missing background knowledge can therefore be discovered through a combination of semantic relatedness measures and pattern acquisition techniques and subsequently exploited. The benefits of our approach are: (i) proposing the dynamic enrichment of large-scale generic ontologies with missing background knowledge, and thus, enabling the reuse of such knowledge, (ii) dealing with the issue of costly ontological manual enrichment by domain experts. Experimental results in a precision-based evaluation setting demonstrate the effectiveness of the proposed techniques.