Knowledge Extraction Process Research Articles

Personal ontologies: Generation of user profiles based on the YAGO ontology

Personalized search is aimed at tailoring the search outcome to users; to this aim user profiles play an important role: the more faithfully a user profile represents the user interests and preferences, the higher is the probability to improve the search process.In the approaches proposed in the literature, user profiles are formally represented as bags of words, as vectors, or as conceptual taxonomies, generally defined based on external knowledge resources (such as the WordNet and the ODP – Open Directory Project). Ontologies have been more recently considered as a powerful expressive means for knowledge representation. The advantage offered by ontological languages is that they allow a more structured and expressive knowledge representation with respect to the above mentioned approaches. A challenging research activity consists in defining user profiles by a knowledge extraction process from an existing ontology, with the main aim of producing a semantically rich representation of the user interests.In this paper a method to automatically define a personal ontology via a knowledge extraction process from the general purpose ontology YAGO is presented; starting from a set of keywords, which are representatives of the user interests, the process is aimed to define a structured and semantically coherent representation of the user topical interests. In the paper the proposed method is described, as well as some evaluations that show its effectiveness.

Real-time recognition of patient intentions from sequences of pressure maps using artificial neural networks

Objective: In this paper we address the problem of recognising the movement intentions of patients restricted to a medical bed. The developed recognition system will be used to implement a natural human–machine interface to move a medical bed by means of the slight movements of patients with reduced mobility.Methods and material: Our proposal uses pressure map sequences as input and presents a novel system based on artificial neural networks to recognise the movement intentions. The system analyses each pressure map in real-time and classifies the raw information into output classes which represent these intentions. The complexity of the recognition problem is high because of the multiple body characteristics and distinct ways of communicating intentions. To address this problem, a complete processing chain was developed consisting of image processing algorithms, a knowledge extraction process, and a multilayer perceptron (MLP) classification model.Results: Different configurations of the MLP have been investigated and quantitatively compared. The accuracy of our approach is high, obtaining an accuracy of 87%. The model was compared with five well-known classification paradigms. The performance of a reduced model, obtained by through feature selection algorithms, was found to be better and less time-consuming than the original model. The whole proposal has been validated with real patients in pre-clinical tests using the final medical bed prototype.Conclusions: The proposed approach produced very promising results, outperforming existing classification approaches. The excellent behaviour of the recognition system will enable its use in controlling the movements of the bed, in several degrees of freedom, by the patient with his/her own body.

Study on Method of Knowledge Acquisition and its Application in the Design of Vehicle Clutch

Facing a great deal of materials and data in the process of design and manufacturing of clutch product, Quick and accurate knowledge extraction from the literature resources and web pages is a key to the design of vehicle clutch. The motheds of automatic knowledge acquisition based on literature and XML pages were proposed as well as specific algorithms and processes of knowledge extraction from documents and web pages were constructed.Techniques of knowledge element extraction, agent and ontology were applied to the intelligent type selection system.Practice tells that this intelligent type selection system can better assist intelligent type selection and design of vehicle clutch and knowledge management

EFFICIENT METHODOLOGIES TO HANDLE HANGING PAGES USING VIRTUAL NODE

In this article we first explain the knowledge extraction (KE) process from the World Wide Web (WWW) using search engines. Then we explore the PageRank algorithm of Google search engine (a well-known link-based search engine) with its hidden Markov analysis. We also explore one of the problems of link-based ranking algorithms called hanging pages or dangling pages (pages without any forward links). The presence of these pages affects the ranking of Web pages. Some of the hanging pages may contain important information that cannot be neglected by the search engine during ranking. We propose methodologies to handle the hanging pages and compare the methodologies. We also introduce the TrustRank algorithm (an algorithm to handle the spamming problems in link-based search engines) and include it in our proposed methods so that our methods can combat Web spam. We implemented the PageRank algorithm and TrustRank algorithm and modified those algorithms to implement our proposed methodologies.

Achieving Memetic Adaptability by Means of Agent-Based Machine Learning

Over recent years, there has been increasing interest of the research community towards evolutionary algorithms, i.e., algorithms that exploit computational models of natural processes to solve complex optimization problems. In spite of their ability to explore promising regions of the search space, they present two major drawbacks: 1) they can take a relatively long time to locate the exact optimum and 2) may sometimes not find the optimum with sufficient precision. Memetic Algorithms are evolutionary algorithms inspired by both Darwinian principles and Dawkins' notion of a meme, able not only to converge to high-quality solutions, but also search more efficiently than their conventional evolutionary counterparts. However, memetic approaches are affected by several design issues related to the different choices that can be made to implement them. This paper introduces a multiagent-based memetic algorithm which executes in a parallel way different cooperating optimization strategies in order to solve a given problem's instance in an efficient way. The algorithm adaptation is performed by jointly exploiting a knowledge extraction process together with a decision making framework based on fuzzy methodologies. The effectiveness of our approach is tested in several experiments in which our results are compared with those obtained by nonadaptive memetic algorithms. The superiority of the proposed strategy is manifest in the majority of cases.

Interactive visualization in multiclass learning: integrating the SASSC algorithm with KLIMT

The paper deals with multiclass learning from the perspective of analytically interpreting the results of the analysis as well as that of navigating into them by using interactive visualization tools. It is showed that by combining the Sequential Automatic Search of Subset of Classifiers (SASSC) algorithm with the interactive visualization of classification trees provided by the Klassification—Interactive Methods for Trees (KLIMT) software it is possible to highlight important information deriving from the knowledge extraction process without neglecting the prediction accuracy of the classification method. Empirical evidence from two benchmark datasets demonstrates the advantages deriving from the joint use of SASSC and KLIMT.

Facing dynamic optimization using a cooperative metaheuristic configured via fuzzy logic and SVMs

Abstract Many dynamic optimization problems appear in the real world, and to solve them we need to find strategies that can track the optimum as it moves in the search space. In this paper we propose the use of a cooperative metaheuristic to cope with such problems. In this strategy different metaheuristics cooperate under the supervision of a coordinator. This coordinator is able to control the cooperation using a collection of Support Vector Machine models and a fuzzy decision framework. The combination of these two techniques allows us to modify the behavior of the strategy depending on the instance being solved. In order to obtain the models we use a well defined knowledge extraction process, which is performed only once and before operating the strategy. To test the validity of this approach we have applied it to a combinatorial optimization problem and a continuous optimization problem, respectively, Dynamic Knapsack Problem and Moving Peaks Benchmark. The strategy has been compared with other individual and cooperative strategies with very interesting results.

The development of a two-phase knowledge extraction framework on liquid crystal display image quality with knowledge-creation process

This study develops a two-phase knowledge extraction framework of liquid crystal display (LCD) to provide some useful knowledge for ordinary users and manufacturers. At the first phase, the knowledge about important factors from ordinary users' viewpoints is extracted through an experiment. At the second phase, a systematic method is developed to convert these important factors into related manufacturing techniques and materials from manufacturers' viewpoints. Finally, the two-phase knowledge extraction framework is demonstrated to illustrate the knowledge extraction process of LCD image quality.

Structuring and extracting knowledge for the support of hypothesis generation in molecular biology.

BackgroundHypothesis generation in molecular and cellular biology is an empirical process in which knowledge derived from prior experiments is distilled into a comprehensible model. The requirement of automated support is exemplified by the difficulty of considering all relevant facts that are contained in the millions of documents available from PubMed. Semantic Web provides tools for sharing prior knowledge, while information retrieval and information extraction techniques enable its extraction from literature. Their combination makes prior knowledge available for computational analysis and inference. While some tools provide complete solutions that limit the control over the modeling and extraction processes, we seek a methodology that supports control by the experimenter over these critical processes.ResultsWe describe progress towards automated support for the generation of biomolecular hypotheses. Semantic Web technologies are used to structure and store knowledge, while a workflow extracts knowledge from text. We designed minimal proto-ontologies in OWL for capturing different aspects of a text mining experiment: the biological hypothesis, text and documents, text mining, and workflow provenance. The models fit a methodology that allows focus on the requirements of a single experiment while supporting reuse and posterior analysis of extracted knowledge from multiple experiments. Our workflow is composed of services from the 'Adaptive Information Disclosure Application' (AIDA) toolkit as well as a few others. The output is a semantic model with putative biological relations, with each relation linked to the corresponding evidence.ConclusionWe demonstrated a 'do-it-yourself' approach for structuring and extracting knowledge in the context of experimental research on biomolecular mechanisms. The methodology can be used to bootstrap the construction of semantically rich biological models using the results of knowledge extraction processes. Models specific to particular experiments can be constructed that, in turn, link with other semantic models, creating a web of knowledge that spans experiments. Mapping mechanisms can link to other knowledge resources such as OBO ontologies or SKOS vocabularies. AIDA Web Services can be used to design personalized knowledge extraction procedures. In our example experiment, we found three proteins (NF-Kappa B, p21, and Bax) potentially playing a role in the interplay between nutrients and epigenetic gene regulation.

Using machine learning in a cooperative hybrid parallel strategy of metaheuristics

This paper proposes the construction of a centralized hybrid metaheuristic cooperative strategy to solve optimization problems. Knowledge (intelligence) is incorporated into the coordinator to improve performance. This knowledge is incorporated through a set of rules and models obtained from a knowledge extraction process applied to the records of the results returned by individual metaheuristics. The effectiveness of the approach is tested in several computational experiments in which we compare the results obtained by the individual metaheuristics, by several non-cooperative and cooperative strategies and by the strategy proposed in this paper.

Ontology-supported database marketing

Database marketing (DBM) provides in -depth analysis of marketing databases. Knowledge discovery in database techniques is one of the most prominent approaches to supporting some of the DBM process phases. However, in many cases, the benefits of these tools are not fully exploited by marketers. Complexity and amount of data constitute two major factors limiting the application of knowledge discovery techniques in marketing activities. Currently, ontologies may here play an important role in the marketing discipline. Motivated by its success in the area of artificial intelligence, we propose an ontology-supported DBM approach. The approach aims to enhance DBM with ontology by providing detailed step-phase specific information. Our research work has its foundations in a double methodological approach using the Delphi and Action Research methodologies. First, we use Delphi to structure related DBM knowledge, and then we align our work to the Action Research methodology in order to systematise the knowledge extraction process and knowledge base creation. The issues raised in this paper both respond and contribute to calls for a DBM process improvement. Our work was evaluated in the relationship marketing domain focusing on a relational marketing programme database. The findings of this study not only advance the state of DBM research, but also shed light on future research directions.

PPKM: Preserving Privacy in Knowledge Management

This paper discusses the techniques that support the extraction, sharing, and utilization of knowledge for collaborative problem solving applications. A system framework is proposed for secure knowledge management, called PPKM, which in addition to provide standard security mechanisms such as access control, will possess crucial feature, namely privacy-preservation, where privacy-preservation means that the knowledge extraction process should not compromise the privacy of the source data. This framework is explained by elaborating on its components and their relationship to existing techniques such as database, data perturbation, rule hiding, data mining, and machine learning.

Using conditional FCM to mine event-related brain dynamics

We introduce a framework for mining event related dynamics based on conditional FCM (CFCM). For a given set of responses, the variation in the data is summarized by means of a small set of meaningful prototypes accompanied with a low-dimensional graph capturing their relative relationships. CFCM enables prototyping in a principled manner. User-defined constraints, which are imposed by the nature of experimental data and/or dictated by the neuroscientist's intuition, direct the process of knowledge extraction and can robustify single-trial analysis. The method is introduced using simulated data and demonstrated using actual encephalographic data.

Seeding the survey and analysis of research literature with text mining

Text mining is a semi-automated process of extracting knowledge from a large amount of unstructured data. Given that the amount of unstructured data being generated and stored is increasing rapidly...

Comparing extended classifier system and genetic programming for financial forecasting: an empirical study

As a broad subfield of artificial intelligence, machine learning is concerned with the development of algorithms and techniques that allow computers to learn. These methods such as fuzzy logic, neural networks, support vector machines, decision trees and Bayesian learning have been applied to learn meaningful rules; however, the only drawback of these methods is that it often gets trapped into a local optimal. In contrast with machine learning methods, a genetic algorithm (GA) is guaranteeing for acquiring better results based on its natural evolution and global searching. GA has given rise to two new fields of research where global optimization is of crucial importance: genetic based machine learning (GBML) and genetic programming (GP). This article adopts the GBML technique to provide a three-phase knowledge extraction methodology, which makes continues and instant learning while integrates multiple rule sets into a centralized knowledge base. Moreover, the proposed system and GP are both applied to the theoretical and empirical experiments. Results for both approaches are presented and compared. This paper makes two important contributions: (1) it uses three criteria (accuracy, coverage, and fitness) to apply the knowledge extraction process which is very effective in selecting an optimal set of rules from a large population; (2) the experiments prove that the rule sets derived by the proposed approach are more accurate than GP.

Knowledge-based Intelligent Diagnosis of Ground Robot Collision with Non Detectable Obstacles

The paper describes the application of fuzzy techniques to analyze motion problems in a mobile robot. The robot is equipped with ultrasound sensors used for obstacle detection, but, in some cases, small obstacles are out of the range of the sensors and can be dragged by the robot without being detected. Using other variables as, measured velocity, undershoots of that velocity or changes in battery voltage, a fuzzy system is able to determine those situations. The paper also analyzes the knowledge extraction process for the application using expert and induced knowledge (from data collected during navigation tasks) in a cooperative way, dealing with integration and simplification issues. The expert knowledge was used for describing the robot behaviour in order to identify the variables that should be used with the aim of detecting a collision of the vehicle against an undetected obstacle, as well as proposing a suitable recovery action. Data collected in real trials were used for inducing knowledge so as to complete and validate the expert knowledge. Both kind of knowledge were integrated in the final fuzzy-based system. The aim is to build up a knowledge base, which is interpretable and accurate at the same time, and it is used by our fuzzy system in order to solve the motion problems under consideration.

BUSINESS KNOWLEDGE EXTRACTION FROM LEGACY INFORMATION SYSTEMS

This article discusses the process of enterprise knowledge extraction from relational database and source code of legacy information systems. Problems of legacy systems and main solutions for them are briefly described here. The uses of data reverse engineering and program understanding techniques to automatically infer as much as possible the schema and semantics of a legacy information system is analyzed. Eight step data reverse engineering algorithm for knowledge extraction from legacy systems is provided. A hypothetical example of knowledge extraction from legacy information system is presented.

Technology and knowledge document cluster analysis for enterprise R&D strategic planning

Through technology and knowledge document analysis, companies can realise the condition of specified technology development and the potential competitors in the market. As patents provide exclusive right and legal protection for patent inventors, patents play an important role in the development of technology. This paper presents the process of patent knowledge extraction and methodologies of patent analysis to improve the efficiency of patent analysis. Furthermore, the methodologies proposed in this paper include patent map analysis, patent technology clustering, patent document clustering and technology maturity measurement. Through these methodologies, companies can gain rich information and perform better in patent management. Moreover, the strategic plans of R&D can also be determined using the methodologies proposed in this paper.

Technical Knowledge Consolidation using Theory of Inventive Problem Solving

Technical knowledge is a valuable asset for construction companies. The diversity and accumulation of such knowledge on an organizational level contribute to company profitability and growth. This paper discusses a new approach for extracting, consolidating, and then retrieving technical construction knowledge that builds on the contradiction resolution concepts of the theory of inventive problem solving. The approach was used to extract knowledge from a number of lessons learned describing technical construction problems encountered by a major construction company. The approach depends on finding the similarities between technical solutions of problems that belong to different technological domains. These similarities represent the essence of these solutions and are represented using domain-independent terms so that they can be applied to new problems. The outcomes of the knowledge extraction and accumulation process are discussed in the paper to address the feasibility of the proposed approach and its potential benefits and limitations.

Expert-Novice Differencesin the Understanding and Explanation of Complex Political Conflicts

We compare the structure and content of political experts' knowledge with that of novices. We were particularly interested in whether experts would show more causal and historical reasoning in explaining political events, as well as whether their knowledge was structured in the form of a narrative. Eight relative political experts (advanced political science graduate students), 19 intermediate knowledge subjects (7 psychology graduate students and 12 knowledgeable undergraduates), and 9 novices were extensively interviewed about 1 of 3 political conflicts in a 2-step knowledge extraction process advocated by Graesser and Clark (1985). Experts relied more on events; used a more historical analysis consisting of past states, events, goals, and actions; and, most important, relied heavily on causal reasoning to create a coherent, understandable causal scenario or narrative. In addition, experts' overall explanation networks were significantly more connected (but less centralized) than those of the other groups.