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Fuzzy cognitive maps take the features of a problem or a system as a concept to solve a problem or to model real world systems. Here, the positive or negative relationships between the concepts of fuzzy cognitive map are expressed with fuzzy weights, and the proposed solution or cognitive construction of the system model is completed. Previously, many different perspectives have been proposed in the literature for FCM weighting. Some of them utilize an expert group to determine the relationships between concepts, some using Hebbian-based methods, some using population-based meta-heuristic optimization methods, and some FCMs use different supervised computational intelligence methods. For FCMs that use supervised learning techniques in weight determination and updating, it is not possible to provide a data set with system-related conceptual relationships. Data sets for systems or problems generally contain the values that the concept values take over time. In this study, it is aimed to bring the cognitive weights corresponding to the concept values of the raw data sets of the systems to be expressed with FCM by a genetic algorithm based method. The method tested on different scenarios has a very high performance.

Migration and its impact on society and language have been one of the most discussed issues in human history. The aim of the present study is to review the basic migration notions and related terminology and to perform a comparative analysis of terminology in various cultures and legislations. The theoretical framework of the study is based on the latest development of mobility paradigm in social sciences and linguistics focusing on language of international migration and legislation. Language studies within mobility paradigm reveal particulars of language use in globalized world. Other approaches such as conceptual and comparative analysis have been used to specify terminology and language of international migration. Conceptual analysis allows examining the concept system and concept relationship of this particular sphere and provides comparison and data aggregation about the entire process of migration. A comparative approach to Russian, European, and the USA migration terminology reveals similarities and differences of concepts and their designation in various cultures and languages. The results of present studies contribute to the comprehensive description of international migration language development, importance of its terminology for communicating migration policies, legislative instruments of migration and translation.

This paper presents a language-independent ontology (LION) construction method that uses tagged images in an image folksonomy. Existing multilingual frameworks that construct an ontology deal with concepts translated on the basis of parallel corpora, which are not always available; however, the proposed method enables LION construction without parallel corpora by using visual features extracted from tagged images as the alternative. In the proposed method, visual similarities in tagged images are leveraged to aggregate synonymous concepts across languages. The aggregated concepts take on intrinsic semantics of themselves, while they also hold distinct characteristics in different languages. Then relationships between concepts are extracted on the basis of visual and textual features. The proposed method constructs a LION whose nodes and edges correspond to the aggregated concepts and relationships between them, respectively. The LION enables successful image retrieval across languages since each of the aggregated concepts can be referred to in different languages. Consequently, the proposed method removes the language barriers by providing an easy way to access a broader range of tagged images for users in the folksonomy, regardless of the language they use.

Background Information in Electronic Health Records is largely stored as unstructured free text. Natural language processing (NLP), or Medical Language Processing (MLP) in medicine, aims at extracting structured information from free text, and is less expensive and time-consuming than manual extraction. However, most algorithms in MLP are institution-specific or address only one clinical need, and thus cannot be broadly applied. In addition, most MLP systems do not detect concepts in misspelled text and cannot detect attribute relationships between concepts. The objective of this study was to develop and evaluate an MLP application that includes generic algorithms for the detection of (misspelled) concepts and of attribute relationships between them. Methods An implementation of the MLP system cTAKES, called DIRECT, was developed with generic SNOMED CT concept filter, concept relationship detection, and attribute relationship detection algorithms and a custom dictionary. Four implementations of cTAKES were evaluated by comparing 98 manually annotated oncology charts with the output of DIRECT. The F1-score was determined for named-entity recognition and attribute relationship detection for the concepts ‘lung cancer’, ‘non-small cell lung cancer’, and ‘recurrence’. The performance of the four implementations was compared with a two-tailed permutation test. Results DIRECT detected lung cancer and non-small cell lung cancer concepts with F1-scores between 0.828 and 0.947 and between 0.862 and 0.933, respectively. The concept recurrence was detected with a significantly higher F1-score of 0.921, compared to the other implementations, and the relationship between recurrence and lung cancer with an F1-score of 0.857. The precision of the detection of lung cancer, non-small cell lung cancer, and recurrence concepts were 1.000, 0.966, and 0.879, compared to precisions of 0.943, 0.967, and 0.000 in the original implementation, respectively. Conclusion DIRECT can detect oncology concepts and attribute relationships with high precision and can detect recurrence with significant increase in F1-score, compared to the original implementation of cTAKES, due to the usage of a custom dictionary and a generic concept relationship detection algorithm. These concepts and relationships can be used to encode clinical narratives, and can thus substantially reduce manual chart abstraction efforts, saving time for clinicians and researchers.

Using the methods of economic criminology, the author explains the meaning of the concept of «criminalization of the economy» and «criminalization of the economic relations» and shows the interconnection of these phenomena with economic processes and legislation.

We synthesize previous research on information technology (IT) management and benchmarking into a concept relationship map on strategic IT benchmarking success.We find that there is much literature on how to obtain strategic IT benchmarking (SITBM) results.Research does not, however, explain how results are transformed into outcomes.We suggest propositions and future research directions as a foundation for a theory of successful SITBM. Information technology (IT) organizations use strategic IT benchmarking (SITBM) to revise IT strategies or perform internal marketing. Despite benchmarking's long tradition, many SITBM initiatives do not reveal the desired outcomes. The vast body of knowledge on benchmarking and IT management does not help overcome the challenges of successful SITBM. We therefore synthesize the existing research on IT management and benchmarking into a concept relationship map and derive future research avenues. We find that there is much literature on how to obtain SITBM results. However, research does not explain how these results are transformed into outcomes - essentially the problem many practitioners struggle with.

In this paper, we investigate how to establish the relationship between semantic concepts based on the large-scale real-world click data from image commercial engine, which is a challenging topic because the click data suffers from the noise such as typos, the same concept with different queries, etc. We first define five specific relationships between concepts. We then extract some concept relationship features in textual and visual domain to train the concept relationship models. The relationship of each pair of concepts will thus be classified into one of the five special relationships. We study the efficacy of the conceptual relationships by applying them to augment imperfect image tags, i.e., improve representative power. We further employ a sophisticated hashing approach to transform augmented image tags into binary codes, which are subsequently used for content-based image retrieval task. Experimental results on NUS-WIDE dataset demonstrate the superiority of our proposed approach as compared to state-of-the-art methods.

Traditionally, content-based recommendation is performed using term occurrences, which are leveraged in the TF-IDF method. This method is the defacto standard in text mining and information retrieval. Valuable additional information from domain ontologies, however, is not employed by default. The TF-IDF-based CF-IDF method successfully utilizes the semantics of a domain ontology for news recommendation by detecting ontological concepts instead of terms. However, like other semantics-based methods, CF-IDF fails to consider the different concept relationship types. In this paper, we extend CF-IDF to additionally take into account concept relationship types. Evaluation is performed using Ceryx, an extension to the Hermes news personalization framework. Using a custom news data set, our CF-IDF+ news recommender outperforms the CF-IDF and TF-IDF recommenders in terms of \(F_1\) and Kappa.

Concepts are the abstract representatives of those classifications created by objects, events, ideas and behaviors which have some similar or common traits. Individuals think through concepts while they are thinking. The richer relations have the concepts with each other, the richer will thinking process is. Therefore, developing concepts is an important part for education. Social Sciences have a great role in developing concepts. The aim of this study is to find out how one can associate some citizenship concepts of social sciences classes with those of related with them. For this purpose, government, citizenship, democracy, constitution, state, election and public concepts, which are planned to be developed through Social Studies Curriculum for 4-7 graders, have been selected. In this research single crosshatching model has been used among general crosshatching models. In order to achieve the goal of the study, a word association test developed through the topic, has been applied to 181 8th grader students. The achieved data has been analyzed in terms of percentage and frequency of the concepts. Concepts which have breakpoints above 60, 50, 40 and 30, have been associated with mind maps. At the end of the research, mind maps of the students upon citizenship concepts have been deducted and it has been concluded that they have some misunderstandings about public, state and government concepts. It has also been understood that the concept of citizenship could not been associated with other concepts. Apart from this, it has been seen that concepts like prime-minister, president, right, liberty and TGNA (Turkish Grand National Assembly) have come into prominence.

It is normally hard to believe in software security claim if we do not know what is meant exactly by "secure" and the reasons to support the claim are not sufficiently provided. Security cases—which document the rationale for believing that a system is adequately secure—are intended to address both these issues. However, due to lack of practical construction method of security case, there has been limited use of security case so far. This paper presents a hierarchical software security case development method. We present a general asset model and a security concept relationship model first, then come up with a hierarchical asset-threat-control measure argument strategy, which is supported by the general asset model and software threat classification to make it explicit. Lastly, we propose several key argument patterns, which are reusable and instrumental for security case development. A case study of an IM (instant messaging) server is used to demonstrate the capability of this method.

Diagnosing student learning barriers has been recognized as the most fundamental and important issue for improving the learning achievements of students. In the past decade, several learning diagnosis approaches have been proposed based on the concept–effect relationship (CER) model. However, past studies have shown that the effectiveness of this model heavily depends on the concept relationship knowledge provided by the domain experts (eg, experienced teachers or educators for a specified subject); ie, the performance of the developed learning diagnosis systems could be significantly affected by subjective opinions, ignorance or insufficient knowledge if those concept relationships are derived from a single domain expert. To cope with this problem, this study proposes a group decision approach for developing the CER model with the cooperation of multiple domain experts. Based on the proposed approach, a testing and diagnostic system has been implemented; moreover, an experiment has been conducted to evaluate the effectiveness of this new approach. The experimental results show that this approach is able to develop quality CER models, and hence the low-achievement students who received the generated learning suggestions had significantly better learning achievements than those who learned with the previous approach.

28th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2015; Seoul; South Korea; 10 June 2015 through 12 June 2015, We discuss the Fuzzy-Syllogistic System (FSS) that consists of the well-known 256 categorical syllogisms, namely syllogistic moods, and Fuzzy- Syllogistic Reasoning (FSR), which is an implementation of the FSS as one complex approximate reasoning mechanism, in which the 256 moods are interpreted as fuzzy inferences. Here we introduce a sample application of FSR as ontology reasoner. The reasoner can associate up to 256 possible fuzzyinferences with truth ratios in [0,1] for every triple concept relationship of the ontology. We further discuss a transformation technique, by which the truth ratio of a fuzzy-inference can increase, by adapting the fuzzy-quantifiers of a fuzzy-inference to the syllogistic logic of the sample propositions.

This study uncovered ambiguity and inconsistency of the semantic relationships of the existing thesaurus by analyzing the concept relationships of AGROVOC and proposed the concept relationships of ontology in partially overcoming these limitations. By the results of analyzing the concept relationships, the study proposed conceptual model as most important part of conecept relationships of ontology and semantically developed concept relationship types. These relationships partially can perform inferences and must be useful for information knowledge system based on more exact semantic relationships. Also the study found out new relationship types and they will be useful for extension of the concept relationships of existing thesaurus. And these relationship types showed that they were useful for the existing thesaurus as Legal Thesaurus.

How to establish the relationship between concepts based on the large scale real-world click data from commercial engine is a challenging topic due to that the click data suffers from the noise such as typos, the same concept with different queries etc. In this paper, we propose an approach for automatically establishing the concept relationship. We first define five specific relationships between concepts and leverage them to annotate the images collected from commercial search engine. We then extract some conceptual features in textual and visual domain to train the concept model. The relationship of each pairwise concept will thus be classified into one of the five special relationships. Experimental results demonstrate our proposed approach is more effective than Google Distance.

Many approaches, which to modeling data structure and data behavior for the total shipbuilding, have been made so far. Especially Object-Oriented product modeling approaches have led to some successful results.Data in one product model for shipbuilding is inevitably referenced and manipulated during each phase of design or production activities, and data or manipulation status naturally varies from the original with the advance of each activities. For Object-Oriented approach, it is hard to identify object classes dealing with those variations, and even if once a product model developed, it might be getting much harder to modify it to cope with a new additional phase of activities.This paper proposes an approach, temporarily named Concept-Relationship Modeling Approach, which handles data structure and behavior independently of each other in order to resolve the difficulties above.

This paper applies Chinese word segmentation technology to the automatic extraction and description of the relationship between concepts. It takes text as corpus, matches the concept-pairs by rules and then describes the relationship between concepts in statistical methods. The paper implements an experiment based on the text in the field "respond to emergency", and optimizes speech tagging on account of experimental results, so that the relations extracted are more meaningful to emergency response. It analyzes the display order of inquiries and formulates rules of response and makes the results more meaningful. Consequently, the method turns out to be effective, and can be flexibly extended to other areas.

In this paper, a new graph diffusion method is presented to improve the high-level feature extraction performance. In this method, we construct a semantic graph by describe the concepts as nodes and the concept affinities as the weights of edges, then we use the training set and its corresponding label matrix to estimate the concept relationship, where the relationship of two concepts were measured by the inner product of its corresponding row vector. We test the method on the high-level feature extraction task of TRECVID 2009 and the experimental results show the effectiveness of the method.

We propose a new method to boost the performance of video annotation by exploiting concept relationship in temporal context. The motivation of our idea mainly comes from the fact that temporally continuous shots in video are generally with consistent content, so that concepts in these shots should be semantically relevant. We utilize a temporal model to describe the contributions of relevant concepts to the presence of a target concept. By connecting our model with conditional random field and adopting the learning and inference approaches of it, we could obtain the refined probability of a concept occurring in the shot, which is the leverage of temporal context and initial output of video annotation. Experimental results on the widely used TRECVID dataset exhibit the effectiveness of our method for improving video annotation accuracy.

The problem of learning concept hierarchies and terminological ontologies can be divided into two sub-tasks: concept extraction and relation learning. The authors of this chapter describe a novel approach to learn relations automatically from unstructured text corpus based on probabilistic topic models. The authors provide definition (Information Theory Principle for Concept Relationship) and quantitative measure for establishing “broader” (or “narrower”) and “related” relations between concepts. They present a relation learning algorithm to automatically interconnect concepts into concept hierarchies and terminological ontologies with the probabilistic topic models learned. In this experiment, around 7,000 ontology statements expressed in terms of “broader” and “related” relations are generated using different combination of model parameters. The ontology statements are evaluated by domain experts and the results show that the highest precision of the learned ontologies is around 86.6% and structures of learned ontologies remain stable when values of the parameters are changed in the ontology learning algorithm.

While applying attribute based access control in dynamic open multi-domain environments, an important critical problem is how to make these domains know each other about the semantics of attributes defined by them. Towards this problem, a novel method of attribute matching is proposed in this paper. This matching is based on other domains’ knowledges of the specific concept relationship. The knowledge involves five different concept relationships. Compared with the numerical similarity between two concepts, these concept relationships are more applicable in access control. In addition, the proposed method does not need the overall information of ontologies to which the matched concepts belong. For this, examples are given to illustrate applications of the method.

The prospects of integrating full-blown biological taxonomies into an ontological reasoning framework are reviewed. We contrast the common usage of a static 'snapshot' hierarchy in ontological representations of taxonomy with a more realistic situation that involves dynamic, piece-meal revisions of particular taxonomic groups and requires alignment with relevant preceding perspectives. Taxonomic practice is characterized by a range of phenomena that are orthogonal to the logical semantic background from which ontological entities and relationships originate, and therefore pose special challenges to ontological representation and reasoning. Among these phenomena are: (1) the notion that there is a single phylogenetic hierarchy in nature which taxonomy can only gradually approximate; (2) the evolvability of taxa which means that taxon-defining features may be lost in subordinate members or independently gained across multiple sections of the tree of life; (3) the hybrid approach of defining taxa both in reference to properties (intensional) and members (ostensive) which undermines the individual/class dichotomy sustaining conventional ontologies; (4) the idiosyncratic yet inferentially valuable usage of Linnaean ranks; (5) the indelible and semantically complex 250-year legacy of nomenclatural and taxonomic changes that characterizes the current system; (6) the insufficient taxonomic exploration of large portions of the tree of life; and the need to use a sophisticated terminology for aligning taxonomic entities in order to integrate both (7) single and (8) multiple hierarchies. We briefly such how such integration may proceed based on an initial expert alignment of concept relationship and subsequent use of first-order logic algorithms to maximize consistency, reveal implied relationships, and ultimately merge taxonomies. In light of the aforementioned obstacles, we suggest that research along the taxonomy/ontology interface should focus on either strictly nomenclatural entities or specialize in ontology-driven methods for producing alignments between multiple taxonomies. We furthermore suggest that the prospects of developing successful ontologies for taxonomy will largely depend on the ability of the taxonomic expert community to present their phylogenies and classifications in a way that is more compatible with ontological reasoning than concurrent practice. Minimally, this means (1) adopting rigorous standards for linking new core taxonomies to relevant peripheral taxonomies through comprehensive alignments so that their ontological/taxonomic connections are transparent; (2) using lineage-specific ontological standards for phenotype-based accounts of taxa while taking into account the phylogenetic contextuality of phenotypic descriptors; (3) presenting all nomenclatural and taxonomic novelties in an explicit, ontology-compatible format, including intensional and ostensive definitions; and (4) offering comprehensive intensional/ostensive alignments to entities in relevant preceding taxonomies.

Concept alignment, namely how to make different domains know each other about the semantics of concepts defined by them, is one of the key issues of applying attribute based access control in dynamic open multi-domain environment. In this paper, a novel approach is proposed for building up a concept alignment framework based on other domains’ knowledge about specific concept relationship. The knowledge comprises five different definite concept relationships which are represented by five logical formulae respectively. Compared with the numerical similarity and those related approaches, the knowledge is more suitable for depicting concept relationship, while the proposed approach is more adapted in access control. Finally, a trust degree model is introduced into the framework in order to improve the quality of concept alignment results, and how to use and manage the trust degree is discussed.

There is a subtle risk of ambiguity in the choice between concepts and roles forced by current KL-ONE-like languages, since many roles may be concepts as well. In this paper we explore the ontological foundations of the role/concept relationship, and analyze its implications on the practice of knowledge engineering. We criticize the current interpretation of KL-ONE roles as arbitrary relations, which vanishes their original meaning and makes them identical to slots. We suggest to call attributes those concepts which actually act as conceptual components, and propose a formal semantics which binds these concepts to their corresponding relations.

Supporting multimedia search has emerged as an important research topic. There are three paradigms on the research spectrum that ranges from the least automatic to the most automatic. On the far left end, there is the pure manual labeling paradigm that labels multimedia content, e.g., images and video clips, manually with text labels and then use text search to search multimedia content indirectly. On the far right end, there is the content-based search paradigm that can be fully automatic by using low-level features from multimedia analysis. In recent years, a third paradigm emerged which is in the middle: the annotation paradigm. Once the concept models are trained, this paradigm can automatically detect/annotate concepts in unseen multimedia content. This talk looks into this annotation paradigm. Specifically, this talk argues that within the annotation paradigm, the relationship-based annotation approach outperforms other existing annotation approaches, because individual concepts are considered jointly instead of independently. We will use within concept relationship, between concept relationship and blind concept relationship to illustrate the idea.

Semantic concept detectors are often individually and independently developed. Using peripherally related concepts for leveraging the power of joint detection, which is referred to as context-based concept fusion (CBCF), has been one of the focus studies in recent years. This paper proposes the construction of a context space and the exploration of the space for CBCF. Context space considers the global consistency of concept relationship, addresses the problem of missing annotation, and is extensible for cross-domain contextual fusion. The space is linear and can be built by modeling the inter-concept relationship through annotation provided by either manual labeling or machine tagging. With context space, CBCF becomes a problem of concept selection and detector fusion, under which the significance of a concept/detector can be adapted when applied to a target domain different from where the detector is being developed. Experiments on TRECVID datasets of years 2005 to 2008 confirm the usefulness of context space for CBCF. We observe a consistent improvement of 2.8% to 38.8% for concept detection when context space is used, and more importantly, with significant speed-up compared to existing approaches.

The paper proposes a concept segmentation method to extract meta-knowledge from the multi-source knowledge base. We improve the traditional structure-based extracting method by using the concept hierarchical partition. The concept and concept relationship can be described with ontology model, which can discover the semantic relationship between concepts. Then a self-learning of meta-knowledge model is set up which can optimize the meta-knowledge description. Finally an empirical study is carried out by implementing the meta-knowledge extraction process from multi-source knowledge bass for educational resources.

An increasingly common approach being taken by taxonomists to define the relationships between taxa in alternative hierarchical classifications is to use a set-based notation which states relationship between two taxa from alternative classifications. Textual recording of these relationships is cumbersome and difficult for taxonomists to manage. While text based GUI tools are beginning to appear which ease the process, these have several limitations. Interactive visual tools offer greater potential to allow taxonomists to explore the taxa in these hierarchies and specify such relationships. This paper describes the Concept Relationship Editor, an interactive visualisation tool designed to support the assertion of relationships between taxonomic classifications. The tool operates using an interactive space-filling adjacency layout which allows users to expand multiple lists of taxa with common parents so they can explore and assert relationships between two classifications.

In this article I present research on different forms of concept representation in specialized aeronautical documents. I have isolated a sample of concepts in Spanish documents addressed to fighter aircraft mechanics in the area of fighter aircraft fuel systems. I have created a terminological database comprising these concepts and their representations, i.e., terms, explanations and illustrations. The analysis of this terminographic work allows us to determine the degree of impor- tance to the expert of the different forms of concept representation and the relationships between them.

The results of interviews with 75 infertile married couples show difficulties of the partners to understand their roles and difficulties in the relationship of the couple. Correlations to known causes of the infertility and following conceptions support the opinion that infertile marriages consider themselves better than they are on objective evaluation. Furthermore, there are correlations between the subjective evaluation of the cause of infertility. Four behaviour patterns for the subjective evaluation of the infertility can be recognized. The importance of the evaluated factors for infertility and the treatment of infertility are discussed.