Concept-based Representation Research Articles

Mapping the Landscape of Impostor Phenomenon Research in Organizational Behavior: A Bibliometric Study between 2003 and 2022

The Impostor Phenomenon is a psychological phenomenon characterized by an individual's inability to internalize their success despite substantial evidence, leading them to feel like a fraud in their status. This phenomenon is increasingly recognized as an important factor in the success and survival of organizations. However, despite its practical and scientific importance, the Impostor Phenomenon has a relatively short history in the field of organizational behavior. Research into the impostor phenomenon in organizational behavior has seen significant growth between 2003 and 2022. This study aims to provide a comprehensive overview of the research status of this topic during this period and to determine the main and sub-themes in which this concept has been examined in academic contexts. The current study focuses on determining which main and sub-themes this concept is examined in academic context. In the study, the articles in the web of science database were examined. The articles were analyzed by performing Bibliometrics analysis with the Vosviewer program. It is aimed to determine and cluster the network relations of the concepts with each other. It has been determined that five basic clusters are word or concept-based representation of imposter phenomenon. These clusters are named as “Organizational Psychology”, "Business Management”, “Sustainable Business”, “"Human Resource Management and Positive Psychology” and "Organizational Change Management."

Semantic Recovery of Traceability Links between System Artifacts

This paper introduces a mechanism to recover traceability links between the requirements and logical models in the context of critical systems development. Currently, lifecycle processes are covered by a good number of tools that are used to generate different types of artifacts. One of the cornerstone capabilities in the development of critical systems lies in the possibility of automatically recovery traceability links between system artifacts generated in different lifecycle stages. To do so, it is necessary to establish to what extent two or more of these work products are similar, dependent or should be explicitly linked together. However, the different types of artifacts and their internal representation depict a major challenge to unify how system artifacts are represented and, then, linked together. That is why, in this work, a concept-based representation is introduced to provide a semantic and unified description of any system artifact. Furthermore, a traceability function is defined and implemented to exploit this new semantic representation and to support the recovery of traceability links between different types of system artifacts. In order to evaluate the traceability function, a case study in the railway domain is conducted to compare the precision and recall of recovery traceability links between text-based requirements and logical model elements. As the main outcome of this work, the use of a concept-based paradigm to represent that system artifacts are demonstrated as a building block to automatically recover traceability links within the development lifecycle of critical systems.

Predicting software defect type using concept-based classification

Automatically predicting the defect type of a software defect from its description can significantly speed up and improve the software defect management process. A major challenge for the supervised learning based current approaches for this task is the need for labeled training data. Creating such data is an expensive and effort-intensive task requiring domain-specific expertise. In this paper, we propose to circumvent this problem by carrying out concept-based classification (CBC) of software defect reports with help of the Explicit Semantic Analysis (ESA) framework. We first create the concept-based representations of a software defect report and the defect types in the software defect classification scheme by projecting their textual descriptions into a concept-space spanned by the Wikipedia articles. Then, we compute the “semantic” similarity between these concept-based representations and assign the software defect type that has the highest similarity with the defect report. The proposed approach achieves accuracy comparable to the state-of-the-art semi-supervised and active learning approach for this task without requiring labeled training data. Additional advantages of the CBC approach are: (i) unlike the state-of-the-art, it does not need the source code used to fix a software defect, and (ii) it does not suffer from the class-imbalance problem faced by the supervised learning paradigm.

Unreported links between trial registrations and published articles were identified using document similarity measures in a cross-sectional analysis of ClinicalTrials.gov

ObjectivesTrial registries can be used to measure reporting biases and support systematic reviews, but 45% of registrations do not provide a link to the article reporting on the trial. We evaluated the use of document similarity methods to identify unreported links between ClinicalTrials.gov and PubMed. Study Design and SettingWe extracted terms and concepts from a data set of 72,469 ClinicalTrials.gov registrations and 276,307 PubMed articles and tested methods for ranking articles across 16,005 reported links and 90 manually identified unreported links. Performance was measured by the median rank of matching articles and the proportion of unreported links that could be found by screening ranked candidate articles in order. ResultsThe best-performing concept-based representation produced a median rank of 3 (interquartile range [IQR] 1–21) for reported links and 3 (IQR 1–19) for the manually identified unreported links, and term-based representations produced a median rank of 2 (1–20) for reported links and 2 (IQR 1–12) in unreported links. The matching article was ranked first for 40% of registrations, and screening 50 candidate articles per registration identified 86% of the unreported links. ConclusionLeveraging the growth in the corpus of reported links between ClinicalTrials.gov and PubMed, we found that document similarity methods can assist in the identification of unreported links between trial registrations and corresponding articles.

Counting trees in Random Forests: Predicting symptom severity in psychiatric intake reports

The CEGS N-GRID 2016 Shared Task (Filannino et al., 2017) in Clinical Natural Language Processing introduces the assignment of a severity score to a psychiatric symptom, based on a psychiatric intake report. We present a method that employs the inherent interview-like structure of the report to extract relevant information from the report and generate a representation. The representation consists of a restricted set of psychiatric concepts (and the context they occur in), identified using medical concepts defined in UMLS that are directly related to the psychiatric diagnoses present in the Diagnostic and Statistical Manual of Mental Disorders, 4th Edition (DSM-IV) ontology. Random Forests provides a generalization of the extracted, case-specific features in our representation. The best variant presented here scored an inverse mean absolute error (MAE) of 80.64%. A concise concept-based representation, paired with identification of concept certainty and scope (family, patient), shows a robust performance on the task.

Wikipedia-based cross-language text classification

This paper presents the application of a Wikipedia-based bag of concepts (WikiBoC) document representation to cross-language text classification (CLTC). Its main objective is to alleviate the major drawbacks of the state-of-the-art CLTC approaches – typically based on the machine translation (MT) of documents, which are represented as bags of words (BoW). We propose a technique called cross-language concept matching (CLCM), to convert concept-based representations of documents from one language to another using Wikipedia correspondences between concepts in different languages and thus not relying on automated full-text translations. We describe two proposals: the first proposal consists in the use of the WikiBoC representation in conjunction with the CLCM technique (WikiBoC-CLCM) to classify documents written in a language L1 by using a SVM algorithm that was trained with documents written in another language L2; the second proposal consists of a hybrid model for representing documents that combines WikiBoC-CLCM with the classic BoW-MT approach. To evaluate the two proposals we conducted several experiments with three cross-lingual corpora: the JRC-Acquis corpus and two purpose-built corpora composed of Wikipedia articles. The first proposal outperforms state-of-the-art approaches when training sequences are short, achieving performance increases up to 233.33%. The second proposal outperforms state-of-the-art approaches in the whole range of training sequences, achieving performance increases up to 23.78%. Results obtained show the benefits of the WikiBoC-CLCM approach, since concepts extracted from documents add useful information to the classifier, thus improving its performance.

Concept-based item representations for a cross-lingual content-based recommendation process

The growth of the Web is the most influential factor that contributes to the increasing importance of text retrieval and filtering systems. On one hand, the Web is becoming more and more multilingual, and on the other hand users themselves are becoming increasingly polyglot. In this context, platforms for intelligent information access as search engines or recommender systems need to evolve to deal with this increasing amount of multilingual information. This paper proposes a content-based recommender system able to generate cross-lingual recommendations. The idea is to exploit user preferences learned in a given language, to suggest item in another language. The main intuition behind the work is that, differently from keywords which are inherently language dependent, concepts are stable across different languages, allowing to deal with multilingual and cross-lingual scenarios. We propose four knowledge-based strategies to build concept-based representation of items, by relying on the knowledge contained in two knowledge sources, i.e. Wikipedia and BabelNet. We learn user profiles by leveraging the different concept-based representations, in order to define a cross-lingual recommendation process. The empirical evaluation carried out on two state of the art datasets, DBbook and Movielens, shows that concept-based approaches are suitable to provide cross-lingual recommendations, even though there is not a clear advantage of using one of the different proposed representations. However, it emerges that most of the times the approaches based on BabelNet outperform those based on Wikipedia, which clearly shows the advantage of using a native multilingual knowledge source.

Graph-based Methods for Significant Concept Selection

It is well known in information retrieval area that one important issue is the gap between the query and document vocabularies. Concept-based representation of both the document and the query is one of the most effective approaches that lowers the effect of text mismatch and allows the selection of relevant documents that deal with the shared semantics hidden behind both. However, identifying the best representative concepts from texts is still challenging. In this paper, we propose a graph-based method to select the most significant concepts to be integrated into a conceptual indexing system. More specifically, we build the graph whose nodes represented concepts and weighted edges represent semantic distances. The importance of concepts are computed using centrality algorithms that levrage between structural and contextual importance. We experimentally evaluated our method of concept selection using the standard ImageClef2009 medical data set. Results showed that our approach significantly improves the retrieval effectiveness in comparison to state-of-the-art retrieval models.

Semantic Search as Inference

In this thesis, we present models for semantic search: Information Retrieval (IR) models that elicit the meaning behind the words found in documents and queries rather than simply matching keywords. This is achieved by the integration of structured domain knowledge and data-driven information retrieval methods. The research is set within health informatics to tackle the unique challenges within this domain; specifically, how to bridge the 'semantic gap'; that is, how to overcome the mismatch between raw medical data and the way human beings interpret it. Bridging the semantic gap involves addressing two issues: semantics; that is, aligning the meaning or concepts behind words found in documents and queries; and leveraging inference, which utilises semantics to infer relevant information. Three semantic search models -- all utilising concept-based rather than term-based representations---are developed; these include: the Bag-of-concepts model, which utilises concepts from the SNOMED CT medical ontology as its underlying representation; the Graph-based Concept Weighting model, which captures concept dependence and importance in a novel weighting function; and the core contribution of the thesis, the Graph INference model (GIN): a unified theoretical model of semantic search as inference, achieved by the integration of structured domain knowledge (ontologies) and statistical, information retrieval methods. It is the GIN that provides the necessary mechanism for inference to bridge the semantic gap. All three models are empirically evaluated using clinical queries and a real-world collection of clinical records taken from the TREC Medical Records Track (MedTrack). Our evaluation shows that the use of concept-based representations in the Bag-of-concepts model leads to improved retrieval effectiveness. When concepts are combined within the Graph-based ConceptWeighting model, further improvements are possible. The evaluation of GIN highlighted that its inference mechanism is suited to hard queries -- those that perform poorly on a term-based system. In-depth analysis also revealed that the GIN returned many new documents not retrieved by term-based systems and therefore never evaluated for relevance as part of the TREC MedTrack. This highlights that using current IR test collections, where semantic search systems did not contribute to the pool, may underestimate the effectiveness of semantic search systems. This work represents a significant step forward in the integration of structured domain knowledge and data-driven information retrieval methods. Furthermore, the thesis provides an understanding of inference -- when and how it should be applied for effective semantic search. It shows that queries with certain characteristics benefit from inference, while others do not. The detailed investigation into the evaluation of semantic search systems shows how current IR test collections may underestimate effectiveness of such systems and new techniques for evaluation are suggested. The Graph Inference model, although developed within the medical domain, is generally defined and has implications in other areas, including web search, where an emerging research trend is to utilise structured knowledge resources for more effective semantic search.

Semantic grounding of social annotations for enhancing resource classification in folksonomies

User-generated annotations in tagging or bookmarking sites such as Flickr or Delicious can provide a promising and interesting source of information for aiding tasks such as Web resource classification. However, the use of tags brings up some challenges. Since there are no constraints on the terms that can be used for tagging, noise and ambiguity are introduced when users annotate resources. Moreover, traditional bag-of-words representations ignore connections between terms and, thus, are affected by synonymity and hyponymia. Althougth tag-based representations are a valuable source for classifying resources, the problems associated with the unsupervised nature of tags may hinder classification results. This paper presents an approach for semantically analysing social annotations in order to attain enriched concept-based representations of Web resources. Representations are enriched with concepts extracted from WordNet and Wikipedia to overcome problems caused by natural language as well as enhancing the quality of information available for performing an effective classification of resources. Several strategies for tag pre-processing, concept disambiguation and incorporation of semantic entities to representations are discussed and evaluated in this paper. Experimental results showed that the strategies proposed to associate tags with conceptual entities allow improving resource classification results, outperforming traditional approaches based on bag-of-words representations.

Mining Conceptual Relations from Textual Web Content Using Leximancer

Concept mining is a process that focuses on extracting ideas and concepts found in documents. The approach is somewhat similar to text mining, with the main difference being that mining a text focuses on the extraction of information rather than ideas. In this paper, we propose concept-based text representation, with an accent on using the proposed representation in different applications such as information retrieval, text summarization, and question answering. This work presents a new prototype for concept mining by extracting the concept-based information from a raw text using leximancer. At the text representation level, we introduce a sentence based conceptual ontological representation that builds concept-based representations for the whole document. A new concept-based similarity measure is proposed to measure the similarity of texts based on their meaning. The proposed approach is domain independent and it could be applied to general domain applications. The proposed approach is going to apply to the domain of information retrieval, and give an assertion for proceeding in the right directions of this research.

The uncertain representation ranking framework for concept-based video retrieval

Concept based video retrieval often relies on imperfect and uncertain concept detectors. We propose a general ranking framework to define effective and robust ranking functions, through explicitly addressing detector uncertainty. It can cope with multiple concept-based representations per video segment and it allows the re-use of effective text retrieval functions which are defined on similar representations. The final ranking status value is a weighted combination of two components: the expected score of the possible scores, which represents the risk-neutral choice, and the scores’ standard deviation, which represents the risk or opportunity that the score for the actual representation is higher. The framework consistently improves the search performance in the shot retrieval task and the segment retrieval task over several baselines in five TRECVid collections and two collections which use simulated detectors of varying performance.

Proof of concept

In this thesis we investigate the possibility to integrate domain-specific knowledge into biomedical information retrieval (IR). Recent decades have shown a fast growing interest in biomedical research, reflected by an exponential growth in scientific literature. An important problem for biomedical IR is dealing with the complex and inconsistent terminology encountered in biomedical publications. Dealing with the terminology problem requires domain knowledge stored in terminological resources: controlled indexing vocabularies and thesauri. The integration of this knowledge is, however, far from trivial. The first research theme investigates heuristics for obtaining word-based representations from biomedical text for robust retrieval. We investigated the effect of choices in document preprocessing heuristics on retrieval effectiveness. Document preprocessing heuristics such as stop word removal, stemming, and breakpoint identification and normalization were shown to strongly affect retrieval performance. An effective combination of heuristics was identified to obtain a word-based representation from text for the remainder of this thesis. The second research theme deals with concept-based retrieval. We compared a word-based to a concept-based representation and determined to what extent a manual concept-based representation can be automatically obtained from text. Retrieval based on only concepts was demonstrated to be significantly less effective than word-based retrieval. This deteriorated performance could be explained by errors in the classification process, limitations of the concept vocabularies and limited exhaustiveness of the concept-based document representations. Retrieval based on a combination of word-based and automatically obtained concept-based query representations did significantly improve word-only retrieval. In the third and last research theme we propose a cross-lingual framework for monolingual biomedical IR. In this framework, the integration of a concept-based representation is viewed as a cross-lingual matching problem involving a word-based and concept-based representation language. This framework gives us the opportunity to adopt a large set of established crosslingual information retrieval methods and techniques for this domain. Experiments with basic term-to-term translation models demonstrate that this approach can significantly improve word-based retrieval. Directions for future work are using these concepts for communication between user and retrieval system, extending upon the translation models and extending CLIR-enhanced concept-based retrieval outside the biomedical domain. Available online from http://purl.utwente.nl/publications/72481.

Opinion comparison between internet forums and customer reviews

The advent of the internet marks a new source of customer feedbacks for product designers to gain valuable insights into their outputs. The paper investigates the distinctive characteristics of customer opinions found on internet forums. A comparison of the similarities and differences between customer reviews and forum feedbacks is given. Finally, data mining techniques based on concept-based representations are used to analyse feedback from forums.

A pilot study of contextual UMLS indexing to improve the precision of concept-based representation in XML-structured clinical radiology reports

ObjectiveDespite the advantages of structured data entry, much of the patient record is still stored as unstructured or semistructured narrative text. The issue of representing clinical document content remains problematic. The authors' prior work using an automated UMLS document indexing system has been encouraging but has been affected by the generally low indexing precision of such systems. In an effort to improve precision, the authors have developed a context-sensitive document indexing model to calculate the optimal subset of UMLS source vocabularies used to index each document section. This pilot study was performed to evaluate the utility of this indexing approach on a set of clinical radiology reports. DesignA set of clinical radiology reports that had been indexed manually using UMLS concept descriptors was indexed automatically by the SAPHIRE indexing engine. Using the data generated by this process the authors developed a system that simulated indexing, at the document section level, of the same document set using many permutations of a subset of the UMLS constituent vocabularies. MeasurementsThe precision and recall scores generated by simulated indexing for each permutation of two or three UMLS constituent vocabularies were determined. ResultsWhile there was considerable variation in precision and recall values across the different subtypes of radiology reports, the overall effect of this indexing strategy using the best combination of two or three UMLS constituent vocabularies was an improvement in precision without significant impact of recall. ConclusionIn this pilot study a contextual indexing strategy improved overall precision in a set of clinical radiology reports.

Consistency-driven knowledge elicitation: using a learning-oriented knowledge representation that supports knowledge elicitation in NeoDISCIPLE

A general approach to knowledge elicitation in interactive learning systems is presented which both improves a knowledge base by removing inconsistencies and extends the representation space for learning. This approach addresses the problem of learning "new terms" with interactive learning systems. Two methods that illustrate this approach are implemented in the learning apprentice system NeoDISCIPLE, using a concept-based representation that is very appropriate for learning. At the same time, the representation facilitates knowledge elicitation associated with human-oriented representations like, for instance, repertory grids. Both methods are consistency-driven in that they elicit knowledge from a human expert in order to remove inconsistencies in the knowledge pieces learned by NeoDISCIPLE. The input to these methods is an inconsistent rule learned by NeoDISCIPLE, together with the examples from which the rule has been learned. The elicitation process is characterized by a guided interaction with the human expert, who is asked to make relevant distinctions pertaining to concepts appearing in the positive and negative examples of the rule. The first method elicits concept properties through a goal-driven property transfer from one concept to another, and the second one elicits concepts using a goal-driven conceptual clustering. In both cases the elicited knowledge is used to improve the inconsistent rule while simultaneously extending the representation space for learning.