Knowledge Search within a Company-WIKI

The usage of Wikis for the purpose of knowledge management within a business company is only of value if the stored information can be found easily. The fundamental characteristic of a Wiki, its easy and informal usage, results in large amounts of steadily changing, unstructured documents. The widely used full-text search often provides search results of insufficient accuracy. In this paper, we will present an approach likely to improve search quality, through the use of Semantic Web, Text Mining, and Case Based Reasoning (CBR) technologies. Search results are more precise and complete because, in contrast to full-text search, the proposed knowledge-based search operates on the semantic layer

An empirical study of inter-concept similarities in multimedia ontologies

Generic concept detection has been a widely studied topic in recent research on multimedia analysis and retrieval, but the issue of how to exploit the structure of a multimedia ontology as well as different inter-concept relations, has not received similar attention. In this paper, we present results from our empirical analysis of different types of similarity among semantic concepts in two multimedia ontologies, LSCOM-Lite and CDVP-206. The results show promise that the proposed methods may be helpful in providing insight into the existing inter-concept relations within an ontology and selecting the most facilitating set of concepts and hierarchical relations. Such an analysis as this can be utilized in various tasks such as building more reliable concept detectors and designing large-scale ontologies

Doctor of Philosophy

dissertationBiomedical data are a rich source of information and knowledge. Not only are they useful for direct patient care, but they may also offer answers to important population-based questions. Creating an environment where advanced analytics can be performed against biomedical data is nontrivial, however. Biomedical data are currently scattered across multiple systems with heterogeneous data, and integrating these data is a bigger task than humans can realistically do by hand; therefore, automatic biomedical data integration is highly desirable but has never been fully achieved. This dissertation introduces new algorithms that were devised to support automatic and semiautomatic integration of heterogeneous biomedical data. The new algorithms incorporate both data mining and biomedical informatics techniques to create "concept bags" that are used to compute similarity between data elements in the same way that "word bags" are compared in data mining. Concept bags are composed of controlled medical vocabulary concept codes that are extracted from text using named-entity recognition software. To test the new algorithm, three biomedical text similarity use cases were examined: automatically aligning data elements between heterogeneous data sets, determining degrees of similarity between medical terms using a published benchmark, and determining similarity between ICU discharge summaries. The method is highly configurable and 5 different versions were tested. The concept bag method performed particularly well aligning data elements and outperformed the compared algorithms by iv more than 5%. Another configuration that included hierarchical semantics performed particularly well at matching medical terms, meeting or exceeding 30 of 31 other published results using the same benchmark. Results for the third scenario of computing ICU discharge summary similarity were less successful. Correlations between multiple methods were low, including between terminologists. The concept bag algorithms performed consistently and comparatively well and appear to be viable options for multiple scenarios. New applications of the method and ideas for improving the algorithm are being discussed for future work, including several performance enhancements, configuration-based enhancements, and concept vector weighting using the TF-IDF formulas

One-Shot Labeling for Automatic Relevance Estimation

Dealing with unjudged documents ("holes") in relevance assessments is a perennial problem when evaluating search systems with offline experiments. Holes can reduce the apparent effectiveness of retrieval systems during evaluation and introduce biases in models trained with incomplete data. In this work, we explore whether large language models can help us fill such holes to improve offline evaluations. We examine an extreme, albeit common, evaluation setting wherein only a single known relevant document per query is available for evaluation. We then explore various approaches for predicting the relevance of unjudged documents with respect to a query and the known relevant document, including nearest neighbor, supervised, and prompting techniques. We find that although the predictions of these One-Shot Labelers (1SL) frequently disagree with human assessments, the labels they produce yield a far more reliable ranking of systems than the single labels do alone. Specifically, the strongest approaches can consistently reach system ranking correlations of over 0.86 with the full rankings over a variety of measures. Meanwhile, the approach substantially increases the reliability of t-tests due to filling holes in relevance assessments, giving researchers more confidence in results they find to be significant. Alongside this work, we release an easy-to-use software package to enable the use of 1SL for evaluation of other ad-hoc collections or systems.Comment: SIGIR 202

X-IM Framework to Overcome Semantic Heterogeneity Across XBRL Filings

Semantic heterogeneity in XBRL precludes the full automation of the business reporting pipeline, a key motivation for the SEC’s XBRL mandate. To mitigate this problem, several approaches leveraging Semantic Web technologies have emerged. While some approaches are promising, their mapping accuracy in resolving semantic heterogeneity must be improved to realize the promised benefits of XBRL. Considering this limitation and following the design science research methodology (DSRM), we develop a novel framework, XBRL indexing-based mapping (X-IM), which takes advantage of the representational model of representation theory to map heterogeneous XBRL elements across diverse XBRL filings. The application of representation theory to the design process informs the use of XBRL label linkbases as a repository of regularities constitutive of the relationships between financial item names and the concepts they describe along a set of equivalent financial terms of interest to investors. The instantiated design artifact is thoroughly evaluated using standard information retrieval metrics. Our experiments show that X-IM significantly outperforms existing methods

Semantic multimedia modelling & interpretation for annotation

The emergence of multimedia enabled devices, particularly the incorporation of cameras in mobile phones, and the accelerated revolutions in the low cost storage devices, boosts the multimedia data production rate drastically. Witnessing such an iniquitousness of digital images and videos, the research community has been projecting the issue of its significant utilization and management. Stored in monumental multimedia corpora, digital data need to be retrieved and organized in an intelligent way, leaning on the rich semantics involved. The utilization of these image and video collections demands proficient image and video annotation and retrieval techniques. Recently, the multimedia research community is progressively veering its emphasis to the personalization of these media. The main impediment in the image and video analysis is the semantic gap, which is the discrepancy among a user’s high-level interpretation of an image and the video and the low level computational interpretation of it. Content-based image and video annotation systems are remarkably susceptible to the semantic gap due to their reliance on low-level visual features for delineating semantically rich image and video contents. However, the fact is that the visual similarity is not semantic similarity, so there is a demand to break through this dilemma through an alternative way. The semantic gap can be narrowed by counting high-level and user-generated information in the annotation. High-level descriptions of images and or videos are more proficient of capturing the semantic meaning of multimedia content, but it is not always applicable to collect this information. It is commonly agreed that the problem of high level semantic annotation of multimedia is still far from being answered. This dissertation puts forward approaches for intelligent multimedia semantic extraction for high level annotation. This dissertation intends to bridge the gap between the visual features and semantics. It proposes a framework for annotation enhancement and refinement for the object/concept annotated images and videos datasets. The entire theme is to first purify the datasets from noisy keyword and then expand the concepts lexically and commonsensical to fill the vocabulary and lexical gap to achieve high level semantics for the corpus. This dissertation also explored a novel approach for high level semantic (HLS) propagation through the images corpora. The HLS propagation takes the advantages of the semantic intensity (SI), which is the concept dominancy factor in the image and annotation based semantic similarity of the images. As we are aware of the fact that the image is the combination of various concepts and among the list of concepts some of them are more dominant then the other, while semantic similarity of the images are based on the SI and concept semantic similarity among the pair of images. Moreover, the HLS exploits the clustering techniques to group similar images, where a single effort of the human experts to assign high level semantic to a randomly selected image and propagate to other images through clustering. The investigation has been made on the LabelMe image and LabelMe video dataset. Experiments exhibit that the proposed approaches perform a noticeable improvement towards bridging the semantic gap and reveal that our proposed system outperforms the traditional systems

Overview of the gene ontology task at BioCreative IV

Gene Ontology (GO) annotation is a common task among model organism databases (MODs) for capturing gene function data from journal articles. It is a time-consuming and labor-intensive task, and is thus often considered as one of the bottlenecks in literature curation. There is a growing need for semiautomated or fully automated GO curation techniques that will help database curators to rapidly and accurately identify gene function information in full-length articles. Despite multiple attempts in the past, few studies have proven to be useful with regard to assisting real-world GO curation. The shortage of sentence-level training data and opportunities for interaction between text-mining developers and GO curators has limited the advances in algorithm development and corresponding use in practical circumstances. To this end, we organized a text-mining challenge task for literature-based GO annotation in BioCreative IV. More specifically, we developed two subtasks: (i) to automatically locate text passages that contain GO-relevant information (a text retrieval task) and (ii) to automatically identify relevant GO terms for the genes in a given article (a concept-recognition task). With the support from five MODs, we provided teams with >4000 unique text passages that served as the basis for each GO annotation in our task data. Such evidence text information has long been recognized as critical for text-mining algorithm development but was never made available because of the high cost of curation. In total, seven teams participated in the challenge task. From the team results, we conclude that the state of the art in automatically mining GO terms from literature has improved over the past decade while much progress is still needed for computer-assisted GO curation. Future work should focus on addressing remaining technical challenges for improved performance of automatic GO concept recognition and incorporating practical benefits of text-mining tools into real-world GO annotation

Developing HL7 CDA-Based Data Warehouse for the Use of Electronic Health Record Data for Secondary Purposes

Background The growing availability of clinical and administrative data collected in electronic health records (EHRs) have led researchers and policy makers to implement data warehouses to improve the reuse of EHR data for secondary purposes. This approach can take advantages from a unique source of information that collects data from providers across multiple organizations. Moreover, the development of a data warehouse benefits from the standards adopted to exchange data provided by heterogeneous systems. Objective This article aims to design and implement a conceptual framework that semiautomatically extracts information collected in Health Level 7 Clinical Document Architecture (CDA) documents stored in an EHR and transforms them to be loaded in a target data warehouse. Results The solution adopted in this article supports the integration of the EHR as an operational data store in a data warehouse infrastructure. Moreover, data structure of EHR clinical documents and the data warehouse modeling schemas are analyzed to define a semiautomatic framework that maps the primitives of the CDA with the concepts of the dimensional model. The case study successfully tests this approach. Conclusion The proposed solution guarantees data quality using structured documents already integrated in a large-scale infrastructure, with a timely updated information flow. It ensures data integrity and consistency and has the advantage to be based on a sample size that covers a broad target population. Moreover, the use of CDAs simplifies the definition of extract, transform, and load tools through the adoption of a conceptual framework that load the information stored in the CDA in the data warehouse

Information Retrieval-Based Optimization Approaches for Requirement Traceability Recovery

Requirements traceability provides support for important software engineering activities. Requirements traceability recovery (RTR) is becoming increasingly important due to the numerous benefits to the overall quality of software. Improving the RTR problem has become an active topic of research for software engineers; researchers have proposed a number of approaches for improving and automating RTR across the requirements and the source code of the system. Textual analysis and Information Retrieval (IR) techniques have been applied to the RTR problem for many years; however, most of the existing IR-based methodologies applied to the RTR problem are semiautomatic or time-consuming, even though many links are correctly recovered using IR. Thus, there is a need for effective and innovative approaches for automatization in the RTR problem. In this research, we study IR techniques applied to the RTR problem to determine the optimal alternative to RTR across the textual content of requirements and system source code, and propose innovative methodologies based on computational intelligence combine with IR to achieve automatization. We approach the study of the RTR problem as an optimization problem; the problem is formulated as a multi or mono objective search in which we assign one-to-many relationships between each requirement and source code classes by considering similarity in their textual content. The Non-dominated Sorting Genetic Algorithm (NSGA-II) and Artificial Bee Colony (ABC), when combined with IR techniques, appear to provide promising alternatives for finding a complete and accurate list of traceability links. We adapt the NGSA-II and ABC algorithms to solve the RTR problem, generate programing tools for experimentation, and report the results on three open source projects. Results show values of precision and recall above 70%. NSGA-II and ABC are also analyzed based on time complexity using the big-O notation; results indicate NSGA-II is more time efficient and less precise than ABC