Information and Knowledge Retrieval within Software Projects and their Graphical Representation for Collaborative Programming

With the ever-increasing growth of data and information, finding the appropriate knowledge becomes a real challenge and an urgent task. Traditional data and information retrieval systems that support the current web are no longer adequate for knowledge seeking tasks. Knowledge retrieval systems will be the next generation of retrieval systems to serve those purposes. However, the key problem becomes how to construct the basic unit of knowledge retrieval, crossing from data retrieval, information retrieval to knowledge retrieval. From the perspective of knowledge presentation and user needs, the knowledge is a category with context. This paper proposes a semantic text deep mining based on knowledge element. The basic unit of knowledge retrieval and the semantic triangle model of knowledge element are discussed. Application of semantic triangle of knowledge element is given by an example of mining electronic medical records. Experimental results verify the validity and feasibility of the design scheme.

The drastic growth of coastal observation sensors results in copious data that provide weather information. The intricacies in sensor-generated big data are heterogeneity and interpretation, driving high-end Information Retrieval (IR) systems. The Semantic Web (SW) can solve this issue by integrating data into a single platform for information exchange and knowledge retrieval. This paper focuses on exploiting the SW base system to provide interoperability through ontologies by combining the data concepts with ontology classes. This paper presents a 4-phase weather data model: data processing, ontology creation, SW processing, and query engine. The developed Oceanographic Weather Ontology helps to enhance data analysis, discovery, IR, and decision making. In addition to that, it also evaluates the developed ontology with other state-of-the-art ontologies. The proposed ontology’s quality has improved by 39.28% in terms of completeness, and structural complexity has decreased by 45.29%, 11% and 37.7% in Precision and Accuracy. Indian Meteorological Satellite INSAT-3D’s ocean data is a typical example of testing the proposed model. The experimental result shows the effectiveness of the proposed data model and its advantages in machine understanding and IR.

Over the last decade, architecture has witnessed a growing popularity for new computational tools such as parametric design environments (PDEs). Given their rapid evolution and development, expertise tends to become increasingly transient, and architects find themselves in a situation where they must constantly re-learn their tools. At the same time, access to information has become increasingly widespread. Self-learners can thus rely on information retrieval systems to address knowledge gaps. However, the inherent tool complexity has given rise to a new kind of knowledge. On the basis of the different types described by Anderson and Krathwohl, the authors have previously shown that conceptual knowledge is essential for teaching parametric design. In contrast, research on interactive information retrieval (IIR) has highlighted that procedural knowledge is preferred in create tasks like design. Consequently, it can be argued that in a self-learning situation, architects might not be adopting best practice in relation to knowledge retrieval, especially when considering the visual scripting nature of certain PDEs. The purpose of this paper is to observe cognitive patterns in knowledge search activities while designing in parametric environments and validate the integration of CLT and IIR for further research. We highlight the types of knowledge and sources architecture graduate students, novices in PDEs, search for during design over multiple sessions and why. The paper reports on three design tasks completed during a computational course that emphasized student's autonomy. A qualitative analysis of interviews reveals epistemic actions to fall prey to procedural information, which is in line with both IIR and CLT research. This research is part of a PhD project studying the impact of knowledge retrieval on architectural design when using PDEs. Eventually, it could raise awareness in education, research, and practice regarding information retrieval in architectural design.

Advent of internet made abundant of medical data available in various formats. Emerging technologies like AI, big data, machine learning and sematic web are increasingly applied in various sectors. Healthcare is the essential field which requires technological support in order to administer healthcare workforce, to improve the quality of healthcare services in resource constrained region, to manage the chronic diseases and pandemics like Covid-19 and so on. The supply of precise data to the technical system plays a significant role in providing better healthcare support than the available unorganized data. With the help of ontologies, semantic web represents the conceptual relationship between data more precisely. The accurate semantic rule and query engine provides effective definition of rules and knowledge retrieval from ontologies. In the medical field ontologies are used to represent medical terms, interoperability between systems with lexically different but semantically similar terms. It enhances the reuse and sharing of medical information and patient data. Also increases the percentage of support to integration of data and knowledge. The proposed case study considered Covid-19 ontology with 158 classes and maximum depth of 12 levels. Information retrieval from the knowledgebase is done using Simple Protocol and RDF Query Language (SPARQL), and Semantic Web Rule Language (SWRL). Simple queries can be formulated using Natural language based template. Additional knowledge can also be generated from the available information. This chapter ensures the effective utilization of information thereby enrich interoperability, integration and information retrieval.

Arabic Legal Information Retrieval: The Impact of Morphological Segmentation and Semantic Embeddings

The automated task of locating the potential buggy files in a software project given a bug report is called bug localization. Bug localization helps developers focus on crucial files. However, the existing automated bug localization approaches face a key challenge, called lexical mismatch. Specifically, the terms used in bug reports to describe a bug are different from the terms and code tokens used in source files. To address that, we present a novel approach that uses deep neural network (DNN) in combination with rVSM, an information retrieval (IR) technique. rVSM collects the feature on the textual similarity between bug reports and source files. DNN is used to learn to relate the terms in bug reports to potentially different code tokens and terms in source files. Our empirical evaluation on real-world bug reports in the open-source projects shows that DNN and IR complement well to each other to achieve higher bug localization accuracy than individual models. Importantly, our new model, DNNLOC, with a combination of the features built from DNN, rVSM, and project's bug-fixing history, achieves higher accuracy than the state-of-the-art IR and machine learning techniques. In half of the cases, it iscorrect with just a single suggested file. In 66% of the time, acorrect buggy file is in the list of three suggested files. With 5 suggested files, it is correct in almost 70% of the cases.

Retrieval of legal knowledge by the general public is a challenging problem due to the technicality of the professional knowledge and the lack of fundamental understanding by laypersons on the subject. Traditional information retrieval techniques assume that users are capable of formulating succinct and precise queries for effective document retrieval. In practice, however, the wide gap between the highly technical contents and untrained users makes legal knowledge retrieval very difficult. We propose a methodology, called QBR, which employs a Questions Bank (QB) as an effective medium for bridging the knowledge gap. We show how the QB is used to derive training samples to enhance the embedding of knowledge units within documents, which leads to effective fine-grained knowledge retrieval. We discuss and evaluate through experiments various advantages of QBR over traditional methods. These include more accurate, efficient, and explainable document retrieval, better comprehension of retrieval results, and highly effective fine-grained knowledge retrieval. We also present some case studies and show that QBR achieves social impact by assisting citizens to resolve everyday legal concerns.

During the development of a software project, developers often need to upgrade third-party libraries (TPLs), aiming to keep their code up-to-date with the newest functionalities offered by the used libraries. In most cases, upgrading used TPLs is a complex and error-prone activity that must be carefully carried out to limit the ripple effects on the software project that depends on the libraries being upgraded. In this paper, we propose EvoPlan as a novel approach to the recommendation of different upgrade plans given a pair of library-version as input. In particular, among the different paths that can be possibly followed to upgrade the current library version to the desired updated one, EvoPlan is able to suggest the plan that can potentially minimize the efforts being needed to migrate the code of the clients from the library’s current release to the target one. The approach has been evaluated on a curated dataset using conventional metrics used in Information Retrieval, i.e., precision, recall, and F-measure. The experimental results show that EvoPlan obtains an encouraging prediction performance considering two different criteria in the plan specification, i.e., the popularity of migration paths and the number of open and closed issues in GitHub for those projects that have already followed the recommended migration paths.

Towards Automated Information Retrieval of Process Data and Knowledge from Academic Databases

The increased accessibility of data that computers provide has caused a range of expert and information-retrieval systems to be designed in all areas of knowledge, including corrosion knowledge. In that light, an analysis is made of corrosion information and what is needed in the process industry. It is shown that expert systems should be business- related, i.e. both the information storage and the retrieval method should be brought in close relation with the business of the users of the system. A new bussiness-related corrosion expert system provides expert knowledge about specific plants and equipment, has superior accessibility and can stay, as a dynamic system, comfortably ahead of the state-of-the-art.

The current processes of planning, design, construction management, and facility management are driven by the collection and communication of large quantities of knowledge, data, and intent, much of which are lost due to organizational fragmentation as well as to incompatible and inefficient information exchange media. There is a general lack of models for such intimate integration of all the relevant processes. The purpose of this paper is to present one such global model, patterned on the medieval master builder. The model proposes an open architecture and media for the representation, classification, capture, and real‐time retrieval of data, information, and knowledge in their various digital and graphic forms, to allow multiple participants to interactively access data from their perspectives. The key aspects of the virtual master builder (VMB) are described in terms of its information architecture, communication languages, software architecture, process and system components, and shareable knowledge ...

Complementary hemispheric asymmetries in object naming and recognition: A voxel-based correlational study

Terminologies are structured collections of designations (“terms”) that describe entities and relationships that represent the knowledge within a given domain.1,2 Terms may consist of words, phrases, or other notations (such as numbers or symbols), and are designed to support communication, storage, retrieval, and use of knowledge and information by humans and machines. An example is the clinical entity of “blood pressure measured during the diastolic phase of the cardiac cycle,” which is designated (in the terminology SNOMED CT) by the preferred term “Diastolic blood pressure” and the concept identifier “271650006.” Terminologies can formally define and specify representation of information content, and when used with messaging standards, can support structured information exchange among different electronic patient care systems. Terminologies have been developed with differing levels of rigor, and best practices have been described.2,8

Marine incident reports available from official marine bodies contain a wealth of information about the type, causes of incident, and details on the sequence of events that lead to the incident. Knowledge discovery and retrieval of useful information through content analysis and text mining from such reports will help us to understand the primary/secondary causes for the incidents and establish the associative relationship between the causal factors and the type of incidents. In this work, we used the marine incident reports obtained from the ‘National Transportation Safety Board, US’. These reports were classified as per the incident type and the primary/secondary causes of the incidents were identified. In addition, heat map(s) to establish the relationship between the primary/secondary causes with respect to the incident type were generated. Causal factors and associative relationships that were not apparent in earlier studies were also identified using the proposed methodology. Additional information on the incidents was also collected to list down the lessons learnt from the historical events so as to formulate the recommendations for mariners to aid their future navigation.

This paper describes a new approach to solving jigsaw puzzles. This approach facilitates the notion of matching the shape and image of jigsaw puzzle pieces. It relies on human intelligence and knowledge retrieval of information by "looking" at a piece of a jigsaw puzzle. The method challenges the combinatorial and geometrical problem of solving jigsaw puzzles and can be used for further challenging 2D reconstruction of spatial pieces, such as broken glass or parts