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Studies on the relationship between education and employability are of paramount importance for policy makers, training institutions, companies and students. The availability of large scale data on the Internet, such as online job ads, has been leveraged to better investigate this relationship. Recent studies have shown that representing this data using knowledge graphs can be useful in identifying the job market needs and establishing better assessment methods. Such studies also highlight the skill mismatch between education and the job market and help mitigate it. In this paper, we provide a comprehensive review of the applications of knowledge graphs in education and employability. We review knowledge graph based frameworks that have been proposed in previous work on different aspects of education and employability. The survey introduces new taxonomies for these applications and their methodologies, and provides a thorough outlook on promising research directions. We also present a use case to illustrate how knowledge graphs can be used to investigate the relationship between education and the job market.

Abstract Objectives Recently natural language interfaces (e.g., chatbots) have gained enormous attention. Such interfaces execute underlying application programming interfaces (APIs) based on the user's utterances to perform tasks (e.g., reporting weather). Supervised approaches for building such interfaces rely upon a large set of user utterances paired with APIs. Collecting such pairs is typically starts with obtaining initial utterances for a given API method. Generating initial utterances can be considered as a machine translation task in which an API method is translated into an utterance. However, the key challenge is the lack of training samples for training domain-independent translation models. In this paper, we propose a dataset for training supervised models to generate initial utterances for APIs. Data description The dataset contains 14,370 pairs of API methods and utterances. It is built automatically by converting method descriptions of a large number of APIs to user utterances; and it is cleaned manually to ensure quality. The dataset is also accompanied with a set of microservices (e.g., translating API methods to utterances) which can facilitate the process of collecting training samples for building natural language interfaces.

Abstract Rule-based systems have been used increasingly to augment learning algorithms for annotating data. Rules alleviate many of the shortcomings inherent in pure algorithmic approaches, in cases algorithms are not working well or lack from enough training data. However, in dynamic curation environments where data are constantly changing, there is a need to craft and adapt rules to keep them applicable and precise. Rule adaptation has been proven to be painstakingly difficult and error-prone, as an analyst is needed for examining the precision of rules and applying different modifications to adapt the imprecise ones. In this paper, we present an autonomic and conceptual approach to adapt data annotation rules. Our approach offloads analysts from adapting rules; it boosts rules to annotate a larger number of items using a set of high-level conceptual features, e.g. topic. We utilize a Bayesian multi-armed-bandit algorithm, an online learning algorithm that adapts rules based on the feedback collects from the curation environment over time. We propose a summarization technique, which offers a set of high-level conceptual features for annotating items by identifying the semantical relationships among them. We conduct experiments on different curation domains and compare the performance of our approach with systems relying on analysts for adapting rules. The experimental results show that our approach has a comparative performance to analysts in adapting rules.

Abstract Objectives Text classification is a recurrent goal in machine learning projects and a typical task in crowdsourcing platforms. Hybrid approaches, leveraging crowdsourcing and machine learning, work better than either in isolation and help to reduce crowdsourcing costs. One way to mix crowd and machine efforts is to have algorithms highlight passages from texts and feed these to the crowd for classification. In this paper, we present a dataset to study text highlighting generation and its impact on document classification. Data description The dataset was created through two series of experiments where we first asked workers to (i) classify documents according to a relevance question and to highlight parts of the text that supported their decision, and on a second phase, (ii) to assess document relevance but supported by text highlighting of varying quality (six human-generated and six machine-generated highlighting conditions). The dataset features documents from two application domains: systematic literature reviews and product reviews, three document sizes, and three relevance questions of different levels of difficulty. We expect this dataset of 27,711 individual judgments from 1851 workers to benefit not only this specific problem domain, but the larger class of classification problems where crowdsourced datasets with individual judgments are scarce.

Application Programming Interface (API) is a core technology that facilitates developers’ productivity by enabling the reuse of software components. Understanding APIs and gaining knowledge about their usage are therefore fundamental needs for developers. Here, API documentation plays a pivotal role in enabling developers to take full advantage of the benefits brought by APIs. The quality of API documentation has therefore become an important concern given the celerity and dynamics at which APIs are now being made available to users. This article aims at exploring existing research in the area of API documentation in order to identify the associated quality dimensions addressed by the literature. The research is carried out as a systematic mapping study where 103 research papers selected from the literature were reviewed and a total of 5 core quality dimensions were identified and analyzed. By focusing on the two most relevant quality dimensions (understandability and completeness), this article presents an approach to enable API users to explore, discover and learn about APIs through API topic issues discussed in Stack Overflow (SO). We demonstrate the feasibility of our approach through Scout-bot, our tool for exploration and discovery of API topic issues.

Business processes represent a cornerstone to the operation of any enterprise. They are the operational means for such organizations to fulfill their goals. Nowadays, enterprises are able to gather massive amounts of event data. These are generated as business processes are executed and stored in transaction logs, databases, e-mail correspondences, free form text on (enterprise) social media, and so on. Taping into these data, enterprises would like to weave data analytic techniques into their decision making capabilities. In recent years, the IT industry has witnessed significant advancements in the domain of Big Data analytics. Unfortunately, the business process management (BPM) community has not kept up to speed with such developments and often rely merely on traditional modeling-based approaches. New ways of effectively exploiting such data are not sufficiently used. In this paper, we advocate that a good understanding of the business process and Big Data worlds can play an effective role in improving the efficiency and the quality of various data-intensive business operations using a wide spectrum of emerging Big Data systems. Moreover, we coin the term process footprint as a wider notion of process data than that is currently perceived in the BPM community. A roadmap towards taking business process data intensive operations to the next level is shaped in this paper.

Cloud computing has emerged as a powerful paradigm for delivering data-intensive services over the Internet. Cloud computing has enabled the implementation and success of big data, a recent phenomenon handling huge data being generated from different sources. Competing clouds have made it challenging to select a cloud provider that guarantees quality of cloud service (QoCS). Also, cloud providers' claims of guaranteeing QoCS are exaggerated for marketing purposes; hence, they cannot often be trusted. Therefore, a comprehensive trust model is necessary to evaluate the QoCS prior to making any selection decision. In this paper, we propose a multi-dimensional trust model for big data workflow processing over different clouds. It evaluates the trustworthiness of cloud providers based on: the most up-to-date cloud resource capabilities, the reputation evidence measured by neighboring users, and a recorded personal history of experiences with the cloud provider. The ultimate goal is to ensure an efficient selection of trustworthiness cloud provider who eventually will guarantee high QoCS and fulfills key big data workflow requirements. Various experiments were conducted to validate our proposed model. The results show that our model captures the different components of trust, ensures high QoCS, and effectively adapts to the dynamic nature of the cloud.