Combining data mining and ontology engineering to enrich ontologies and linked data

In this position paper, we claim that the need for time consuming data preparation and result interpretation tasks in knowledge discovery, as well as for costly expert consultation and consensus building activities required for ontology building can be reduced through exploiting the interplay of data mining and ontology engineering. The aim is to obtain in a semi-automatic way new knowledge from distributed data sources that can be used for inference and reasoning, as well as to guide the extraction of further knowledge from these data sources. The proposed approach is based on the creation of a novel knowledge discovery method relying on the combination, through an iterative ?feedbackloop?, of (a) data mining techniques to make emerge implicit models from data and (b) pattern-based ontology engineering to capture these models in reusable, conceptual and inferable artefacts

Boundary heat diffusion classifier for a semi-supervised learning in a multilayer network embedding

International audienceThe scarcity of high-quality annotations in many application scenarios has recently led to an increasing interest in devising learning techniques that combine unlabeled data with labeled data in a network. In this work, we focus on the label propagation problem in multilayer networks. Our approach is inspired by the heat diffusion model, which shows usefulness in machine learning problems such as classification and dimensionality reduction. We propose a novel boundary-based heat diffusion algorithm that guarantees a closed-form solution with an efficient implementation. We experimentally validated our method on synthetic networks and five real-world multilayer network datasets representing scientific coauthorship, spreading drug adoption among physicians, two bibliographic networks, and a movie network. The results demonstrate the benefits of the proposed algorithm, where our boundary-based heat diffusion dominates the performance of the state-of-the-art methods

Crowdsourcing Linked Data on listening experiences through reuse and enhancement of library data

Research has approached the practice of musical reception in a multitude of ways, such as the analysis of professional critique, sales figures and psychological processes activated by the act of listening. Studies in the Humanities, on the other hand, have been hindered by the lack of structured evidence of actual experiences of listening as reported by the listeners themselves, a concern that was voiced since the early Web era. It was however assumed that such evidence existed, albeit in pure textual form, but could not be leveraged until it was digitised and aggregated. The Listening Experience Database (LED) responds to this research need by providing a centralised hub for evidence of listening in the literature. Not only does LED support search and reuse across nearly 10,000 records, but it also provides machine-readable structured data of the knowledge around the contexts of listening. To take advantage of the mass of formal knowledge that already exists on the Web concerning these contexts, the entire framework adopts Linked Data principles and technologies. This also allows LED to directly reuse open data from the British Library for the source documentation that is already published. Reused data are re-published as open data with enhancements obtained by expanding over the model of the original data, such as the partitioning of published books and collections into individual stand-alone documents. The database was populated through crowdsourcing and seamlessly incorporates data reuse from the very early data entry phases. As the sources of the evidence often contain vague, fragmentary of uncertain information, facilities were put in place to generate structured data out of such fuzziness. Alongside elaborating on these functionalities, this article provides insights into the most recent features of the latest instalment of the dataset and portal, such as the interlinking with the MusicBrainz database, the relaxation of geographical input constraints through text mining, and the plotting of key locations in an interactive geographical browser

Facilitating scientometrics in learning analytics and educational data mining - The LAK dataset

The Learning Analytics and Knowledge (LAK) Dataset represents an unprecedented corpus which exposes a near complete collection of bibliographic resources for a specific research discipline, namely the connected areas of Learning Analytics and Educational Data Mining. Covering over five years of scientific literature from the most relevant conferences and journals, the dataset provides Linked Data about bibliographic metadata as well as full text of the paper body. The latter was enabled through special licensing agreements with ACM for publications not yet available through open access. The dataset has been designed following established Linked Data pattern, reusing established vocabularies and providing links to established schemas and entity coreferences in related datasets. Given the temporal and topic coverage of the dataset, being a near-complete corpus of research publications of a particular discipline, it facilitates scientometric investigations, for instance, about the evolution of a scientific field over time, or correlations with other disciplines, what is documented through its usage in a wide range of scientific studies and applications.peer-reviewe

Extracting data models from background knowledge graphs

International audienceKnowledge Graphs have emerged as a core technology to aggregate and publish knowledge on the Web. However, integrating knowledge from different sources, not specifically designed to be interoperable, is not a trivial task. Finding the right ontologies to model a dataset is a challenge since several valid data models exist and there is no clear agreement between them. In this paper, we propose to facilitate the selection of a data model with the RICDaM (Recommending Interoperable and Consistent Data Models) framework. RICDaM generates and ranks candidates that match entity types and properties in an input dataset. These candidates are obtained by aggregating freely available domain RDF datasets in a knowledge graph and then enriching the relationships between the graph's entities. The entity type and object property candidates are obtained by exploiting the instances and structure of this knowledge graph to compute a score that considers both the accuracy and interoperability of the candidates. Datatype properties are predicted with a random forest model, trained on the knowledge graph properties and their values, so to make predictions on candidate properties and rank them according to different measures. We present experiments using multiple datasets from the library domain as a use case and show that our methodology can produce meaningful candidate data models, adaptable to specific scenarios and needs

Where to publish and find ontologies? A survey of ontology libraries

One of the key promises of the Semantic Web is its potential to enable and facilitate data interoperability. The ability of data providers and application developers to share and reuse ontologies is a critical component of this data interoperability: if different applications and data sources use the same set of well defined terms for describing their domain and data, it will be much easier for them to “talk” to one another. Ontology libraries are the systems that collect ontologies from different sources and facilitate the tasks of finding, exploring, and using these ontologies. Thus ontology libraries can serve as a link in enabling diverse users and applications to discover, evaluate, use, and publish ontologies. In this paper, we provide a survey of the growing—and surprisingly diverse—landscape of ontology libraries. We highlight how the varying scope and intended use of the libraries affects their features, content, and potential exploitation in applications. From reviewing 11 ontology libraries, we identify a core set of questions that ontology practitioners and users should consider in choosing an ontology library for finding ontologies or publishing their own. We also discuss the research challenges that emerge from this survey, for the developers of ontology libraries to address

Online Access to Quantitative Data Resources

Nowadays, the increasing amount of semantic data available on the Web leads to a new stage in the potential of Semantic Web applications. However, it also introduces new issues due to the heterogeneity of the available semantic resources. One of the most remarkable is redundancy, that is, the excess of dierent semantic descriptions, coming from dierent sources, to describe the same intended meaning. In this paper, we propose a technique to perform a large scale integration of senses (expressed as ontology terms), in order to cluster the most similar ones, when indexing large amounts of online semantic information. It can dramatically reduce the redundancy problem on the current Semantic Web. In order to make this objective feasible, we have studied the adaptability and scalability of our previous work on sense integration, to be translated to the much larger scenario of the Semantic Web. Our evaluation shows a good behaviour of these techniques when used in large scale experiments, then making feasible the proposed approach

A Factorial Study of Neural Network Learning from Differences for Regression

International audienceFor regression tasks, using neural networks in a supervised way typically requires to repeatedly (over several iterations called epochs) present a set of items described by a number of features and the expected value to the network, so that it can learn to predict those values from those features. Inspired by case-based reasoning, several previous studies have made the hypothesis that there could be some advantages in training such neural networks on differences between sets of features, to predict differences between values. To test such a hypothesis, we applied a systematic factorial study on seven datasets and variants of datasets. The goal is to understand the impact on the performance of a neural network trained on differences, as compared to one trained in the usual way, of parameters such as the size of the training set, the number of epochs of training or the number of similar cases retrieved. We find that learning from differences achieves similar or better results than the ones of a neural network trained in the usual way. Our most significant finding however is that, in all cases, difference-based networks start obtaining good results from a low number of epochs, compared to the one required by a neural network trained in the usual manner. In other words, they achieve similar results while requiring less training

AFEL-Analytics for Everyday Learning

The goal of AFEL is to develop, pilot and evaluate methods and applications, which advance informal/collective learning as it surfaces implicitly in online social environments. The project is following a multi-disciplinary, industry-driven approach to the analysis and understanding of learner data in order to personalize, accelerate and improve informal learning processes. Learning Analytics and Educational Data Mining traditionally relate to the analysis and exploration of data coming from learning environments, especially to understand learners' behaviours. However, studies have for a long time demonstrated that learning activities happen outside of formal educational platforms, also. This includes informal and collective learning usually associated, as a side effect, with other (social) environments and activities. Relying on real data from a commercially available platform, the aim of AFEL is to provide and validate the technological grounding and tools for exploiting learning analytics on such learning activities. This will be achieved in relation to cognitive models of learning and collaboration, which are necessary to the understanding of loosely defined learning processes in online social environments. Applying the skills available in the consortium to a concrete set of live, industrial online social environments, AFEL will tackle the main challenges of informal learning analytics through 1) developing the tools and techniques necessary to capture information about learning activities from (not necessarily educational) online social environments; 2) creating methods for the analysis of such informal learning data, based on combining feature engineering and visual analytics with cognitive models of learning and collaboration; and 3) demonstrating the potential of the approach in improving the understanding of informal learning, and the way it is better supported; 4) evaluate all the former items in real world large scale applications and platforms.The authors would like to thank the rest of the AFEL consortium. This work was supported by the Know-Center Graz, the Science Foundation Ireland (SFI) Insight Centre for Data Analytics and the European-funded project AFEL (GA687916). The Know-Center Graz is funded within the Austrian COMET Program - Competence Centers for Excellent Technologies - under the auspices of the Austrian Ministry of Transport, Innovation and Technology, the Austrian Ministry of Economics and Labor and by the State of Styria. COMET is managed by the Austrian Research Promotion Agency (FFG).non-peer-reviewe

Privacy, security and policies: A review of problems and solutions with semantic web technologies

Semantic Web technologies aim to simplify the distribution, sharing and exploitation of information and knowledge, across multiple distributed actors on the Web. As with all technologies that manipulate information, there are privacy and security implications, and data policies (e.g., licenses and regulations) that may apply to both data and software artifacts. Additionally, semantic web technologies could contribute to the more intelligent and flexible handling of privacy, security and policy issues, through supporting information integration and sense-making. In order to better understand the scope of existing work on this topic we examine 78 articles from dedicated venues, including this special issue, the PrivOn workshop series, two SPOT workshops, as well as the broader literature that connects the Semantic Web research domain with issues relating to privacy, security and/or policies. Specifically, we classify each paper according to three taxonomies (one for each of the aforementioned areas), in order to identify common trends and research gaps. We conclude by summarising the strong focus on relevant topics in Semantic Web research (e.g. information collection, information processing, policies and access control), and by highlighting the need to further ex