Mathieu d’Aquin

Using ontological contexts to assess the relevance of statements in ontology evolution

Ontology evolution tools often propose new ontological changes in the form of statements. While different methods exist to check the quality of such statements to be added to the ontology (e.g., in terms of consistency and impact), their relevance is usually left to the user to assess. Relevance in this context is a notion of how well the statement fits in the target ontology. We present an approach to automatically assess such relevance. It is acknowledged in cognitive science and other research areas that a piece of information flowing between two entities is relevant if there is an agreement on the context used between the entities. In our approach, we derive the context of a statement from online ontologies in which it is used, and study how this context matches with the target ontology. We identify relevance patterns that give an indication of relevance when the statement context and the target ontology fulfill specific conditions. We validate our approach through an experiment in three different domains, and show how our pattern-based technique outperforms a naive overlap-based approach.

How much semantic data on small devices

Semantic tools such as triple stores, reasoners and query engines tend to be designed for large-scale applications. However, with the rise of sensor networks, smart-phones and smart-appliances, new scenarios appear where small devices with restricted resources have to handle limited amounts of data. It is therefore important to assess how existing semantic tools behave on such small devices, and how much data they can reasonably handle. There exist benchmarks for comparing triple stores and query engines, but these benchmarks are targeting large-scale applications and would not be applicable in the considered scenarios. In this paper, we describe a set of small to medium scale benchmarks explicitly targeting applications on small devices. We describe the result of applying these benchmarks on three different tools (Jena, Sesame and Mulgara) on the smallest existing netbook (the Asus EEE PC 700), showing how they can be used to test and compare semantic tools in resource-limited environments.

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 Ana- lytics and Educational Data Mining. Covering over five years of scientific literature from the most relevant conferences and jour- nals, 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.

Extending open rating systems for ontology ranking and reuse

Ontology reuse saves costs and improves interoperability between ontologies. Knowing which ontology to reuse is difficult without having a quality assessment. We employ user ratings to determine the user-perceived quality of ontologies. The combination of an Open Rating System (ORS), user ratings, and information on trust between users, allow us to compute a personalized ranking of ontologies. In this paper, we present our extension, the Topic-Specific Trust Open Rating System (TS-ORS). To overcome the limitations of the ORS, the TS-ORS features topic-specific trust and multi-faceted ratings. In a user study, we show that having user ratings and result ranking based on a TS-ORS significantly facilitates ontology assessment and selection for the end user.

Measuring Accuracy of Triples in Knowledge Graphs

An increasing amount of large-scale knowledge graphs have been constructed in recent years. Those graphs are often created from text-based extraction, which could be very noisy. So far, cleaning knowledge graphs are often carried out by human experts and thus very inefficient. It is necessary to explore automatic methods for identifying and eliminating erroneous information. In order to achieve this, previous approaches primarily rely on internal information i.e. the knowledge graph itself. In this paper, we introduce an automatic approach, Triples Accuracy Assessment (TAA), for validating RDF triples (source triples) in a knowledge graph by finding consensus of matched triples (among target triples) from other knowledge graphs. TAA uses knowledge graph interlinks to find identical resources and apply different matching methods between the predicates of source triples and target triples. Then based on the matched triples, TAA calculates a confidence score to indicate the correctness of a source triple. In addition, we present an evaluation of our approach using the FactBench dataset for fact validation. Our findings show promising results for distinguishing between correct and wrong triples.

Learning to Assess Linked Data Relationships Using Genetic Programming

The goal of this work is to learn a measure supporting the detection of strong relationships between Linked Data entities. Such relationships can be represented as paths of entities and properties, and can be obtained through a blind graph search process traversing Linked Data. The challenge here is therefore the design of a cost-function that is able to detect the strongest relationship between two given entities, by objectively assessing the value of a given path. To achieve this, we use a Genetic Programming approach in a supervised learning method to generate path evaluation functions that compare well with human evaluations. We show how such a cost-function can be generated only using basic topological features of the nodes of the paths as they are being traversed (i.e. without knowledge of the whole graph), and how it can be improved through introducing a very small amount of knowledge about the vocabularies of the properties that connect nodes in the graph.

Visualizing and Navigating Ontologies with KC-Viz

There is empirical evidence that current user interfaces for ontology engineering are still inadequate in their ability to reduce task complexity for users, especially non-expert ones. Here we present a novel tool for visualizing and navigating ontologies, KC-Viz, which exploits an innovative ontology summarization method to support a “middle-out ontology browsing” approach, where it becomes possible to navigate ontologies starting from the most information-rich nodes (i.e., key concepts). This approach is similar to map-based visualization and navigation in geographical information systems, where, e.g., major cities are displayed more prominently than others, depending on the current level of granularity. Building on its powerful and empirically validated ontology summarization algorithm, KC-Viz provides a rich set of navigation and visualization mechanisms, including flexible zooming into and hiding of specific parts of an ontology, visualization of the most salient nodes, history browsing, saving and loading of customized ontology views, as well as essential interface support, such as graphical zooming, font manipulation, tree layout customization, and other functionalities.

Towards a Formalization of Ontology Relations in the Context of Ontology Repositories

In the context of Semantic Web Search Engines is becoming crucial to study relations between ontologies to improve the ontology selection task. In this paper, we describe DOOR - The Descriptive Ontology of Ontology Relations, to represent, manipulate and reason upon relations between ontologies in large ontology repositories. DOOR represents a first attempt in describing and formalizing ontology relations. In fact, it does not pretend to be a universal standard structure. Rather, It is intended to be a flexible, easily modifiable structure to model ontology relations in the context of ontology repositories. Here, we provide a detailed description of the methodology used to design the DOOR ontology, as well as an overview of its content. We also describe how DOOR is used in a complete framework (called KANNEL) for detecting and managing semantic relations between ontologies in large ontology repositories. Applied in the context of a large collection of automatically crawled ontologies, DOOR and KANNEL provide a starting point for analyzing the underlying structure of the network of ontologies that is the Semantic Web.

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 explore under-represented topics (e.g., malware detection, fraud detection, and supporting policy validation by data consumers).

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.