44 research outputs found
Observing LOD: Its Knowledge Domains and the Varying Behavior of Ontologies Across Them
Linked Open Data (LOD) is the largest, collaborative, distributed, and publicly-accessible Knowledge Graph (KG) uniformly encoded in the Resource Description Framework (RDF) and formally represented according to the semantics of the Web Ontology Language (OWL). LOD provides researchers with a unique opportunity to study knowledge engineering as an empirical science: to observe existing modelling practices and possibly understanding how to improve knowledge engineering methodologies and knowledge representation formalisms. Following this perspective, several studies have analysed LOD to identify (mis-)use of OWL constructs or other modelling phenomena e.g. class or property usage, their alignment, the average depth of taxonomies. A question that remains open is whether there is a relation between observed modelling practices and knowledge domains (natural science, linguistics, etc.): do certain practices or phenomena change as the knowledge domain varies? Answering this question requires an assessment of the domains covered by LOD as well as a classification of its datasets. Existing approaches to classify LOD datasets provide partial and unaligned views, posing additional challenges. In this paper, we introduce a classification of knowledge domains, and a method for classifying LOD datasets and ontologies based on it. We classify a large portion of LOD and investigate whether a set of observed phenomena have a domain-specific character
Engineering Background Knowledge for Social Robots
Social robots are embodied agents that continuously perform knowledge-intensive tasks involving several kinds of information coming from different heterogeneous sources. Providing a framework for engineering robots' knowledge raises several problems like identifying sources of information and modeling solutions suitable for robots' activities, integrating knowledge coming from different sources, evolving this knowledge with information learned during robots' activities, grounding perceptions on robots' knowledge, assessing robots' knowledge with respect humans' one and so on. In this thesis we investigated feasibility and benefits of engineering background knowledge of Social Robots with a framework based on Semantic Web technologies and Linked Data. This research has been supported and guided by a case study that provided a proof of concept through a prototype tested in a real socially assistive context
Observing LOD using Equivalent Set Graphs: it is mostly flat and sparsely linked
This paper presents an empirical study aiming at understanding the modeling
style and the overall semantic structure of Linked Open Data. We observe how
classes, properties and individuals are used in practice. We also investigate
how hierarchies of concepts are structured, and how much they are linked. In
addition to discussing the results, this paper contributes (i) a conceptual
framework, including a set of metrics, which generalises over the observable
constructs; (ii) an open source implementation that facilitates its application
to other Linked Data knowledge graphs.Comment: 18 page
A Reference Software Architecture for Social Robots
Social Robotics poses tough challenges to software designers who are required
to take care of difficult architectural drivers like acceptability, trust of
robots as well as to guarantee that robots establish a personalised interaction
with their users. Moreover, in this context recurrent software design issues
such as ensuring interoperability, improving reusability and customizability of
software components also arise.
Designing and implementing social robotic software architectures is a
time-intensive activity requiring multi-disciplinary expertise: this makes
difficult to rapidly develop, customise, and personalise robotic solutions.
These challenges may be mitigated at design time by choosing certain
architectural styles, implementing specific architectural patterns and using
particular technologies.
Leveraging on our experience in the MARIO project, in this paper we propose a
series of principles that social robots may benefit from. These principles lay
also the foundations for the design of a reference software architecture for
Social Robots. The ultimate goal of this work is to establish a common ground
based on a reference software architecture to allow to easily reuse robotic
software components in order to rapidly develop, implement, and personalise
Social Robots
Streamlining Knowledge Graph Construction with a fa\c{c}ade: The SPARQL Anything project
What should a data integration framework for knowledge engineers look like?
Recent research on Knowledge Graph construction proposes the design of a
fa\c{c}ade, a notion borrowed from object-oriented software engineering. This
idea is applied to SPARQL Anything, a system that allows querying heterogeneous
resources as-if they were in RDF, in plain SPARQL 1.1, by overloading the
SERVICE clause. SPARQL Anything supports a wide variety of file formats, from
popular ones (CSV, JSON, XML, Spreadsheets) to others that are not supported by
alternative solutions (Markdown, YAML, DOCx, Bibtex). Features include querying
Web APIs with high flexibility, parametrised queries, and chaining multiple
transformations into complex pipelines. In this paper, we describe the design
rationale and software architecture of the SPARQL Anything system. We provide
references to an extensive set of reusable, real-world scenarios from various
application domains. We report on the value-to-users of the founding
assumptions of its design, compared to alternative solutions through a
community survey and a field report from the industry.Comment: 15 page
Facade-X: An Opinionated Approach to SPARQL Anything
The Semantic Web research community understood since its beginning how crucial it is to equip practitioners with methods to transform non-RDF resources into RDF. Proposals focus on either engineering content transformations or accessing non-RDF resources with SPARQL. Existing solutions require users to learn specific mapping languages (e.g. RML), to know how to query and manipulate a variety of source formats (e.g. XPATH, JSON-Path), or to combine multiple languages (e.g. SPARQL Generate). In this paper, we explore an alternative solution and contribute a general-purpose meta-model for converting non-RDF resources into RDF: Facade-X. Our approach can be implemented by overriding the SERVICE operator and does not require to extend the SPARQL syntax. We compare our approach with the state of art methods RML and SPARQL Generate and show how our solution has lower learning demands and cognitive complexity, and it is cheaper to implement and maintain, while having comparable extensibility and efficiency