1,720,996 research outputs found
Addressing Knowledge Integration with a Frame-Driven Approach
No full textGiven a knowledge-based system running virtually forever able to acquire and automatically store new open-domain knowledge, one of the challenges is to evolve by continuously integrating new knowledge. This needs to be done while handling conflicts, redundancies and linking existing knowledge to the incoming one. We refer to this task with the name Knowledge integration. In this paper we define the problem by discussing its challenges, we propose an approach for tackling the problem, and, we suggest a methodology for the evaluation of results
Observing LOD: Its Knowledge Domains and the Varying Behavior of Ontologies Across Them
Get PDFLinked 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
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Knowledge Graph Construction with a <i>façade</i>: a unified method to access heterogeneous data sources on the Web
Get PDFData integration is the dominant use case for RDF Knowledge Graphs. However, Web resources come in formats with weak semantics (for example CSV and JSON), or formats specific to a given application (for example BibTex, HTML, and Markdown). To solve this problem, Knowledge Graph Construction (KGC) is gaining momentum due to its focus on supporting users in transforming data into RDF. However, using existing KGC frameworks result in complex data processing pipelines, which mix structural and semantic mappings, whose development and maintenance constitute a significant bottleneck for KG engineers. Such frameworks force users to rely on different tools, sometimes based on heterogeneous languages, for inspecting sources, designing mappings, and generating triples, thus making the process unnecessarily complicated. We argue that it is possible and desirable to equip KG engineers with the ability of interacting with Web data formats by relying on their expertise in RDF and the well-established SPARQL query language.
In this article, we study a unified method for data access to heterogeneous data sources with Facade-X, a meta-model implemented in a new data integration system called SPARQL Anything. We demonstrate that our approach is theoretically sound, since it allows a single meta-model, based on RDF, to represent data from (a) any file format expressible in BNF syntax, as well as (b) any relational database. We compare our method to state-of-the-art approaches in terms of usability (cognitive complexity of the mappings) and general performance. Finally, we discuss the benefits and challenges of this novel approach by engaging with the reference user community
How is Your Knowledge Graph Used: Content-Centric Analysis of SPARQL Query Logs
Get PDFKnowledge graphs (KGs) are used to integrate and persist information useful to organisations, communities, or the general public. It is essential to understand how KGs are used so as to evaluate the strengths and shortcomings of semantic web standards, data modelling choices formalised in ontologies, deployment settings of triple stores etc. One source of information on the usage of the KGs is the query logs, but making sense of hundreds of thousands of log entries is not trivial. Previous works that studied available logs from public SPARQL endpoints mainly focused on the general syntactic properties of the queries disregarding the semantics and their intent. We introduce a novel, content-centric, approach that we call query log summarisation, in which we group the queries that can be derived from some common pattern. The type of patterns considered in this work is query templates, i.e. common blueprints from which multiple queries can be generated by the replacement of parameters with constants. Moreover, we present an algorithm able to summarise a query log as a list of templates whose time and space complexity is linear with respect to the size of the input (number and dimension of queries). We experimented with the algorithm on the query logs of the Linked SPARQL Queries dataset showing promising results
Frame-Based Ontology Alignment
No full textThe need of handling semantic heterogeneity of resources is a key problem of the Semantic Web. State of the art techniques for ontology matching are the key technology for addressing this issue. However, they only partially exploit the natural lan- guage descriptions of ontology entities and they are mostly unable to find correspondences between entities having dif- ferent logical types (e.g. mapping properties to classes). We introduce a novel approach aimed at finding correspondences between ontology entities according to the intensional mean- ing of their models, hence abstracting from their logical types. Lexical linked open data and frame semantics play a crucial role in this proposal. We argue that this approach may lead to a step ahead in the state of the art of ontology matching, and positively affect related applications such as question an- swering and knowledge reconciliation
Empirical analysis of foundational distinctions in linked open data
Get PDFThe Web and its Semantic extension (i.e. Linked Open Data) contain open global-scale knowledge and make it available to potentially intelligent machines that want to benefit from it. Nevertheless, most of Linked Open Data lack ontological distinctions and have sparse axiomatisation. For example, distinctions such as whether an entity is inherently a class or an individual, or whether it is a physical object or not, are hardly expressed in the data, although they have been largely studied and formalised by foundational ontologies (e.g. DOLCE, SUMO). These distinctions belong to common sense too, which is relevant for many artificial intelligence tasks such as natural language understanding, scene recognition, and the like. There is a gap between foundational ontologies, that often formalise or are inspired by pre-existing philosophical theories and are developed with a top-down approach, and Linked Open Data that mostly derive from existing databases or crowd-based effort (e.g. DBpedia, Wikidata). We investigate whether machines can learn foundational distinctions over Linked Open Data entities, and if they match common sense. We want to answer questions such as "does the DBpedia entity for dog refer to a class or to an instance?". We report on a set of experiments based on machine learning and crowdsourcing that show promising results
FOOD: Food in open data
No full textThis paper describes the outcome of an e-government project named FOOD, FOod in Open Data, which was carried out in the context of a collaboration between the Institute of Cognitive Sciences and Technologies of the Italian National Research Council, the Italian Ministry of Agriculture (MIPAAF) and the Italian Digital Agency (AgID). In particular, we implemented several ontologies for describing protected names of products (wine, pasta, fish, oil, etc.). In addition, we present the process carried out for producing and publishing a LOD dataset containing data extracted from existing Italian policy documents on such products and compliant with the aforementioned ontologies
An Ontology Design Pattern for supporting behaviour arbitration in cognitive agents
No full textIn this paper we present an Ontology Design Pattern for the
definition of situation-driven behaviour selection and arbitration models
for cognitive agents. The proposed pattern relies on the descriptions and
situations ontology pattern, combined with a frame-based representation
scheme. Inspired by the affordance theory and behaviour-based robotics
principles, our reference model enables the definition of weighted relationships, or affordances, between situations (representing agent’s perception of the environmental and social context) and agent’s functional
and behavioral abilities. These weighted links serve as a basis for supporting runtime task selection and arbitration policies, to dynamically
and contextually select agent’s behaviour. The pattern is at the heart
of the behaviour-based cognitive approach adopted in the EU H2020
MARIO project for the design of an autonomous service robot (i.e., the
cognitive agent) to support elderly people with cognitive impairments
Facade-X: An Opinionated Approach to SPARQL Anything
Get PDFThe 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
Knowledge-driven Support for Reminiscence on Companion Robots
Get PDFIn this paper we present our work towards the development of an application for personalized reminiscence therapy in people with dementia. The reminiscence process aims at recalling personal memories by combining user-specific knowledge, dialogue-based human-robot interaction and multimedia content. The application is part of a robotic software framework for companion robots, investigated in the EU MARIO project and under evaluation in different dementia care settings
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