399 research outputs found
Dolce+D&S Ultralite and its main Ontology Design Patterns
No full textThis chapter aims at providing a hand note on the Dolce+D&S Ultralite ontology (DUL) and its main ontology design patterns (ODP)
Pitchclass2vec: Symbolic Music Structure Segmentation with Chord Embeddings
No full textStructure perception is a fundamental aspect of music cognition in humans.
Historically, the hierarchical organization of music into structures served as
a narrative device for conveying meaning, creating expectancy, and evoking
emotions in the listener. Thereby, musical structures play an essential role in
music composition, as they shape the musical discourse through which the
composer organises his ideas. In this paper, we present a novel music
segmentation method, pitchclass2vec, based on symbolic chord annotations, which
are embedded into continuous vector representations using both natural language
processing techniques and custom-made encodings. Our algorithm is based on
long-short term memory (LSTM) neural network and outperforms the
state-of-the-art techniques based on symbolic chord annotations in the field
A multi-dimensional comparison of ontology design patterns for representing n-ary relations
No full textWithin the broad area of knowledge pattern science, an important topic is the discovery, description, and evaluation of modeling patterns for a certain task. One of the most controversial problem is constituted by modeling relations with large or variable (polymorphic) arity. There is indeed a large literature on representing n-ary relations in logical languages with expressivity limited to unary and binary relations, e.g. when time, space, roles and other knowledge should be used as indexes to binary relations. In this paper we provide a comparison of several design patterns, based on their respective (dis)advantages, as well as on their axiomatic complexity. Data on actual processing time for queries and DL reasoning from an in-vitro study is also provided. © 2013 Springer-Verlag Berlin Heidelberg
Formal Representation and Extraction of Perspectives
No full textAfter a survey of perspective/viewpoint theories, the chapter presents a formal model of perspectives as they can be extracted from discourse. It uses the Framester frame semantics, and demonstrates it by semantically overloading knowledge graphs extracted from perspective-laden natural language sentences
Towards a pattern science for the Semantic Web
No full textWith the web of data, the semantic web can be an empirical science. Two problems have to be dealt with. The knowledge soup problem is about semantic heterogeneity, and can be considered a difficult technical issue, which needs appropriate transformation and inferential pipelines that can help making sense of the different knowledge contexts. The knowledge boundary problem is at the core of empirical investigation over the semantic web: what are the meaningful units that constitute the research objects for the semantic web? This question touches many aspects of semantic web studies: data, schemata, representation and reasoning, interaction, linguistic grounding, etc
Observing LOD: Its Knowledge Domains and the Varying Behavior of Ontologies Across Them
No full textLinked 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
Multi-layered n-ary Patterns
No full textIn this chapter, we show an example of knowledge pattern science by empir- ically studying solutions for representing extensional and intensional1 multigrade predicates, widely known as n-ary relationships in web ontologies and linked data
Extraction of common conceptual components from multiple ontologies
No full textUnderstanding large ontologies is still an issue, and has an impact on many ontology engineering tasks. We describe a novel method for identifying and extracting conceptual components from domain ontologies, which are used to understand and compare them. The method is applied to two corpora of ontologies in the Cultural Heritage and Conference domain, respectively. The results, which show good quality, are evaluated by manual inspection and by correlation with datasets and tool performance from the ontology alignment evaluation initiative
Fine-Tuning Triplification with Semion
No full textThe Web of Data is fed mainly by “triplifiers (or RDFizers)”, tools able to transform content (usually from databases) to linked data. Current triplifiers implement diverse methods, and are usually based on bulk recipes, which make fixed assumptions on the domain semantics. They focus more on syntactic than on semantic transformation, and al- low for limited (sometimes no) customization of the process. We present Semion, a method and a tool for triplifying content sources that over- comes such limitations. It focuses on applying good practices of design, provides high customizability of the transformation process, and exploits OWL expressivity for describing the domain of interest
Empirical ontology design patterns and shapes from Wikidata
No full textThe ontology underlying the Wikidata knowledge graph (KG) has not been formalized. Instead, its semantics emerges
bottom-up from the use of its classes and properties. Flexible guidelines and rules have been defined by the Wikidata project
for the use of its ontology, however, it is still often difficult to reuse the ontology’s constructs. Based on the assumption that
identifying ontology design patterns from a knowledge graph contributes to making its (possibly) implicit ontology emerge, in
this paper we present a method for extracting what we term empirical ontology design patterns (EODPs) from a knowledge
graph. This method takes as input a knowledge graph and extracts EODPs as sets of axioms/constraints involving the classes
instantiated in the KG. These EODPs include data about the probability of such axioms/constraints happening. We apply our
method on two domain-specific portions of Wikidata, addressing the music and art, architecture, and archaeology domains, and
we compare the empirical ontology design patterns we extract with the current support present in Wikidata. We show how these
patterns can provide guidance for the use of the Wikidata ontology and its potential improvement, and can give insight into the
content of (domain-specific portions of) the Wikidata knowledge graph
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