Search (16 results, page 1 of 1)

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Abstract

This thesis focuses on conversion of vocabularies for representation and integration of collections on the Semantic Web. A secondary focus is how to represent metadata schemas (RDF Schemas representing metadata element sets) such that they interoperate with vocabularies. The primary domain in which we operate is that of cultural heritage collections. The background worldview in which a solution is sought is that of the Semantic Web research paradigmwith its associated theories, methods, tools and use cases. In other words, we assume the SemanticWeb is in principle able to provide the context to realize interoperable collections. Interoperability is dependent on the interplay between representations and the applications that use them. We mean applications in the widest sense, such as "search" and "annotation". These applications or tasks are often present in software applications, such as the E-Culture application. It is therefore necessary that applications requirements on the vocabulary representation are met. This leads us to formulate the following problem statement: HOW CAN EXISTING VOCABULARIES BE MADE AVAILABLE TO SEMANTIC WEB APPLICATIONS?
We refine the problem statement into three research questions. The first two focus on the problem of conversion of a vocabulary to a Semantic Web representation from its original format. Conversion of a vocabulary to a representation in a Semantic Web language is necessary to make the vocabulary available to SemanticWeb applications. In the last question we focus on integration of collection metadata schemas in a way that allows for vocabulary representations as produced by our methods. Academisch proefschrift ter verkrijging van de graad Doctor aan de Vrije Universiteit Amsterdam, Dutch Research School for Information and Knowledge Systems.

Date

29. 7.2011 14:44:56

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Abstract

Knowledge Representation and Reasoning (KR & R) has become one of the promising fields of Artificial Intelligence. KR is dedicated towards representing information about the domain that can be utilized in path planning. Ontology based knowledge representation and reasoning techniques provide sophisticated knowledge about the environment for processing tasks or methods. Ontology helps in representing the knowledge about environment, events and actions that help in path planning and making robots more autonomous. Knowledge reasoning techniques can infer new conclusion and thus aids planning dynamically in a non-deterministic environment. In the initial sections, the representation of knowledge using ontology and the techniques for reasoning that could contribute in path planning are discussed in detail. In the following section, we also provide comparison of various planning domain modeling languages, ontology editors, planners and robot simulation tools.

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Abstract

Semantic Web knowledge representation standards, and in particular RDF and OWL, often come endowed with a formal semantics which is considered to be of fundamental importance for the field. Reasoning, i.e., the drawing of logical inferences from knowledge expressed in such standards, is traditionally based on logical deductive methods and algorithms which can be proven to be sound and complete and terminating, i.e. correct in a very strong sense. For various reasons, though, in particular the scalability issues arising from the ever increasing amounts of Semantic Web data available and the inability of deductive algorithms to deal with noise in the data, it has been argued that alternative means of reasoning should be investigated which bear high promise for high scalability and better robustness. From this perspective, deductive algorithms can be considered the gold standard regarding correctness against which alternative methods need to be tested. In this paper, we show that it is possible to train a Deep Learning system on RDF knowledge graphs, such that it is able to perform reasoning over new RDF knowledge graphs, with high precision and recall compared to the deductive gold standard.

Date

16.11.2018 14:22:01

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Abstract

Starting from an unsolved problem of information retrieval this paper presents an ontology-based model for indexing and retrieval. The model combines the methods and experiences of cognitive-to-interpret indexing languages with the strengths and possibilities of formal knowledge representation. The core component of the model uses inferences along the paths of typed relations between the entities of a knowledge representation for enabling the determination of hit quantities in the context of retrieval processes. The entities are arranged in aspect-oriented facets to ensure a consistent hierarchical structure. The possible consequences for indexing and retrieval are discussed.

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Abstract

Faceted arrangement of entities and typed relations for representing different associations between the entities are established tools in knowledge representation. In this paper, a proposal is being discussed combining both tools to draw inferences along relational paths. This approach may yield new benefit for information retrieval processes, especially when modeled for heterogeneous environments in the Semantic Web. Faceted arrangement can be used as a selection tool for the semantic knowledge modeled within the knowledge representation. Typed relations between the entities of different facets can be used as restrictions for selecting them across the facets.

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Abstract

The increasingly pervasive nature of the Web, expanding to devices and things in everydaylife, along with new trends in Artificial Intelligence call for new paradigms and a new look onKnowledge Representation and Processing at scale for the Semantic Web. The emerging, but stillto be concretely shaped concept of "Knowledge Graphs" provides an excellent unifying metaphorfor this current status of Semantic Web research. More than two decades of Semantic Webresearch provides a solid basis and a promising technology and standards stack to interlink data,ontologies and knowledge on the Web. However, neither are applications for Knowledge Graphsas such limited to Linked Open Data, nor are instantiations of Knowledge Graphs in enterprises- while often inspired by - limited to the core Semantic Web stack. This report documents theprogram and the outcomes of Dagstuhl Seminar 18371 "Knowledge Graphs: New Directions forKnowledge Representation on the Semantic Web", where a group of experts from academia andindustry discussed fundamental questions around these topics for a week in early September 2018,including the following: what are knowledge graphs? Which applications do we see to emerge?Which open research questions still need be addressed and which technology gaps still need tobe closed?

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Abstract

In Linked Data, the use of owl:sameAs is ubiquitous in 'inter-linking' data-sets. However, there is a lurking suspicion within the Linked Data community that this use of owl:sameAs may be somehow incorrect, in particular with regards to its interactions with inference. In fact, owl:sameAs can be considered just one type of 'identity link', a link that declares two items to be identical in some fashion. After reviewing the definitions and history of the problem of identity in philosophy and knowledge representation, we outline four alternative readings of owl:sameAs, showing with examples how it is being (ab)used on the Web of data. Then we present possible solutions to this problem by introducing alternative identity links that rely on named graphs.

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Abstract

Thesauri that are "ontologized" into OWL-DL semantics are highly amenable to modeling errors resulting from falsely interpreting existential restrictions. We investigated the OWL-DL representation of the NCI Thesaurus (NCIT) in order to assess the correctness of existential restrictions. A random sample of 354 axioms using the someValuesFrom operator was taken. According to a rating performed by two domain experts, roughly half of these examples, and in consequence more than 76,000 axioms in the OWL-DL version, make incorrect assertions if interpreted according to description logics semantics. These axioms therefore constitute a huge source for unintended models, rendering most logic-based reasoning unreliable. After identifying typical error patterns we discuss some possible improvements. Our recommendation is to either amend the problematic axioms in the OWL-DL formalization or to consider some less strict representational format.

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Abstract

Ontologies have proven to be an essential element in many applications. They are used in agent systems, knowledge management systems, and e-commerce platforms. They can also generate natural language, integrate intelligent information, provide semantic-based access to the Internet, and extract information from texts in addition to being used in many other applications to explicitly declare the knowledge embedded in them. However, not only are ontologies useful for applications in which knowledge plays a key role, but they can also trigger a major change in current Web contents. This change is leading to the third generation of the Web-known as the Semantic Web-which has been defined as "the conceptual structuring of the Web in an explicit machine-readable way."1 This definition does not differ too much from the one used for defining an ontology: "An ontology is an explicit, machinereadable specification of a shared conceptualization."2 In fact, new ontology-based applications and knowledge architectures are developing for this new Web. A common claim for all of these approaches is the need for languages to represent the semantic information that this Web requires-solving the heterogeneous data exchange in this heterogeneous environment. Here, we don't decide which language is best of the Semantic Web. Rather, our goal is to help developers find the most suitable language for their representation needs. The authors analyze the most representative ontology languages created for the Web and compare them using a common framework.

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Date

29. 7.2011 14:44:56
26.12.2011 13:40:22

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Abstract

Mathematics is a ubiquitous foundation of science, technology, and engineering. Specific areas, such as numeric and symbolic computation or logics, enjoy considerable software support. Working mathematicians have recently started to adopt Web 2.0 environment, such as blogs and wikis, but these systems lack machine support for knowledge organization and reuse, and they are disconnected from tools such as computer algebra systems or interactive proof assistants.We argue that such scenarios will benefit from Semantic Web technology. Conversely, mathematics is still underrepresented on the Web of [Linked] Data. There are mathematics-related Linked Data, for example statistical government data or scientific publication databases, but their mathematical semantics has not yet been modeled. We argue that the services for the Web of Data will benefit from a deeper representation of mathematical knowledge. Mathematical knowledge comprises logical and functional structures - formulæ, statements, and theories -, a mixture of rigorous natural language and symbolic notation in documents, application-specific metadata, and discussions about conceptualizations, formalizations, proofs, and (counter-)examples. Our review of approaches to representing these structures covers ontologies for mathematical problems, proofs, interlinked scientific publications, scientific discourse, as well as mathematical metadata vocabularies and domain knowledge from pure and applied mathematics. Many fields of mathematics have not yet been implemented as proper Semantic Web ontologies; however, we show that MathML and OpenMath, the standard XML-based exchange languages for mathematical knowledge, can be fully integrated with RDF representations in order to contribute existing mathematical knowledge to theWeb of Data. We conclude with a roadmap for getting the mathematical Web of Data started: what datasets to publish, how to interlink them, and how to take advantage of these new connections.

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Date

13.12.2017 14:17:22

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Date

29. 9.2015 18:56:44

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Date

29. 7.2011 14:44:56