138,150 research outputs found
Engineering a semantic web trust infrastructure
The ability to judge the trustworthiness of information is an important and challenging problem in the field of Semantic Web research. In this thesis, we take an end-to-end look at the challenges posed by trust on the Semantic Web, and present contributions in three areas: a Semantic Web identity vocabulary, a system for bootstrapping trust environments, and a framework for trust aware information management. Typically Semantic Web agents, which consume and produce information, are not described with sufficient information to permit those interacting with them to make good judgements of trustworthiness. A descriptive vocabulary for agent identity is required to enable effective inter agent discourse, and the growth of trust and reputation within the Semantic Web; we therefore present such a foundational identity ontology for describing web-based agents.It is anticipated that the Semantic Web will suffer from a trust network bootstrapping problem. In this thesis, we propose a novel approach which harnesses open data to bootstrap trust in new trust environments. This approach brings together public records published by a range of trusted institutions in order to encourage trust in identities within new environments. Information integrity and provenance are both critical prerequisites for well-founded judgements of information trustworthiness. We propose a modification to the RDF Named Graph data model in order to address serious representational limitations with the named graph proposal, which affect the ability to cleanly represent claims and provenance records. Next, we propose a novel graph based approach for recording the provenance of derived information. This approach offers computational and memory savings while maintaining the ability to answer graph-level provenance questions. In addition, it allows new optimisations such as strategies to avoid needless repeat computation, and a delta-based storage strategy which avoids data duplication.<br/
Enabling Scalable Multi-channel Communication through Semantic Technologies
With the advance of the Web in the direction Social
Media the number of communication possibilities has
exponentially increased bringing new challenges and
opportunities for companies to build and shape their
reputation online as well as to engage and maintain the
relationships to their customers. In this paper we describe how
semantic technologies enable scalable, effective and efficient
on-line communication. We illustrate four different ways in
which semantics can be used for this purpose. First, we discuss
semantic analysis of communication items based on 'classical'
semantic, such as natural language processing. Second, we look
at semantics as a channel, viewing Linked Open Data
vocabularies not only as terminological assets but as
communication channels. Third, semantics provide the
methodologies and tools for content modeling by means of
ontologies. Finally, semantics through semantic matchmaking
enable semi-automatic assignment and distribution of content
to channels and vice-versa
Microsoft Academic is on the verge of becoming a bibliometric superpower
Last year, the new Microsoft Academic service was launched. Sven E. Hug and Martin P. Brändle look at how it compares with more established competitors such as Google Scholar, Scopus, and Web of Science. While there are reservations about the availability of instructions for novice users, Microsoft Academic has impressive semantic search functionality, broad coverage, structured and rich metadata, and solid citation analysis features. Moreover, accessing raw data is relatively cheap. Given these benefits and its fast pace of development, Microsoft Academic is on the verge of becoming a bibliometric superpower
Who Controls the Past Controls the Future - Life Annotation in Principle and Practice
The fields of the Semantic Web and Ubiquitous Computing are both relatively new fields within the discipline of Computer Science. Yet both are growing and have begun to overlap as people demand ever-smaller computers with persistent access to the internet. The Semantic Web has the potential to become a global knowledge store duplicating the information on the Web, albeit in a machine-readable form. Such a knowledge base combined with truly ubiquitous systems could provide a great benefit for humans. But what of personal knowledge? Information is generally of more use when linked to other information. Sometimes this information must be kept private, so integrating personal knowledge with the Semantic Web is not desirable. Instead, it should be possible for a computer system to collect and store private knowledge while also being able to augment it with public knowledge from the Web, all without the need for user effort. This thesis begins with a review of both fields, indicating the points at which they overlap. It describes the need for semantic annotation and various processes through which it may be achieved. A method for annotating a human's life using a combination of personal data collected using an ubiquitous system and public data freely available on the Semantic Web is suggested and conceptually compared to human memory. Context-aware computing is described along with its potential to annotate the life of a human being and the hypothesis that today's technology is able to carry out this task is presented. The work then introduces a portable system for automatically logging contextual data and describes a study which used this system to gather life annotations on one specific individual over the course of two years. The implementation of the system and its use is documented and the data collected is presented and evaluated. Finally the thesis offers the conclusion that one type of contextual data is not enough to answer most questions and that multiple forms of data need to be merged in order to get a useful picture of a person's life. The thesis concludes with a brief look into the future of the Semantic Web and how it has the potential to assist in achieving better results in this field of study
V-Coder: Adaptive AutoEncoder for Semantic Disclosure in Knowledge Graphs
Semantic Web or Knowledge Graphs (KG) emerged to one of the most important
information source for intelligent systems requiring access to structured
knowledge. One of the major challenges is the extraction and processing of
unambiguous information from textual data. Following the human perception,
overlapping semantic linkages between two named entities become clear due to
our common-sense about the context a relationship lives in which is not the
case when we look at it from an automatically driven process of a machine. In
this work, we are interested in the problem of Relational Resolution within the
scope of KGs, i.e, we are investigating the inherent semantic of relationships
between entities within a network. We propose a new adaptive AutoEncoder,
called V-Coder, to identify relations inherently connecting entities from
different domains. Those relations can be considered as being ambiguous and are
candidates for disentanglement. Likewise to the Adaptive Learning Theory (ART),
our model learns new patterns from the KG by increasing units in a competitive
layer without discarding the previous observed patterns whilst learning the
quality of each relation separately. The evaluation on real-world datasets of
Freebase, Yago and NELL shows that the V-Coder is not only able to recover
links from corrupted input data, but also shows that the semantic disclosure of
relations in a KG show the tendency to improve link prediction. A semantic
evaluation wraps the evaluation up
Highly focused document retrieval in aerospace engineering : user interaction design and evaluation
Purpose – This paper seeks to describe the preliminary studies (on both users and data), the design and evaluation of the K-Search system for searching legacy documents in aerospace engineering. Real-world reports of jet engine maintenance challenge the current indexing practice, while real users’ tasks require retrieving the information in the proper context. K-Search is currently in use in Rolls-Royce plc and has evolved to include other tools for knowledge capture and management.
Design/methodology/approach – Semantic Web techniques have been used to automatically extract information from the reports while maintaining the original context, allowing a more focused retrieval than with more traditional techniques. The paper combines semantic search with classical information retrieval to increase search effectiveness. An innovative user interface has been designed to take advantage of this hybrid search technique. The interface is designed to allow a flexible and
personal approach to searching legacy data.
Findings – The user evaluation showed that the system is effective and well received by users. It also shows that different people look at the same data in different ways and make different use of the same system depending on their individual needs, influenced by their job profile and personal attitude.
Research limitations/implications – This study focuses on a specific case of an enterprise working in aerospace engineering. Although the findings are likely to be shared with other engineering domains (e.g. mechanical, electronic), the study does not expand the evaluation to different settings.
Originality/value – The study shows how real context of use can provide new and unexpected challenges to researchers and how effective solutions can then be adopted and used in organizations.</p
Towards concept analysis in categories: limit inferior as algebra, limit superior as coalgebra
While computer programs and logical theories begin by declaring the concepts
of interest, be it as data types or as predicates, network computation does not
allow such global declarations, and requires *concept mining* and *concept
analysis* to extract shared semantics for different network nodes. Powerful
semantic analysis systems have been the drivers of nearly all paradigm shifts
on the web. In categorical terms, most of them can be described as
bicompletions of enriched matrices, generalizing the Dedekind-MacNeille-style
completions from posets to suitably enriched categories. Yet it has been well
known for more than 40 years that ordinary categories themselves in general do
not permit such completions. Armed with this new semantical view of
Dedekind-MacNeille completions, and of matrix bicompletions, we take another
look at this ancient mystery. It turns out that simple categorical versions of
the *limit superior* and *limit inferior* operations characterize a general
notion of Dedekind-MacNeille completion, that seems to be appropriate for
ordinary categories, and boils down to the more familiar enriched versions when
the limits inferior and superior coincide. This explains away the apparent gap
among the completions of ordinary categories, and broadens the path towards
categorical concept mining and analysis, opened in previous work.Comment: 22 pages, 5 figures and 9 diagram
Model driven design and data integration in semantic web information systems
The Web is quickly evolving in many ways. It has evolved from a Web of documents into a Web of applications in which a growing number of designers offer new and interactive Web applications with people all over the world. However, application design and implementation remain complex, error-prone and laborious. In parallel there is also an evolution from a Web of documents into a Web of `knowledge' as a growing number of data owners are sharing their data sources with a growing audience. This brings the potential new applications for these data sources, including scenarios in which these datasets are reused and integrated with other existing and new data sources. However, the heterogeneity of these data sources in syntax, semantics and structure represents a great challenge for application designers. The Semantic Web is a collection of standards and technologies that offer solutions for at least the syntactic and some structural issues. If offers semantic freedom and flexibility, but this leaves the issue of semantic interoperability. In this thesis we present Hera-S, an evolution of the Model Driven Web Engineering (MDWE) method Hera. MDWEs allow designers to create data centric applications using models instead of programming. Hera-S especially targets Semantic Web sources and provides a flexible method for designing personalized adaptive Web applications. Hera-S defines several models that together define the target Web application. Moreover we implemented a framework called Hydragen, which is able to execute the Hera-S models to run the desired Web application. Hera-S' core is the Application Model (AM) in which the main logic of the application is defined, i.e. defining the groups of data elements that form logical units or subunits, the personalization conditions, and the relationships between the units. Hera-S also uses a so-called Domain Model (DM) that describes the content and its structure. However, this DM is not Hera-S specific, but instead allows any Semantic Web source representation as its DM, as long as its content can be queried by the standardized Semantic Web query language SPARQL. The same holds for the User Model (UM). The UM can be used for personalization conditions, but also as a source of user-related content if necessary. In fact, the difference between DM and UM is conceptual as their implementation within Hydragen is the same. Hera-S also defines a presentation model (PM) which defines presentation details of elements like order and style. In order to help designers with building their Web applications we have introduced a toolset, Hera Studio, which allows to build the different models graphically. Hera Studio also provides some additional functionality like model checking and deployment of the models in Hydragen. Both Hera-S and its implementation Hydragen are designed to be flexible regarding the user of models. In order to achieve this Hydragen is a stateless engine that queries for relevant information from the models at every page request. This allows the models and data to be changed in the datastore during runtime. We show that one way to exploit this flexibility is by applying aspect-orientation to the AM. Aspect-orientation allows us to dynamically inject functionality that pervades the entire application. Another way to exploit Hera-S' flexibility is in reusing specialized components, e.g. for presentation generation. We present a configuration of Hydragen in which we replace our native presentation generation functionality by the AMACONT engine. AMACONT provides more extensive multi-level presentation generation and adaptation capabilities as well aspect-orientation and a form of semantic based adaptation. Hera-S was designed to allow the (re-)use of any (Semantic) Web datasource. It even opens up the possibility for data integration at the back end, by using an extendible storage layer in our database of choice Sesame. However, even though theoretically possible it still leaves much of the actual data integration issue. As this is a recurring issue in many domains, a broader challenge than for Hera-S design only, we decided to look at this issue in isolation. We present a framework called Relco which provides a language to express data transformation operations as well as a collection of techniques that can be used to (semi-)automatically find relationships between concepts in different ontologies. This is done with a combination of syntactic, semantic and collaboration techniques, which together provide strong clues for which concepts are most likely related. In order to prove the applicability of Relco we explore five application scenarios in different domains for which data integration is a central aspect. This includes a cultural heritage portal, Explorer, for which data from several datasources was integrated and was made available by a mapview, a timeline and a graph view. Explorer also allows users to provide metadata for objects via a tagging mechanism. Another application is SenSee: an electronic TV-guide and recommender. TV-guide data was integrated and enriched with semantically structured data from several sources. Recommendations are computed by exploiting the underlying semantic structure. ViTa was a project in which several techniques for tagging and searching educational videos were evaluated. This includes scenarios in which user tags are related with an ontology, or other tags, using the Relco framework. The MobiLife project targeted the facilitation of a new generation of mobile applications that would use context-based personalization. This can be done using a context-based user profiling platform that can also be used for user model data exchange between mobile applications using technologies like Relco. The final application scenario that is shown is from the GRAPPLE project which targeted the integration of adaptive technology into current learning management systems. A large part of this integration is achieved by using a user modeling component framework in which any application can store user model information, but which can also be used for the exchange of user model data
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