Search (51 results, page 1 of 3)

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Classification

006.7 22

Date

7. 3.2007 19:30:22

DDC

006.7 22

Footnote

Semantic web technologies are explained, and ontology representation is emphasized. There is an excellent summary of the fundamental theory behind applying a knowledge-engineering approach to vision problems. This summary represents the concept of the semantic web and multimedia content analysis. A definition of the fuzzy knowledge representation that can be used for realization in multimedia content applications has been provided, with a comprehensive analysis. The second part of the book introduces the multimedia content analysis approaches and applications. In addition, some examples of methods applicable to multimedia content analysis are presented. Multimedia content analysis is a very diverse field and concerns many other research fields at the same time; this creates strong diversity issues, as everything from low-level features (e.g., colors, DCT coefficients, motion vectors, etc.) up to the very high and semantic level (e.g., Object, Events, Tracks, etc.) are involved. The second part includes topics on structure identification (e.g., shot detection for video sequences), and object-based video indexing. These conventional analysis methods are supplemented by results on semantic multimedia analysis, including three detailed chapters on the development and use of knowledge models for automatic multimedia analysis. Starting from object-based indexing and continuing with machine learning, these three chapters are very logically organized. Because of the diversity of this research field, including several chapters of recent research results is not sufficient to cover the state of the art of multimedia. The editors of the book should write an introductory chapter about multimedia content analysis approaches, basic problems, and technical issues and challenges, and try to survey the state of the art of the field and thus introduce the field to the reader.

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Abstract

The basic goal of education within a discipline is to transform a novice into an expert. This entails moving the novice toward the "semantic space" that the expert inhabits-the space of concepts, meanings, vocabularies, and other intellectual constructs that comprise the discipline. Metadata is significant to this goal in digitally mediated education environments. Encoding the experts' semantic space not only enables the sharing of semantics among discipline scientists, but also creates an environment that bridges the semantic gap between the common vocabulary of the novice and the granular descriptive language of the seasoned scientist (Greenberg, et al, 2005). Developments underlying the Semantic Web, where vocabularies are formalized in the Web Ontology Language (OWL), and Web 2.0 approaches of user-generated folksonomies provide an infrastructure for linking vocabulary systems and promoting group learning via metadata literacy. Group learning is a pedagogical approach to teaching that harnesses the phenomenon of "collective intelligence" to increase learning by means of collaboration. Learning a new semantic system can be daunting for a novice, and yet it is integral to advance one's knowledge in a discipline and retain interest. These ideas are key to the "BOT 2.0: Botany through Web 2.0, the Memex and Social Learning" project (Bot 2.0).72 Bot 2.0 is a collaboration involving the North Carolina Botanical Garden, the UNC SILS Metadata Research center, and the Renaissance Computing Institute (RENCI). Bot 2.0 presents a curriculum utilizing a memex as a way for students to link and share digital information, working asynchronously in an environment beyond the traditional classroom. Our conception of a memex is not a centralized black box but rather a flexible, distributed framework that uses the most salient and easiest-to-use collaborative platforms (e.g., Facebook, Flickr, wiki and blog technology) for personal information management. By meeting students "where they live" digitally, we hope to attract students to the study of botanical science. A key aspect is to teach students scientific terminology and about the value of metadata, an inherent function in several of the technologies and in the instructional approach we are utilizing. This poster will report on a study examining the value of both folksonomies and taxonomies for post-secondary college students learning plant identification. Our data is drawn from a curriculum involving a virtual independent learning portion and a "BotCamp" weekend at UNC, where students work with digital plan specimens that they have captured. Results provide some insight into the importance of collaboration and shared vocabulary for gaining confidence and for student progression from novice to expert in botany.

Source

Metadata for semantic and social applications : proceedings of the International Conference on Dublin Core and Metadata Applications, Berlin, 22 - 26 September 2008, DC 2008: Berlin, Germany / ed. by Jane Greenberg and Wolfgang Klas

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Abstract

This article introduces the Simple Knowledge Organisation System (SKOS), a Semantic Web language for representing controlled structured vocabularies, including thesauri, classification schemes, subject heading systems and taxonomies. SKOS provides a framework for publishing thesauri, classification schemes, and subject indexes on the Web, and for applying these systems to resource collections that are part of the SemanticWeb. SemanticWeb applications may harvest and merge SKOS data, to integrate and enhances retrieval service across multiple collections (e.g. libraries). This article also describes some alternatives for integrating Semantic Web services based on the Resource Description Framework (RDF) and SKOS into a distributed enterprise architecture.

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Abstract

Linked open data allow interlinking and integrating any kind of data on the web. Links between various data sources play a key role insofar as they allow software applications (e.g., browsers, search engines) to operate over the aggregated data space as if it was a unique local database. In this new data space, where DBpedia, a data set including structured information from Wikipedia, seems to be the central hub, we analyzed and highlighted outgoing links from this hub in an effort to discover broken links. The paper reports on an experiment to examine the causes of broken links and proposes some treatments for solving this problem.

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Date

22. 9.2007 15:41:14

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Content

"Textual content-based search engines for the web have a number of limitations. Firstly, many web resources have little or no textual content (images, audio or video streams etc.) Secondly, precision is low where natural language terms have overloaded meaning (e.g. 'bank', 'watch', 'chip' etc.) Thirdly, recall is incomplete where the search does not take account of synonyms or quasi-synonyms. Fourthly, there is no basis for assisting a user in modifying (expanding, refining, translating) a search based on the meaning of the original search. Fifthly, there is no basis for searching across natural languages, or framing search queries in terms of symbolic languages. The Semantic Web is a framework for creating, managing, publishing and searching semantically rich metadata for web resources. Annotating web resources with precise and meaningful statements about conceptual aspects of their content provides a basis for overcoming all of the limitations of textual content-based search engines listed above. Creating this type of metadata requires that metadata generators are able to refer to shared repositories of meaning: 'vocabularies' of concepts that are common to a community, and describe the domain of interest for that community.
This type of effort is common in the digital library community, where a group of experts will interact with a user community to create a thesaurus for a specific domain (e.g. the Art & Architecture Thesaurus AAT AAT) or an overarching classification scheme (e.g. the Dewey Decimal Classification). A similar type of activity is being undertaken more recently in a less centralised manner by web communities, producing for example the DMOZ web directory DMOZ, or the Topic Exchange for weblog topics Topic Exchange. The web, including the semantic web, provides a medium within which communities can interact and collaboratively build and use vocabularies of concepts. A simple language is required that allows these communities to express the structure and content of their vocabularies in a machine-understandable way, enabling exchange and reuse. The Resource Description Framework (RDF) is an ideal language for making statements about web resources and publishing metadata. However, RDF provides only the low level semantics required to form metadata statements. RDF vocabularies must be built on top of RDF to support the expression of more specific types of information within metadata. Ontology languages such as OWL OWL add a layer of expressive power to RDF, and provide powerful tools for defining complex conceptual structures, which can be used to generate rich metadata. However, the class-oriented, logically precise modelling required to construct useful web ontologies is demanding in terms of expertise, effort, and therefore cost. In many cases this type of modelling may be superfluous or unsuited to requirements. Therefore there is a need for a language for expressing vocabularies of concepts for use in semantically rich metadata, that is powerful enough to support semantically enhanced search, but simple enough to be undemanding in terms of the cost and expertise required to use it."

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Abstract

Wikis are established means for the collaborative authoring, versioning and publishing of textual articles. The Wikipedia project, for example, succeeded in creating the by far largest encyclopedia just on the basis of a wiki. Recently, several approaches have been proposed on how to extend wikis to allow the creation of structured and semantically enriched content. However, the means for creating semantically enriched structured content are already available and are, although unconsciously, even used by Wikipedia authors. In this article, we present a method for revealing this structured content by extracting information from template instances. We suggest ways to efficiently query the vast amount of extracted information (e.g. more than 8 million RDF statements for the English Wikipedia version alone), leading to astonishing query answering possibilities (such as for the title question). We analyze the quality of the extracted content, and propose strategies for quality improvements with just minor modifications of the wiki systems being currently used.

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Abstract

Many Semantic Web initiatives improve the capabilities of machines to exchange the meaning of information with other machines. These efforts lead to an increased quality of the application's results, but their user interfaces take little or no advantage of the semantic richness. For example, an ontology-based search engine will use its ontology when evaluating the user's query (e.g. for query formulation, disambiguation or evaluation), but fails to use it to significantly enrich the presentation of the results to a human user. For example, one could imagine replacing the endless list of hits with a structured presentation based on the semantic properties of the hits. Another problem is that the modelling of a domain is done from a single perspective (most often that of the information provider). Therefore, presentation based on the resulting ontology is unlikely to satisfy the needs of all the different types of users of the information. So even assuming an ontology for the domain is in place, mapping that ontology to the needs of individual users - based on their tasks, expertise and personal preferences - is not trivial.

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Abstract

Libraries and museums developed rules for the description of their collections prior to formalizing the underlying conceptualization reflected in such rules. That formalizing process took place in the 1990s and resulted in two independent conceptual models: FRBR for bibliographic information (published in 1998), and CIDOC CRM for museum information (developed from 1996 on, and issued as ISO standard 21127 in 2006). An international working group was formed in 2003 with the purpose of harmonizing these two models. The resulting model, FRBROO, was published in 2009. It is an extension to CIDOC CRM, using the formalism in which the former is written. It adds to FRBR the dynamic aspects of CIDOC CRM, and a number of refinements (e.g. in the definitions of Work and Manifestation). Some modifications were made in CIDOC CRM as well. FRBROO was developed with Semantic Web technologies in mind, and lends itself well to the Linked Data environment; but will it be used in that context?

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Date

22. 9.2007 15:41:14

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22. 9.2007 15:41:14

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Content

Vgl.: http%3A%2F%2Fdigbib.ubka.uni-karlsruhe.de%2Fvolltexte%2Fdocuments%2F1627&ei=tAtYUYrBNoHKtQb3l4GYBw&usg=AFQjCNHeaxKkKU3-u54LWxMNYGXaaDLCGw&sig2=8WykXWQoDKjDSdGtAakH2Q&bvm=bv.44442042,d.Yms.

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Abstract

The structural and syntactic web put in place in the early 90s is still much the same as what we use today: resources (web pages, files, etc.) connected by untyped hyperlinks. By untyped, we mean that there is no easy way for a computer to figure out what a link between two pages means - for example, on the W3C website, there are hundreds of links to the various organisations that are registered members of the association, but there is nothing explicitly saying that the link is to an organisation that is a "member of" the W3C or what type of organisation is represented by the link. On John's work page, he links to many papers he has written, but it does not explicitly say that he is the author of those papers or that he wrote such-and-such when he was working at a particular university. In fact, the Web was envisaged to be much more, as one can see from the image in Fig. 1 which is taken from Tim Berners Lee's original outline for the Web in 1989, entitled "Information Management: A Proposal". In this, all the resources are connected by links describing the type of relationships, e.g. "wrote", "describe", "refers to", etc. This is a precursor to the Semantic Web which we will come back to later.

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Abstract

Overlapping structures in XML are not symptoms of a misunderstanding of the intrinsic characteristics of a text document nor evidence of extreme scholarly requirements far beyond those needed by the most common XML-based applications. On the contrary, overlaps have started to appear in a large number of incredibly popular applications hidden under the guise of syntactical tricks to the basic hierarchy of the XML data format. Unfortunately, syntactical tricks have the drawback that the affected structures require complicated workarounds to support even the simplest query or usage. In this article, we present Extremely Annotational Resource Description Framework (RDF) Markup (EARMARK), an approach to overlapping markup that simplifies and streamlines the management of multiple hierarchies on the same content, and provides an approach to sophisticated queries and usages over such structures without the need of ad-hoc applications, simply by using Semantic Web tools and languages. We compare how relevant tasks (e.g., the identification of the contribution of an author in a word processor document) are of some substantial complexity when using the original data format and become more or less trivial when using EARMARK. We finally evaluate positively the memory and disk requirements of EARMARK documents in comparison to Open Office and Microsoft Word XML-based formats.

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Abstract

Robertson and Spärck Jones pioneered experimental probabilistic models (Binary Independence Model) with both a typology generalizing the Boolean model, a frequency counting to calculate elementary weightings, and their combination into a global probabilistic estimation. However, this model did not consider indexing terms dependencies. An extension to mixture models (e.g., using a 2-Poisson law) made it possible to take into account these dependencies from a macroscopic point of view (BM25), as well as a shallow linguistic processing of co-references. New approaches (language models, for example "bag of words" models, probabilistic dependencies between requests and documents, and consequently Bayesian inference using Dirichlet prior conjugate) furnished new solutions for documents structuring (categorization) and for index smoothing. Presently, in these probabilistic models the main issues have been addressed from a formal point of view only. Thus, linguistic properties are neglected in the indexing language. The authors examine how a linguistic and semantic modeling can be integrated in indexing languages and set up a hybrid model that makes it possible to deal with different information retrieval problems in a unified way.

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Abstract

The resource description framework (RDF for short) provides a flexible method for modeling information on the Web [34,40]. All data items in RDF are uniformly represented as triples of the form (subject, predicate, object), sometimes also referred to as (subject, property, value) triples. As a running example for this chapter, a small fragment of an RDF dataset concerning music and music fans is given in Fig. 2.1. Spurred by efforts like the Linking Open Data project, increasingly large volumes of data are being published in RDF. Notable contributors in this respect include areas as diverse as the government, the life sciences, Web 2.0 communities, and so on. To give an idea of the volumes of RDF data concerned, as of September 2012, there are 31,634,213,770 triples in total published by data sources participating in the Linking Open Data project. Many individual data sources (like, e.g., PubMed, DBpedia, MusicBrainz) contain hundreds of millions of triples (797, 672, and 179 millions, respectively). These large volumes of RDF data motivate the need for scalable native RDF data management solutions capabable of efficiently storing, indexing, and querying RDF data. In this chapter, we present a general and up-to-date survey of the current state of the art in RDF storage and indexing.

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Abstract

We are witnessing a smooth evolution of the Web from a worldwide information space of linked documents to a global knowledge base, where resources are identified by means of uniform resource identifiers (URIs, essentially string identifiers) and are semantically described and correlated through resource description framework (RDF, a metadata data model) statements. With the size and availability of data constantly increasing (currently around 7 billion RDF triples and 150 million RDF links), a fundamental problem lies in the difficulty users face to find and retrieve the information they are interested in. In general, to access semantic data, users need to know the organization of data and the syntax of a specific query language (e.g., SPARQL or variants thereof). Clearly, this represents an obstacle to information access for nonexpert users. For this reason, keyword search-based systems are increasingly capturing the attention of researchers. Recently, many approaches to keyword-based search over structured and semistructured data have been proposed]. These approaches usually implement IR strategies on top of traditional database management systems with the goal of freeing the users from having to know data organization and query languages.

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Abstract

With the advance of the semantic Web, increasing amounts of data are available in a structured and machine-understandable form. This opens opportunities for users to employ semantic queries instead of simple keyword-based ones to accurately express the information need. However, constructing semantic queries is a demanding task for human users [11]. To compose a valid semantic query, a user has to (1) master a query language (e.g., SPARQL) and (2) acquire sufficient knowledge about the ontology or the schema of the data source. While there are systems which support this task with visual tools [21, 26] or natural language interfaces [3, 13, 14, 18], the process of query construction can still be complex and time consuming. According to [24], users prefer keyword search, and struggle with the construction of semantic queries although being supported with a natural language interface. Several keyword search approaches have already been proposed to ease information seeking on semantic data [16, 32, 35] or databases [1, 31]. However, keyword queries lack the expressivity to precisely describe the user's intent. As a result, ranking can at best put query intentions of the majority on top, making it impossible to take the intentions of all users into consideration.

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Abstract

Access to external information is an important need for Answer Set Programming (ASP), which is a booming declarative problem solving approach these days. External access not only includes data in different formats, but more general also the results of computations, and possibly in a two-way information exchange. Providing such access is a major challenge, and in particular if it should be supported at a generic level, both regarding the semantics and efficient computation. In this article, we consider problem solving with ASP under external information access using the dlvhex system. The latter facilitates this access through special external atoms, which are two-way API style interfaces between the rules of the program and an external source. The dlvhex system has a flexible plugin architecture that allows one to use multiple predefined and user-defined external atoms which can be implemented, e.g., in Python or C++. We consider how to solve problems using the ASP paradigm, and specifically discuss how to use external atoms in this context, illustrated by examples. As a showcase, we demonstrate the development of a hex program for a concrete real-world problem using Semantic Web technologies, and discuss specifics of the implementation process.

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Source

Metadata and semantics research: 10th International Conference, MTSR 2016, Göttingen, Germany, November 22-25, 2016, Proceedings. Eds.: E. Garoufallou