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Abstract

Today's semantic web deals with meaning in a very restricted sense and offers static solutions. This is adequate for many scientific, technical purposes and for business transactions requiring machine-to-machine communication, but does not answer the needs of culture. Science, technology and business are concerned primarily with the latest findings, the state of the art, i.e. the paradigm or dominant world-view of the day. In this context, history is considered non-essential because it deals with things that are out of date. By contrast, culture faces a much larger challenge, namely, to re-present changes in ways of knowing; changing meanings in different places at a given time (synchronically) and over time (diachronically). Culture is about both objects and the commentaries on them; about a cumulative body of knowledge; about collective memory and heritage. Here, history plays a central role and older does not mean less important or less relevant. Hence, a Leonardo painting that is 400 years old, or a Greek statue that is 2500 years old, typically have richer commentaries and are often more valuable than their contemporary equivalents. In this context, the science of meaning (semantics) is necessarily much more complex than semantic primitives. A semantic web in the cultural domain must enable us to trace how meaning and knowledge organisation have evolved historically in different cultures. This paper examines five issues to address this challenge: 1) different world-views (i.e. a shift from substance to function and from ontology to multiple ontologies); 2) developments in definitions and meaning; 3) distinctions between words and concepts; 4) new classes of relations; and 5) dynamic models of knowledge organisation. These issues reveal that historical dimensions of cultural diversity in knowledge organisation are also central to classification of biological diversity. New ways are proposed of visualizing knowledge using a time/space horizon to distinguish between universals and particulars. It is suggested that new visualization methods make possible a history of questions as well as of answers, thus enabling dynamic access to cultural and historical dimensions of knowledge. Unlike earlier media, which were limited to recording factual dimensions of collective memory, digital media enable us to explore theories, ways of perceiving, ways of knowing; to enter into other mindsets and world-views and thus to attain novel insights and new levels of tolerance. Some practical consequences are outlined.

Type

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Abstract

This is a specification of a precise semantics, and corresponding complete systems of inference rules, for the Resource Description Framework (RDF) and RDF Schema (RDFS).

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Abstract

Over the past 3 years, the semantic web activity has gained momentum with the widespread publishing of structured data as RDF. The Linked Data paradigm has therefore evolved from a practical research idea into a very promising candidate for addressing one of the biggest challenges in the area of the Semantic Web vision: the exploitation of the Web as a platform for data and information integration. To translate this initial success into a world-scale reality, a number of research challenges need to be addressed: the performance gap between relational and RDF data management has to be closed, coherence and quality of data published on theWeb have to be improved, provenance and trust on the Linked Data Web must be established and generally the entrance barrier for data publishers and users has to be lowered. In this vision statement we discuss these challenges and argue, that research approaches tackling these challenges should be integrated into a mutual refinement cycle. We also present two crucial use-cases for the widespread adoption of linked data.

Type

a

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Abstract

This document provides a tutorial on how to publish Linked Data on the Web. After a general overview of the concept of Linked Data, we describe several practical recipes for publishing information as Linked Data on the Web.

Content

This tutorial has been superseeded by the book Linked Data: Evolving the Web into a Global Data Space written by Tom Heath and Christian Bizer. This tutorial was published in 2007 and is still online for historical reasons. The Linked Data book was published in 2011 and provides a more detailed and up-to-date introduction into Linked Data.

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Abstract

The Web Ontology Language OWL is a semantic markup language for publishing and sharing ontologies on the World Wide Web. OWL is developed as a vocabulary extension of RDF (the Resource Description Framework) and is derived from the DAML+OIL Web Ontology Language. This document contains a structured informal description of the full set of OWL language constructs and is meant to serve as a reference for OWL users who want to construct OWL ontologies.

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Abstract

We describe an apporach to retrieval of documents that consist of both free text and semantically enriched markup. In particular, we present the design and implementation prototype of a framework in which both documents and queries can be marked up with statements in the DAML+OIL semantic web language. These statement provide both structured and semi-structured information about the documents and their content. We claim that indexing text and semantic markup will significantly improve retrieval performance. Outr approach allows inferencing to be done over this information at several points: when a document is indexed,when a query is processed and when query results are evaluated.

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Abstract

This paper presents the idea and our work of extracting and reassembling a genealogical network automatically from a collection of biographies. The network can be used as a tool for network analysis of historical persons. The data has been published as Linked Data and as an interactive online service as part of the in-use data service and semantic portal BiographySampo - Finnish Biographies on the Semantic Web.

Type

a

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Abstract

This document specifies XML presentation syntax for OWL, which is defined as a dialect similar to OWL Abstract Syntax [OWL Semantics]. It is not intended to be a normative specification. Instead, it represents a suggestion of one possible XML presentation syntax for OWL.

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Abstract

This document specifies usage scenarios, goals and requirements for a web ontology language. An ontology formally defines a common set of terms that are used to describe and represent a domain. Ontologies can be used by automated tools to power advanced services such as more accurate web search, intelligent software agents and knowledge management.

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Content

"When dealing with a large-scale and widely-used knowledge organization system like the Dewey Decimal Classification, we often tend to focus solely on the organization aspect, which is closely intertwined with editorial work. This is perfectly understandable, since developing and updating the DDC, keeping up with current scientific developments, spotting new trends in both scholarly communication and popular publishing, and figuring out how to fit those patterns into the structure of the scheme are as intriguing as they are challenging. From the organization perspective, the intended user of the scheme is mainly the classifier. Dewey acts very much as a number-building engine, providing richly documented concepts to help with classification decisions. Since the Middle Ages, quasi-religious battles have been fought over the "valid" arrangement of places according to specific views of the world, as parodied by Jorge Luis Borges and others. Organizing knowledge has always been primarily an ontological activity; it is about putting the world into the classification. However, there is another side to this coin--the discovery side. While the hierarchical organization of the DDC establishes a default set of places and neighborhoods that is also visible in the physical manifestation of library shelves, this is just one set of relationships in the DDC. A KOS (Knowledge Organization System) becomes powerful by expressing those other relationships in a manner that not only collocates items in a physical place but in a knowledge space, and exposes those other relationships in ways beneficial and congenial to the unique perspective of an information seeker.
What are those "other" relationships that Dewey possesses and that seem so important to surface? Firstly, there is the relationship of concepts to resources. Dewey has been used for a long time, and over 200,000 numbers are assigned to information resources each year and added to WorldCat by the Library of Congress and the German National Library alone. Secondly, we have relationships between concepts in the scheme itself. Dewey provides a rich set of non-hierarchical relations, indicating other relevant and related subjects across disciplinary boundaries. Thirdly, perhaps most importantly, there is the relationship between the same concepts across different languages. Dewey has been translated extensively, and current versions are available in French, German, Hebrew, Italian, Spanish, and Vietnamese. Briefer representations of the top-three levels (the DDC Summaries) are available in several languages in the DeweyBrowser. This multilingual nature of the scheme allows searchers to access a broader range of resources or to switch the language of--and thus localize--subject metadata seamlessly. MelvilClass, a Dewey front-end developed by the German National Library for the German translation, could be used as a common interface to the DDC in any language, as it is built upon the standard DDC data format. It is not hard to give an example of the basic terminology of a class pulled together in a multilingual way: <class/794.8> a skos:Concept ; skos:notation "794.8"^^ddc:notation ; skos:prefLabel "Computer games"@en ; skos:prefLabel "Computerspiele"@de ; skos:prefLabel "Jeux sur ordinateur"@fr ; skos:prefLabel "Juegos por computador"@es .
Expressed in such manner, the Dewey number provides a language-independent representation of a Dewey concept, accompanied by language-dependent assertions about the concept. This information, identified by a URI, can be easily consumed by semantic web agents and used in various metadata scenarios. Fourthly, as we have seen, it is important to play well with others, i.e., establishing and maintaining relationships to other KOS and making the scheme available in different formats. As noted in the Dewey blog post "Tags and Dewey," since no single scheme is ever going to be the be-all, end-all solution for knowledge discovery, DDC concepts have been extensively mapped to other vocabularies and taxonomies, sometimes bridging them and acting as a backbone, sometimes using them as additional access vocabulary to be able to do more work "behind the scenes." To enable other applications and schemes to make use of those relationships, the full Dewey database is available in XML format; RDF-based formats and a web service are forthcoming. Pulling those relationships together under a common surface will be the next challenge going forward. In the semantic web community the concept of Linked Data (http://en.wikipedia.org/wiki/Linked_Data) currently receives some attention, with its emphasis on exposing and connecting data using technologies like URIs, HTTP and RDF to improve information discovery on the web. With its focus on relationships and discovery, it seems that Dewey will be well prepared to become part of this big linked data set. Now it is about putting the classification back into the world!"

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Abstract

18 August 2009 -- Today W3C announces a new standard that builds a bridge between the world of knowledge organization systems - including thesauri, classifications, subject headings, taxonomies, and folksonomies - and the linked data community, bringing benefits to both. Libraries, museums, newspapers, government portals, enterprises, social networking applications, and other communities that manage large collections of books, historical artifacts, news reports, business glossaries, blog entries, and other items can now use Simple Knowledge Organization System (SKOS) to leverage the power of linked data. As different communities with expertise and established vocabularies use SKOS to integrate them into the Semantic Web, they increase the value of the information for everyone.

Content

SKOS Adapts to the Diversity of Knowledge Organization Systems A useful starting point for understanding the role of SKOS is the set of subject headings published by the US Library of Congress (LOC) for categorizing books, videos, and other library resources. These headings can be used to broaden or narrow queries for discovering resources. For instance, one can narrow a query about books on "Chinese literature" to "Chinese drama," or further still to "Chinese children's plays." Library of Congress subject headings have evolved within a community of practice over a period of decades. By now publishing these subject headings in SKOS, the Library of Congress has made them available to the linked data community, which benefits from a time-tested set of concepts to re-use in their own data. This re-use adds value ("the network effect") to the collection. When people all over the Web re-use the same LOC concept for "Chinese drama," or a concept from some other vocabulary linked to it, this creates many new routes to the discovery of information, and increases the chances that relevant items will be found. As an example of mapping one vocabulary to another, a combined effort from the STITCH, TELplus and MACS Projects provides links between LOC concepts and RAMEAU, a collection of French subject headings used by the Bibliothèque Nationale de France and other institutions. SKOS can be used for subject headings but also many other approaches to organizing knowledge. Because different communities are comfortable with different organization schemes, SKOS is designed to port diverse knowledge organization systems to the Web. "Active participation from the library and information science community in the development of SKOS over the past seven years has been key to ensuring that SKOS meets a variety of needs," said Thomas Baker, co-chair of the Semantic Web Deployment Working Group, which published SKOS. "One goal in creating SKOS was to provide new uses for well-established knowledge organization systems by providing a bridge to the linked data cloud." SKOS is part of the Semantic Web technology stack. Like the Web Ontology Language (OWL), SKOS can be used to define vocabularies. But the two technologies were designed to meet different needs. SKOS is a simple language with just a few features, tuned for sharing and linking knowledge organization systems such as thesauri and classification schemes. OWL offers a general and powerful framework for knowledge representation, where additional "rigor" can afford additional benefits (for instance, business rule processing). To get started with SKOS, see the SKOS Primer.

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Abstract

Thanks to the recent Linked Data initiative, the foundations of the Semantic Web have been built. Shared, open and linked RDF datasets give us the possibility to exploit both the strong theoretical results and the robust technologies and tools developed since the seminal paper in the Semantic Web appeared in 2001. In a simplistic way, we may think at the Semantic Web as a ultra large distributed database we can query to get information coming from different sources. In fact, every dataset exposes a SPARQL endpoint to make the data accessible through exact queries. If we know the URI of the famous actress Nicole Kidman in DBpedia we may retrieve all the movies she acted with a simple SPARQL query. Eventually we may aggregate this information with users ratings and genres from IMDB. Even though these are very exciting results and applications, there is much more behind the curtains. Datasets come with the description of their schema structured in an ontological way. Resources refer to classes which are in turn organized in well structured and rich ontologies. Exploiting also this further feature we go beyond the notion of a distributed database and we can refer to the Semantic Web as a distributed knowledge base. If in our knowledge base we have that Paris is located in France (ontological level) and that Moulin Rouge! is set in Paris (data level) we may query the Semantic Web (interpreted as a set of interconnected datasets and related ontologies) to return all the movies starred by Nicole Kidman set in France and Moulin Rouge! will be in the final result set. The ontological level makes possible to infer new relations among data.
The Linked Data initiative and the state of the art in semantic technologies led off all brand new search and mash-up applications. The basic idea is to have smarter lookup services for a huge, distributed and social knowledge base. All these applications catch and (re)propose, under a semantic data perspective, the view of the classical Web as a distributed collection of documents to retrieve. The interlinked nature of the Web, and consequently of the Semantic Web, is exploited (just) to collect and aggregate data coming from different sources. Of course, this is a big step forward in search and Web technologies, but if we limit our investi- gation to retrieval tasks, we miss another important feature of the current Web: browsing and in particular exploratory browsing (a.k.a. exploratory search). Thanks to its hyperlinked nature, the Web defined a new way of browsing documents and knowledge: selection by lookup, navigation and trial-and-error tactics were, and still are, exploited by users to search for relevant information satisfying some initial requirements. The basic assumptions behind a lookup search, typical of Information Retrieval (IR) systems, are no more valid in an exploratory browsing context. An IR system, such as a search engine, assumes that: the user has a clear picture of what she is looking for ; she knows the terminology of the specific knowledge space. On the other side, as argued in, the main challenges in exploratory search can be summarized as: support querying and rapid query refinement; other facets and metadata-based result filtering; leverage search context; support learning and understanding; other visualization to support insight/decision making; facilitate collaboration. In Section 3 we will show two applications for exploratory search in the Semantic Web addressing some of the above challenges.

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Abstract

DBpedia is a community effort to extract structured information from Wikipedia and to make this information available on the Web. DBpedia allows you to ask sophisticated queries against datasets derived from Wikipedia and to link other datasets on the Web to Wikipedia data. We describe the extraction of the DBpedia datasets, and how the resulting information is published on the Web for human- and machineconsumption. We describe some emerging applications from the DBpedia community and show how website authors can facilitate DBpedia content within their sites. Finally, we present the current status of interlinking DBpedia with other open datasets on the Web and outline how DBpedia could serve as a nucleus for an emerging Web of open data.

Type

a

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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.

Type

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Content

"Swoogle, the Semantic web search engine, is a research project carried out by the ebiquity research group in the Computer Science and Electrical Engineering Department at the University of Maryland. It's an engine tailored towards finding documents on the semantic web. The whole research paper is available here. Semantic web is touted as the next generation of online content representation where the web documents are represented in a language that is not only easy for humans but is machine readable (easing the integration of data as never thought possible) as well. And the main elements of the semantic web include data model description formats such as Resource Description Framework (RDF), a variety of data interchange formats (e.g. RDF/XML, Turtle, N-Triples), and notations such as RDF Schema (RDFS), the Web Ontology Language (OWL), all of which are intended to provide a formal description of concepts, terms, and relationships within a given knowledge domain (Wikipedia). And Swoogle is an attempt to mine and index this new set of web documents. The engine performs crawling of semantic documents like most web search engines and the search is available as web service too. The engine is primarily written in Java with the PHP used for the front-end and MySQL for database. Swoogle is capable of searching over 10,000 ontologies and indexes more that 1.3 million web documents. It also computes the importance of a Semantic Web document. The techniques used for indexing are the more google-type page ranking and also mining the documents for inter-relationships that are the basis for the semantic web. For more information on how the RDF framework can be used to relate documents, read the link here. Being a research project, and with a non-commercial motive, there is not much hype around Swoogle. However, the approach to indexing of Semantic web documents is an approach that most engines will have to take at some point of time. When the Internet debuted, there were no specific engines available for indexing or searching. The Search domain only picked up as more and more content became available. One fundamental question that I've always wondered about it is - provided that the search engines return very relevant results for a query - how to ascertain that the documents are indeed the most relevant ones available. There is always an inherent delay in indexing of document. Its here that the new semantic documents search engines can close delay. Experimenting with the concept of Search in the semantic web can only bore well for the future of search technology."

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Abstract

In this paper, we discuss the architecture and implementation of the Semantic Web Search Engine (SWSE). Following traditional search engine architecture, SWSE consists of crawling, data enhancing, indexing and a user interface for search, browsing and retrieval of information; unlike traditional search engines, SWSE operates over RDF Web data - loosely also known as Linked Data - which implies unique challenges for the system design, architecture, algorithms, implementation and user interface. In particular, many challenges exist in adopting Semantic Web technologies for Web data: the unique challenges of the Web - in terms of scale, unreliability, inconsistency and noise - are largely overlooked by the current Semantic Web standards. Herein, we describe the current SWSE system, initially detailing the architecture and later elaborating upon the function, design, implementation and performance of each individual component. In so doing, we also give an insight into how current Semantic Web standards can be tailored, in a best-effort manner, for use on Web data. Throughout, we offer evaluation and complementary argumentation to support our design choices, and also offer discussion on future directions and open research questions. Later, we also provide candid discussion relating to the difficulties currently faced in bringing such a search engine into the mainstream, and lessons learnt from roughly six years working on the Semantic Web Search Engine project.

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Abstract

The current web consists of documents that are highly heterogeneous and hard for machines to understand. The Semantic Web is a progressive movement of the Word Wide Web, aiming at converting the current web of unstructured documents to the web of data. In the Semantic Web, web documents are annotated with metadata using standardized ontology language. These annotated documents are directly processable by machines and it highly improves their usability and usefulness. In Ericsson, similar problems occur. There are massive documents being created with well-defined structures. Though these documents are about domain specific knowledge and can have rich relations, they are currently managed by a traditional search engine, which ignores the rich domain specific information and presents few data to users. Motivated by the Semantic Web, we aim to find standard ways to process these documents, extract rich domain specific information and annotate these data to documents with formal markup languages. We propose this project to develop a domain specific search engine for processing different documents and building explicit relations for them. This research project consists of the three main focuses: examining different domain specific documents and finding ways to extract their metadata; integrating a text search engine with an ontology server; exploring novel ways to build relations for documents. We implement this system and demonstrate its functions. As a prototype, the system provides required features and will be extended in the future.

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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?

Editor

Polleres, A.

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Abstract

The Semantic Web activity is a W3C project whose goal is to enable a 'cooperative' Web where machines and humans can exchange electronic content that has clear-cut, unambiguous meaning. This vision is based on the automated sharing of metadata terms across Web applications. The declaration of schemas in metadata registries advance this vision by providing a common approach for the discovery, understanding, and exchange of semantics. However, many of the issues regarding registries are not clear, and ideas vary regarding their scope and purpose. Additionally, registry issues are often difficult to describe and comprehend without a working example. This article will explore the role of metadata registries and will describe three prototypes, written by the Dublin Core Metadata Initiative. The article will outline how the prototypes are being used to demonstrate and evaluate application scope, functional requirements, and technology solutions for metadata registries. Metadata schema registries are, in effect, databases of schemas that can trace an historical line back to shared data dictionaries and the registration process encouraged by the ISO/IEC 11179 community. New impetus for the development of registries has come with the development activities surrounding creation of the Semantic Web. The motivation for establishing registries arises from domain and standardization communities, and from the knowledge management community. Examples of current registry activity include:
* Agencies maintaining directories of data elements in a domain area in accordance with ISO/IEC 11179 (This standard specifies good practice for data element definition as well as the registration process. Example implementations are the National Health Information Knowledgebase hosted by the Australian Institute of Health and Welfare and the Environmental Data Registry hosted by the US Environmental Protection Agency.); * The xml.org directory of the Extended Markup Language (XML) document specifications facilitating re-use of Document Type Definition (DTD), hosted by the Organization for the Advancement of Structured Information Standards (OASIS); * The MetaForm database of Dublin Core usage and mappings maintained at the State and University Library in Goettingen; * The Semantic Web Agreement Group Dictionary, a database of terms for the Semantic Web that can be referred to by humans and software agents; * LEXML, a multi-lingual and multi-jurisdictional RDF Dictionary for the legal world; * The SCHEMAS registry maintained by the European Commission funded SCHEMAS project, which indexes several metadata element sets as well as a large number of activity reports describing metadata related activities and initiatives. Metadata registries essentially provide an index of terms. Given the distributed nature of the Web, there are a number of ways this can be accomplished. For example, the registry could link to terms and definitions in schemas published by implementers and stored locally by the schema maintainer. Alternatively, the registry might harvest various metadata schemas from their maintainers. Registries provide 'added value' to users by indexing schemas relevant to a particular 'domain' or 'community of use' and by simplifying the navigation of terms by enabling multiple schemas to be accessed from one view. An important benefit of this approach is an increase in the reuse of existing terms, rather than users having to reinvent them. Merging schemas to one view leads to harmonization between applications and helps avoid duplication of effort. Additionally, the establishment of registries to index terms actively being used in local implementations facilitates the metadata standards activity by providing implementation experience transferable to the standards-making process.

Type

a

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Abstract

The Semantic Web and ontologies can help archaeologists combine and share data, making it more open and useful. Archaeologists create diverse types of data, using a wide variety of technologies and methodologies. Like all research domains, these data are increasingly digital. The creation of data that are now openly and persistently available from disparate sources has also inspired efforts to bring archaeological resources together and make them more interoperable. This allows functionality such as federated cross-search across different datasets, and the mapping of heterogeneous data to authoritative structures to build a single data source. Ontologies provide the structure and relationships for Semantic Web data, and have been developed for use in cultural heritage applications generally, and archaeology specifically. A variety of online resources for archaeology now incorporate Semantic Web principles and technologies.