Search (28 results, page 1 of 2)

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Abstract

More and more cultural heritage institutions publish their collections, vocabularies and metadata on the Web. The resulting Web of linked cultural data opens up exciting new possibilities for searching and browsing through these cultural heritage collections. We report on ongoing work in which we investigate the estimation of relevance in this Web of Culture. We study existing measures of semantic distance and how they apply to two use cases. The use cases relate to the structured, multilingual and multimodal nature of the Culture Web. We distinguish between measures using the Web, such as Google distance and PMI, and measures using the Linked Data Web, i.e. the semantic structure of metadata vocabularies. We perform a small study in which we compare these semantic distance measures to human judgements of relevance. Although it is too early to draw any definitive conclusions, the study provides new insights into the applicability of semantic distance measures to the Web of Culture, and clear starting points for further research.

Date

26.12.2011 13:40:22

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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

Type

a

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Date

15. 3.2011 19:21:22

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Date

5. 7.2015 22:02:31

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Abstract

Seven years on from OWL becoming a W3C recommendation, and two years on from the more recent OWL 2 W3C recommendation, OWL has still experienced only patchy uptake on the Web. Although certain OWL features (like owl:sameAs) are very popular, other features of OWL are largely neglected by publishers in the Linked Data world. This may suggest that despite the promise of easy implementations and the proposal of tractable profiles suggested in OWL's second version, there is still no "right" standard fragment for the Linked Data community. In this paper, we (1) analyse uptake of OWL on the Web of Data, (2) gain insights into the OWL fragment that is actually used/usable on the Web, where we arrive at the conclusion that this fragment is likely to be a simplified profile based on OWL RL, (3) propose and discuss such a new fragment, which we call OWL LD (for Linked Data).

Type

a

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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 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

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

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

a

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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 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.

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Abstract

In this paper, I have traced a series of evolutionary adaptations of FOL motivated entirely by its use by knowledge engineers to represent and share information on the Web culminating in the development of Common Logic. While the primary goal in this paper has been to document this evolution, it is arguable, I think that CL's syntactic and semantic egalitarianism better realizes the goal "topic neutrality" that a logic should ideally exemplify - understood, at least in part, as the idea that logic should as far as possible not itself embody any metaphysical presuppositions. Instead of retaining the traditional metaphysical divisions of FOL that reflect its Fregean origins, CL begins as it were with a single, metaphysically homogeneous domain in which, potentially, anything can play the traditional roles of object, property, relation, and function. Note that the effect of this is not to destroy traditional metaphysical divisions. Rather, it simply to refrain from building those divisions explicitly into one's logic; instead, such divisions are left to the user to introduce and enforce axiomatically in an explicit metaphysical theory.

Type

a

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Abstract

In this thesis, we present an ontology-based information extraction and retrieval system and its application to soccer domain. In general, we deal with three issues in semantic search, namely, usability, scalability and retrieval performance. We propose a keyword-based semantic retrieval approach. The performance of the system is improved considerably using domain-specific information extraction, inference and rules. Scalability is achieved by adapting a semantic indexing approach. The system is implemented using the state-of-the-art technologies in SemanticWeb and its performance is evaluated against traditional systems as well as the query expansion methods. Furthermore, a detailed evaluation is provided to observe the performance gain due to domain-specific information extraction and inference. Finally, we show how we use semantic indexing to solve simple structural ambiguities.

Type

a

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Abstract

After the launch of the World Wide Web, it became clear that searching documentson the Web would not be trivial. Well-known engines to search the web, like Google, focus on search in web documents using keywords. The documents are structured and indexed to ensure keywords match documents as accurately as possible. However, searching by keywords does not always suice. It is oen the case that users do not know exactly how to formulate the search query or which keywords guarantee retrieving the most relevant documents. Besides that, it occurs that users rather want to browse information than looking up something specific. It turned out that there is need for systems that enable more interactivity and facilitate the gradual refinement of search queries to explore the Web. Users expect more from the Web because the short keyword-based queries they pose during search, do not suffice for all cases. On top of that, the Web is changing structurally. The Web comprises, apart from a collection of documents, more and more linked data, pieces of information structured so they can be processed by machines. The consequently applied semantics allow users to exactly indicate machines their search intentions. This is made possible by describing data following controlled vocabularies, concept lists composed by experts, published uniquely identifiable on the Web. Even so, it is still not trivial to explore data on the Web. There is a large variety of vocabularies and various data sources use different terms to identify the same concepts.
This PhD-thesis describes how to effectively explore linked data on the Web. The main focus is on scenarios where users want to discover relationships between resources rather than finding out more about something specific. Searching for a specific document or piece of information fits in the theoretical framework of information retrieval and is associated with exploratory search. Exploratory search goes beyond 'looking up something' when users are seeking more detailed understanding, further investigation or navigation of the initial search results. The ideas behind exploratory search and querying linked data merge when it comes to the way knowledge is represented and indexed by machines - how data is structured and stored for optimal searchability. Queries and information should be aligned to facilitate that searches also reveal connections between results. This implies that they take into account the same semantic entities, relevant at that moment. To realize this, we research three techniques that are evaluated one by one in an experimental set-up to assess how well they succeed in their goals. In the end, the techniques are applied to a practical use case that focuses on forming a bridge between the Web and the use of digital libraries in scientific research. Our first technique focuses on the interactive visualization of search results. Linked data resources can be brought in relation with each other at will. This leads to complex and diverse graphs structures. Our technique facilitates navigation and supports a workflow starting from a broad overview on the data and allows narrowing down until the desired level of detail to then broaden again. To validate the flow, two visualizations where implemented and presented to test-users. The users judged the usability of the visualizations, how the visualizations fit in the workflow and to which degree their features seemed useful for the exploration of linked data.
The ideas behind exploratory search and querying linked data merge when it comes to the way knowledge is represented and indexed by machines - how data is structured and stored for optimal searchability. eries and information should be aligned to facilitate that searches also reveal connections between results. This implies that they take into account the same semantic entities, relevant at that moment. To realize this, we research three techniques that are evaluated one by one in an experimental set-up to assess how well they succeed in their goals. In the end, the techniques are applied to a practical use case that focuses on forming a bridge between the Web and the use of digital libraries in scientific research.
Our first technique focuses on the interactive visualization of search results. Linked data resources can be brought in relation with each other at will. This leads to complex and diverse graphs structures. Our technique facilitates navigation and supports a workflow starting from a broad overview on the data and allows narrowing down until the desired level of detail to then broaden again. To validate the flow, two visualizations where implemented and presented to test-users. The users judged the usability of the visualizations, how the visualizations fit in the workflow and to which degree their features seemed useful for the exploration of linked data. There is a difference in the way users interact with resources, visually or textually, and how resources are represented for machines to be processed by algorithms. This difference complicates bridging the users' intents and machine executable queries. It is important to implement this 'translation' mechanism to impact the search as favorable as possible in terms of performance, complexity and accuracy. To do this, we explain a second technique, that supports such a bridging component. Our second technique is developed around three features that support the search process: looking up, relating and ranking resources. The main goal is to ensure that resources in the results are as precise and relevant as possible. During the evaluation of this technique, we did not only look at the precision of the search results but also investigated how the effectiveness of the search evolved while the user executed certain actions sequentially.
When we speak about finding relationships between resources, it is necessary to dive deeper in the structure. The graph structure of linked data where the semantics give meaning to the relationships between resources enable the execution of pathfinding algorithms. The assigned weights and heuristics are base components of such algorithms and ultimately define (the order) which resources are included in a path. These paths explain indirect connections between resources. Our third technique proposes an algorithm that optimizes the choice of resources in terms of serendipity. Some optimizations guard the consistence of candidate-paths where the coherence of consecutive connections is maximized to avoid trivial and too arbitrary paths. The implementation uses the A* algorithm, the de-facto reference when it comes to heuristically optimized minimal cost paths. The effectiveness of paths was measured based on common automatic metrics and surveys where the users could indicate their preference for paths, generated each time in a different way. Finally, all our techniques are applied to a use case about publications in digital libraries where they are aligned with information about scientific conferences and researchers. The application to this use case is a practical example because the different aspects of exploratory search come together. In fact, the techniques also evolved from the experiences when implementing the use case. Practical details about the semantic model are explained and the implementation of the search system is clarified module by module. The evaluation positions the result, a prototype of a tool to explore scientific publications, researchers and conferences next to some important alternatives.

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Abstract

The formal structure of the information on the Semantic Web lends itself to faceted browsing, an information retrieval method where users can filter results based on the values of properties ("facets"). Numerous faceted browsers have been created to browse RDF and Linked Data, but these systems use their own ontologies for defining how data is queried to populate their facets. Since the source data is the same format across these systems (specifically, RDF), we can unify the different methods of describing how to quer the underlying data, to enable compatibility across systems, and provide an extensible base ontology for future systems. To this end, we present FacetOntology, an ontology that defines how to query data to form a faceted browser, and a number of transformations and filters that can be applied to data before it is shown to users. FacetOntology overcomes limitations in the expressivity of existing work, by enabling the full expressivity of SPARQL when selecting data for facets. By applying a FacetOntology definition to data, a set of facets are specified, each with queries and filters to source RDF data, which enables faceted browsing systems to be created using that RDF data.

Type

a

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Abstract

In representing the shared view of all the people involved, building a Knowledge Organization System (KOS) from scratch is extremely costly, and it is therefore fundamental to reuse existing resources. This can be done by progressively extending the KOS with knowledge coming from similar KOS and by promoting interoperability among them. The linked data initiative is indeed fostering people to share and integrate their datasets into a giant network of interconnected resources. This enables different applications to interoperate and share their data. However, the integration should take into account the purpose of the datasets and make explicit the semantics. In fact, the difference in the purpose is reflected in the difference in the semantics. With this paper we (a) highlight the potential problems that may arise by not taking into account purpose and semantics, (b) make clear how the difference in the purpose is reflected in totally different semantics and (c) provide an algorithm to translate from one semantic into another as a preliminary step towards the integration of ontologies designed for different purposes. This will allow reusing the ontologies even in contexts different from those in which they were designed.

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Abstract

This paper argues for making a paradigm shift in publishing and using biographical dictionaries on the web, based on Linked Data. The idea is to provide the user with enhanced reading experience of biographies by enriching contents with data linking and reasoning. In addition, versatile tooling for 1) biographical research of individual persons as well as for 2) prosopographical research on groups of people are provided. To demonstrate and evaluate the new possibilities,we present the semantic portal "BiographySampo - Finnish Biographies on theSemantic Web". The system is based on a knowledge graph extracted automatically from a collection of 13.100 textual biographies, enriched with data linking to 16 external data sources, and by harvesting external collection data from libraries, museums, and archives. The portal was released in September 2018 for free public use at: http://biografiasampo.fi.

Type

a