Improving access to LOD through Knowledge Patterns
Valentina Presutti
Sign up for access to the world's latest research
checkGet notified about relevant papers
checkSave papers to use in your research
checkJoin the discussion with peers
checkTrack your impact
Abstract
ABSTRACT The cloud of Linked Open Data (LOD) appears, in recent research, to be an ideal basis for improving user experience when interacting with Web content across different applications and domains. Using LOD datasets, however, is not straightforward. They often introduce noisy results and do not follow a unified way of organizing their knowledge, and thus, it is unknown how to query them.
Related papers
Extracting core knowledge from linked data
Recent research has shown the Linked Data cloud to be a potentially ideal basis for improving user experience when interacting with Web content across different applications and domains. Using the explicit knowledge of datasets, however, is neither sufficient nor straightforward. Dataset knowledge is often not uniformly organized, thus it is generally unknown how to query for it. To deal with these issues, we propose a dataset analysis approach based on knowledge patterns, and show how the recognition of patterns can support querying datasets even if their vocabularies are previously unknown. Finally, we discuss results from experimenting on three multimedia-related datasets.
Harvesting Pertinent Resources from Linked Open Data
Journal of Digital Information Management, 2010
Linked Open Data (LOD) is becoming an essential part of the Semantic Web. Although LOD has amassed large quantities of structured data from diverse, openly available data sources, there is still a lack of user-friendly interfaces and mechanisms for exploring this huge resource. In this paper, we describe a methodology for harvesting relevant information from the gigantic LOD cloud. The methodology is based on combination of information: identification, extraction, integration and presentation. Relevant information is identified by using a set of heuristics. The identified information resource is extracted by employing an intelligent URI discovery technique. The extracted information is further integrated with the help of a Concept Aggregation Framework. Then the information is presented to end users in logical informational aspects. Thereby, the proposed system is capable of hiding complex underlying semantic mechanics from end users and reducing the users' cognitive load in locating relevant information. In this paper, we describe the methodology and its implementation in the CAF-SIAL system, and compare it with the state of the art.
Aemoo: Linked Data exploration based on Knowledge Patterns
Semantic Web, 2016
This paper presents a novel approach to Linked Data exploration that uses Encyclopedic Knowledge Patterns (EKPs) as relevance criteria for selecting, organising, and visualising knowledge. EKP are discovered by mining the linking structure of Wikipedia and evaluated by means of a user-based study, which shows that they are cognitively sound as models for building entity summarisations. We implemented a tool named Aemoo that supports EKP-driven knowledge exploration and integrates data coming from heterogeneous resources, namely static and dynamic knowledge as well as text and Linked Data. Aemoo is evaluated by means of controlled, task-driven user experiments in order to assess its usability, and ability to provide relevant and serendipitous information as compared to two existing tools: Google and RelFinder.
CAF-SIAL: Concept Aggregation Framework for Structuring Informational Aspects of Linked Open Data
… , 2009. NDT'09. First …, 2009
A Drag-and-block Approach for Linked Open Data Exploration
Since its initial definition in 2007, the concept of Linked Open Data (LOD) has gained strong traction in the scientific community. However, mainstream adoption has been limited and the emergence of an envisioned global linked data space is still in its early stages. One possible explanation is the gap between the large amounts of published LOD datasets and the lack of end-user tools to effectively explore them. Because existing applications are tailored towards specific datasets and do not allow for reuse and extension, novice users have so far had limited means to access the rich data sources being published. To address this issue, we introduce a novel approach to support non-expert users in the flexible exploration of LOD. To this end, we define a formal model that makes use of existing links between interconnected datasets. We implement the model in a mashup platform and illustrate its potential by means of use cases combining Open Data and Linked Open Data sources.
Linked Data: Evolving the Web into a Global Data Space
Synthesis Lectures on the Semantic Web: Theory and Technology, 2011
This book gives an overview of the principles of Linked Data as well as the Web of Data that has emerged through the application of these principles. The book discusses patterns for publishing Linked Data, describes deployed Linked Data applications and examines their architecture.
Linked Open Graph: browsing multiple SPARQL entry points to build your own LOD views
A number of accessible RDF stores are populating the linked open data world. The navigation on data reticular relationships is becoming every day more relevant. Several knowledge base present relevant links to common vocabularies while many others are going to be discovered increasing the reasoning capabilities of our knowledge base applications. In this paper, the Linked Open Graph, LOG, is presented. It is a web tool for collaborative browsing and navigation on multiple SPARQL entry points. The paper presented an overview of major problems to be addressed, a comparison with the state of the arts tools, and some details about the LOG graph computation to cope with high complexity of large Linked Open Dada graphs. The LOG.disit.org tool is also presented by means of a set of examples involving multiple RDF stores and putting in evidence the new provided features and advantages using dbPedia, Getty, Europeana, Geonames, etc. The LOG tool is free to be used, and it has been adopted, developed and/or improved in multiple projects: such as ECLAP for social media cultural heritage, Sii-Mobility for smart city, and ICARO for cloud ontology analysis, OSIM for competence / knowledge mining and analysis.
Semi-Automatic Knowledge Extraction to Enrich Open Linked Data
Cases on Open-Linked Data and Semantic Web Applications
In this chapter we present the analysis of the Wikipedia collection by means of the ELiDa framework with the aim of enriching linked data. ELiDa is based on association rule mining, an exploratory technique to discover relevant correlations hidden in the analyzed data. To compactly store the large volume of extracted knowledge and efficiently retrieve it for further analysis, a persistent structure has been exploited. The domain expert is in charge of selecting the relevant knowledge by setting filtering parameters, assessing the quality of the extracted knowledge, and enriching the knowledge with the semantic expressiveness which cannot be automatically inferred. We consider, as representative document collections, seven datasets extracted from the Wikipedia collection. Each dataset has been analyzed from two point of views (i.e., transactions by documents, transactions by sentences) to highlight relevant knowledge at different levels of abstraction.
Interlinking Open Data on the Web
A fundamental prerequisite of the Semantic Web is the existence of large amounts of meaningfully interlinked RDF data on the Web. The W3C SWEO community project Linking Open Data has made various open datasets available on the Web as RDF, and developed automated mechanisms to interlink them with RDF statements. Collectively, the datasets currently consist of over one billion triples. We believe that large scale interlinking will demonstrate the value of the Semantic Web compared to more centralized approaches such as Google Base 5. This paper outlines the work to date and describes the accompanying demonstration. A functioning Semantic Web is predicated on the availability of large amounts of data as RDF; not in isolated islands but as a Web of interlinked datasets. To date this prerequisite has not been widely met, leading to criticism of the broader endeavour and hindering the progress of developers wishing to build Semantic Web applications. Thanks to the Open Data movement, a va...
A conceptual framework for linked data exploration
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2012
An increasing number of open data sets is becoming available on the Web as Linked Data (LD), many efforts has been devoted to show the potential of LD applications from the technical point of view. However, less attention has been paid to the analysis of the information seeking requirements from the user point of view. In this paper we examine the Information Seeking Process and we propose a general framework that address all its requirements in the context of LD-based applications. We support seamless integration of both Linked and non-Linked data sources and we allow designers to define complex, rank-aware result construction and exploration rules based on rank aggregation and multiple many-to-many data navigation.
Related papers
Ontodia.org -a simple cognitive service to fill the gap in linked open data management tools
It has been already stated many times [1], [2], [3] that in the world of Semantic Web there is still a great need for practical tools that are accessible to common people with no special knowledge required. In fact, this lack of appropriate tools is frequently mentioned in publications and at open discussions [4]. As for the management, visualization and transformation of linked open data (LOD) the selection of tools is even more scarce. Practically, all the available options posses either one or several of these characteristics: { Bulky and complex (difficult to deploy, learn and maintain). { Developer-oriented (only a developer can launch and use). { Costly (most products’ prices start from $1500). { Neither intuitive nor visual. Cognitive features and visual-driven user interaction are almost unheard of among LOD users and in that sense they are greatly deprived of advanced software user interfaces (UI) when compared to traditional database data users.
Sindice.com: Weaving the Open Linked Data
Lecture Notes in Computer Science, 2007
Developers of Semantic Web applications face a challenge with respect to the decentralised publication model: where to find statements about encountered resources. The "linked data" approach, which mandates that resource URIs should be de-referenced and yield metadata about the resource, helps but is only a partial solution. We present Sindice, a lookup index over resources crawled on the Semantic Web. Our index allows applications to automatically retrieve sources with information about a given resource. In addition we allow resource retrieval through inversefunctional properties, offer full-text search and index SPARQL endpoints.
Exploiting linked data to build web applications
Internet Computing, IEEE, 2009
E d it o r : M u n i n d a r P. S i ng h • s i ng h@ nc su.e du S h e ng r u Tu • s he ngr u@c s .uno .e du
Applications, Methodologies, and Technologies for Linked Open Data
International Journal on Semantic Web and Information Systems, 2020
Advances in semantic web technologies have rocketed the volume of linked data published on the web. In this regard, linked open data (LOD) has long been a topic of great interest in a wide range of fields (e.g. open government, business, culture, education, etc.). This article reports the results of a systematic literature review on LOD. 250 articles were reviewed for providing a general overview of the current applications, technologies, and methodologies for LOD. The main findings include: i) most of the studies conducted so far focus on the use of semantic web technologies and tools applied to contexts such as biology, social sciences, libraries, research, and education; ii) there is a lack of research with regard to a standardized methodology for managing LOD; and iii) a plenty of tools can be used for managing LOD, but most of them lack of user-friendly interfaces for querying datasets.
Leveraging linked open data information extraction for data mining applications
TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES
The linked open data cloud, with a huge volume of data from heterogeneous and interlinked datasets, has turned the Web into a large data store. It has attracted the attention of both developers and researchers in the last few years, opening up new dimensions in machine learning and knowledge discovery. Information extraction procedures for these processes use different approaches, e.g., template-based, federated to multiple sources, fixed depth link traversal, etc. They are limited by problems in online access to datasets' SPARQL endpoints, such as servers being down for maintenance, bandwidth narrowing, limited numbers of access points to datasets in particular time slots, etc., which may result in imprecise and incomplete sets of feature vector generation, affecting the quality of knowledge discovered. The work presented here addresses the disadvantages of online data retrieval by proposing a simple and automatic way to extract features from the linked open data cloud using a linked traversal approach in a local environment with previously identified and known sets of interlinked RDF datasets. The user is given the flexibility to determine the depth of the neighboring properties to be traversed for information extraction to generate the feature vector, which can be used for machine learning and knowledge discovery. The experiment is performed locally with Virtuoso Triple Store for storage of datasets and an interface developed to build the feature vector. The evaluation is performed by comparing the obtained feature vector with gold standard instances annotated manually and with a case study for estimating the effects of demography in movie production for a country. The advantage of the proposed approach lies in overcoming problems with online access of data from the linked data cloud, RDF dataset integration in both local and web environments to build feature vectors for machine learning, and generating background knowledge from the linked data cloud.
Support for Reusable Explorations of Linked Data in the Semantic Web
Lecture Notes in Computer Science, 2012
Absract. The Linked Data cloud growth is changing current Web application development. One of the first steps is to determine whether there is information already available that can be immediately reused.
RKBExplorer.com: A Knowledge Driven Infrastructure for Linked Data Providers
Lecture Notes in Computer Science, 2008
RKB Explorer is a Semantic Web application that is able to present unified views of a significant number of heterogeneous data sources. We have developed an underlying information infrastructure which is mediated by ontologies and consists of many independent triplestores, each publicly available through both SPARQL endpoints and resolvable URIs. To realise this synergy of disparate information sources, we have deployed tools to identify co-referent URIs, and devised an architecture to allow the information to be represented and used. This paper provides a brief overview of the system including the underlying infrastructure, and a number of associated tools for both knowledge acquisition and publishing.
Integrating Know-How into the Linked Data Cloud
This paper presents the first framework for integrating procedural knowledge, or “know-how”, into the Linked Data Cloud. Know-how available on the Web, such as step-by-step instructions, is largely unstructured and isolated from other sources of online knowledge. To overcome these limitations, we propose extending to procedural knowledge the benefits that Linked Data has already brought to representing, retrieving and reusing declarative knowledge. We describe a framework for representing generic know-how as Linked Data and for automatically acquiring this representation from existing resources on the Web. This system also allows the automatic generation of links between different know-how resources, and between those resources and other online knowledge bases, such as DBpedia. We discuss the results of applying this framework to a real-world scenario and we show how it outperforms existing manual community-driven integration efforts.
LOD Explorer: Presenting the Web of Data
—The quantity of data published on the Web according to principles of Linked Data is increasing intensely. However, this data is still largely limited to be used up by domain professionals and users who understand Linked Data technologies. Therefore, it is essential to develop tools to enhance intuitive perceptions of Linked Data for lay users. The features of Linked Data point to various challenges for an easy-to-use data presentation. In this paper, Semantic Web and Linked Data technologies are overviewed, challenges to the presentation of Linked Data is stated, and LOD Explorer is presented with the aim of delivering a simple application to discover triplestore resources. Furthermore, to hide the technical challenges behind Linked Data and provide both specialist and non-specialist users, an interactive and effective way to explore RDF resources.
Knowledge Spring Process - Towards Discovering and Reusing Knowledge within Linked Open Data Foundations
Proceedings of 3rd International Conference on Data Management Technologies and Applications, 2014
Data is everywhere, and non-expert users must be able to exploit it in order to extract knowledge, get insights and make well-informed decisions. The value of the discovered knowledge could be of greater value if it is available for later consumption and reusing. In this paper, we present the first version of the Knowledge Spring Process, an infrastructure that allows non-expert users to (i) apply user-friendly data mining techniques on open data sources, and (ii) share results as Linked Open Data (LOD). The main contribution of this paper is the concept of reusing the knowledge gained from data mining processes after being semantically annotated as LOD, then obtaining Linked Open Knowledge. Our Knowledge Spring Process is based on a model-driven viewpoint in order to easier deal with the wide diversity of open data formats.
This document is currently being converted. Please check back in a few minutes.