Extracting core knowledge from Linked Data

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.

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.

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.

2013

The Web of Data is a rich common resource with billions of triples available in thousands of datasets and individual Web documents created by both expert and non-expert ontologists. A common problem is the imprecision in the use of vocabularies: annotators can misunderstand the semantics of a class or property or may not be able to find the right objects to annotate with. This decreases the quality of data and may eventually hamper its usability over large scale. This paper describes Statistical Knowledge Patterns (SKP) as a means to address this issue. SKPs encapsulate key information about ontology classes, including synonymous properties in (and across) datasets, and are automatically generated based on statistical data analysis. SKPs can be effectively used to automatically normalise data, and hence increase recall in querying. Both pattern extraction and pattern usage are completely automated. The main benefits of SKPs are that: (1) their structure allows for both accurate query expansion and restriction; (2) they are context dependent, hence they describe the usage and meaning of properties in the context of a particular class; and (3) they can be generated offline, hence the equivalence among relations can be used efficiently at run time.

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.

Proceedings of the 17th International Conference on Enterprise Information Systems, 2015

The huge volume of datasets available on the Web has motivated the development of a new class of Web applications, which allow users to perform complex queries on top of a set of predefined linked datasets. However, given the large number of available datasets and the lack of information about their quality, the selection of datasets for a particular application may become a very complex and time consuming task. In this work, we argue that one possible way of helping the selection of datasets for a given application consists of evaluating the completeness of the dataset with respect to the data considered as important by the application users. With this in mind, we propose an approach to assess the completeness of a linked dataset, which considers a set of specific data requirements and allows saving large amounts of query processing. To provide a more detailed evaluation, we propose three distinct types of completeness: schema, literal and instance completeness. We present the definitions underlying our approach and some results obtained with the accomplished evaluation.

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.

Internet Computing, IEEE, 2009

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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.

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.