726 research outputs found
Will this work for Susan? Challenges for delivering usable and useful generic linked data browsers
While we witness an explosion of exploration tools for simple datasets on Web 2.0 designed for use by ordinary citizens, the goal of a usable interface for supporting navigation and sense-making over arbitrary linked data has remained elusive. The purpose of this paper is to analyse why - what makes exploring linked data so hard? Through a user-centered use case scenario, we work through requirements for sense making with data to extract functional requirements and to compare these against our tools to see what challenges emerge to deliver a useful, usable knowledge building experience with linked data. We present presentation layer and heterogeneous data integration challenges and offer practical considerations for moving forward to effective linked data sensemaking tools
LODE: Linking Digital Humanities Content to the Web of Data
Numerous digital humanities projects maintain their data collections in the
form of text, images, and metadata. While data may be stored in many formats,
from plain text to XML to relational databases, the use of the resource
description framework (RDF) as a standardized representation has gained
considerable traction during the last five years. Almost every digital
humanities meeting has at least one session concerned with the topic of digital
humanities, RDF, and linked data. While most existing work in linked data has
focused on improving algorithms for entity matching, the aim of the
LinkedHumanities project is to build digital humanities tools that work "out of
the box," enabling their use by humanities scholars, computer scientists,
librarians, and information scientists alike. With this paper, we report on the
Linked Open Data Enhancer (LODE) framework developed as part of the
LinkedHumanities project. With LODE we support non-technical users to enrich a
local RDF repository with high-quality data from the Linked Open Data cloud.
LODE links and enhances the local RDF repository without compromising the
quality of the data. In particular, LODE supports the user in the enhancement
and linking process by providing intuitive user-interfaces and by suggesting
high-quality linking candidates using tailored matching algorithms. We hope
that the LODE framework will be useful to digital humanities scholars
complementing other digital humanities tools
Ontology Alignment at the Instance and Schema Level
We present PARIS, an approach for the automatic alignment of ontologies.
PARIS aligns not only instances, but also relations and classes. Alignments at
the instance-level cross-fertilize with alignments at the schema-level.
Thereby, our system provides a truly holistic solution to the problem of
ontology alignment. The heart of the approach is probabilistic. This allows
PARIS to run without any parameter tuning. We demonstrate the efficiency of the
algorithm and its precision through extensive experiments. In particular, we
obtain a precision of around 90% in experiments with two of the world's largest
ontologies.Comment: Technical Report at INRIA RT-040
Improving Care using Network-Based Modeling and Intelligent Data Mining of Social Media
Cleverly extracting information from social media has recently attracted nice interest from the medication and Health science community to at an identical time improve health care outcomes and deflate prices victimization consumer-generated opinion. We've got an inclination to tend to propose a social dancing analysis framework that focuses on positive and negative sentiment, in addition as a result of the aspect effects of treatment, in usersâ forum posts, and identi?es user communities (modules) and in?uential users for the aim of ascertaining user opinion of cancer treatment. We get a preference to tend to use a self-organizing map to investigate word frequency information derived from usersâ forum posts. we've got an inclination to tend to then introduced a unique network-based approach for modeling usersâ forum interactions and utilized a network partitioning technique supported optimizing a stability quality live. This allowed North American nation to work out shopper opinion and establish in?uential users at intervals the retrieved modules victimization data derived from each word-frequency information and network-based properties. Our approach will expand analysis into showing intelligence mining social media information for shopper opinion of assorted treatments to supply fast, up-to-date data for the pharmaceutical trade, hospitals, and medical employees, on the effectiveness (or ineffectiveness) of future treatments
Ontology Alignment Architecture for Semantic Sensor Web Integration
Abstract: Sensor networks are a concept that has become very popular in data acquisition and processing for multiple applications in different fields such as industrial, medicine, home automation, environmental detection, etc. Today, with the proliferation of small communication devices with sensors that collect environmental data, semantic Web technologies are becoming closely related with sensor networks. The linking of elements from Semantic Web technologies with sensor networks has been called Semantic Sensor Web and has among its main features the use of ontologies. One of the key challenges of using ontologies in sensor networks is to provide mechanisms to integrate and exchange knowledge from heterogeneous sources (that is, dealing with semantic heterogeneity). Ontology alignment is the process of bringing ontologies into mutual agreement by the automatic discovery of mappings between related concepts. This paper presents a system for ontology alignment in the Semantic Sensor Web which uses fuzzy logic techniques to combine similarity measures between entities of different ontologies. The proposed approach focuses on two key elements: the terminological similarity, which takes into account the linguistic and semantic information of the context of the entity's names, and the structural similarity, based on both the internal and relational structure of the concepts. This work has been validated using sensor network ontologies and the Ontology Alignment Evaluation Initiative (OAEI) tests. The results show that the proposed techniques outperform previous approaches in terms of precision and recall
What are Links in Linked Open Data? A Characterization and Evaluation of Links between Knowledge Graphs on the Web
Linked Open Data promises to provide guiding principles to publish interlinked knowledge graphs on the Web in the form of findable, accessible, interoperable and reusable datasets. We argue that while as such, Linked Data may be viewed as a basis for instantiating the FAIR principles, there are still a number of open issues that cause significant data quality issues even when knowledge graphs are published as Linked Data. Firstly, in order to define boundaries of single coherent knowledge graphs within Linked Data, a principled notion of what a dataset is, or, respectively, what links within and between datasets are, has been missing. Secondly, we argue that in order to enable FAIR knowledge graphs, Linked Data misses standardised findability and accessability mechanism, via a single entry link. In order to address the first issue, we (i) propose a rigorous definition of a naming authority for a Linked Data dataset (ii) define different link types for data in Linked datasets, (iii) provide an empirical analysis of linkage among the datasets of the Linked Open Data cloud, and (iv) analyse the dereferenceability of those links. We base our analyses and link computations on a scalable mechanism implemented on top of the HDT format, which allows us to analyse quantity and quality of different link types at scale.Series: Working Papers on Information Systems, Information Business and Operation
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