21,164 research outputs found
Dealing with uncertain entities in ontology alignment using rough sets
This is the author's accepted manuscript. The final published article is available from the link below. Copyright @ 2012 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.Ontology alignment facilitates exchange of knowledge among heterogeneous data sources. Many approaches to ontology alignment use multiple similarity measures to map entities between ontologies. However, it remains a key challenge in dealing with uncertain entities for which the employed ontology alignment measures produce conflicting results on similarity of the mapped entities. This paper presents OARS, a rough-set based approach to ontology alignment which achieves a high degree of accuracy in situations where uncertainty arises because of the conflicting results generated by different similarity measures. OARS employs a combinational approach and considers both lexical and structural similarity measures. OARS is extensively evaluated with the benchmark ontologies of the ontology alignment evaluation initiative (OAEI) 2010, and performs best in the aspect of recall in comparison with a number of alignment systems while generating a comparable performance in precision
SiGMa: Simple Greedy Matching for Aligning Large Knowledge Bases
The Internet has enabled the creation of a growing number of large-scale
knowledge bases in a variety of domains containing complementary information.
Tools for automatically aligning these knowledge bases would make it possible
to unify many sources of structured knowledge and answer complex queries.
However, the efficient alignment of large-scale knowledge bases still poses a
considerable challenge. Here, we present Simple Greedy Matching (SiGMa), a
simple algorithm for aligning knowledge bases with millions of entities and
facts. SiGMa is an iterative propagation algorithm which leverages both the
structural information from the relationship graph as well as flexible
similarity measures between entity properties in a greedy local search, thus
making it scalable. Despite its greedy nature, our experiments indicate that
SiGMa can efficiently match some of the world's largest knowledge bases with
high precision. We provide additional experiments on benchmark datasets which
demonstrate that SiGMa can outperform state-of-the-art approaches both in
accuracy and efficiency.Comment: 10 pages + 2 pages appendix; 5 figures -- initial preprin
Common vocabularies for collective intelligence - work in progress
Web based applications and tools offer a great potential to increase the efficiency of information flow and communication among different agents during emergencies. Among the different factors, technical and non technical, that hinder the integration of an information model in emergency management sector, is a lack of a common, shared vocabulary. This paper furthers previous work in the area of ontology development, and presents a summary and overview of the goal, process and methodology to construct a shared set of metadata that can be used to map existing vocabulary. This paper is a work in progress report
Demo: A community based approach for managing ontology alignments
The Semantic Web is rapidly becoming a defacto distributed repository for semantically represented data, thus leveraging on the added on value of the network effect. Various ontology mapping techniques and tools have been devised to facilitate the bridging and integration of distributed data repositories. Nevertheless, ontology mapping can benefit from human supervision to increase accuracy of results. The spread of Web 2.0 approaches demonstrate the possibility of using collaborative techniques for reaching consensus. While a number of prototypes for collaborative ontology construction are being developed, collaborative ontology mapping is not yet well investigated. In this paper, we describe aprototype that combines off-the-shelf ontology mapping tools with social software techniques to enable users to collaborate on mapping ontologies. Emphasis is put on the reuse of user generated mappings to improve the accuracy of automatically generated ones
Clinical guidelines as plans: An ontological theory
Clinical guidelines are special types of plans realized by collective agents. We provide an ontological theory of such plans that is designed to support the construction of a framework in which guideline-based information systems can be employed in the management of workflow in health care organizations.
The framework we propose allows us to represent in formal terms how clinical guidelines are realized through the actions of are realized through the actions of individuals organized into teams. We provide various levels of implementation representing different levels of conformity on the part of health care organizations.
Implementations built in conformity with our framework are marked by two dimensions of flexibility that are designed to make them more likely to be accepted by health care professionals than standard guideline-based management systems. They do justice to the fact 1) that responsibilities within a health care organization are widely shared, and 2) that health care professionals may on different occasions be non-compliant with guidelines for a variety of well justified reasons.
The advantage of the framework lies in its built-in flexibility, its sensitivity to clinical context, and its ability to use inference tools based on a robust ontology. One disadvantage lies in its complicated implementation
Crowd-Sourcing Fuzzy and Faceted Classification for Concept Search
Searching for concepts in science and technology is often a difficult task.
To facilitate concept search, different types of human-generated metadata have
been created to define the content of scientific and technical disclosures.
Classification schemes such as the International Patent Classification (IPC)
and MEDLINE's MeSH are structured and controlled, but require trained experts
and central management to restrict ambiguity (Mork, 2013). While unstructured
tags of folksonomies can be processed to produce a degree of structure
(Kalendar, 2010; Karampinas, 2012; Sarasua, 2012; Bragg, 2013) the freedom
enjoyed by the crowd typically results in less precision (Stock 2007).
Existing classification schemes suffer from inflexibility and ambiguity.
Since humans understand language, inference, implication, abstraction and hence
concepts better than computers, we propose to harness the collective wisdom of
the crowd. To do so, we propose a novel classification scheme that is
sufficiently intuitive for the crowd to use, yet powerful enough to facilitate
search by analogy, and flexible enough to deal with ambiguity. The system will
enhance existing classification information. Linking up with the semantic web
and computer intelligence, a Citizen Science effort (Good, 2013) would support
innovation by improving the quality of granted patents, reducing duplicitous
research, and stimulating problem-oriented solution design.
A prototype of our design is in preparation. A crowd-sourced fuzzy and
faceted classification scheme will allow for better concept search and improved
access to prior art in science and technology
Beyond âInteractionâ: How to Understand Social Effects on Social Cognition
In recent years, a number of philosophers and cognitive scientists have advocated for an âinteractive turnâ in the methodology of social-cognition research: to become more ecologically valid, we must design experiments that are interactive, rather than merely observational. While the practical aim of improving ecological validity in the study of social cognition is laudable, we think that the notion of âinteractionâ is not suitable for this task: as it is currently deployed in the social cognition literature, this notion leads to serious conceptual and methodological confusion. In this paper, we tackle this confusion on three fronts: 1) we revise the âinteractionistâ definition of interaction; 2) we demonstrate a number of potential methodological confounds that arise in interactive experimental designs; and 3) we show that ersatz interactivity works just as well as the real thing. We conclude that the notion of âinteractionâ, as it is currently being deployed in this literature, obscures an accurate understanding of human social cognition
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