Towards an Interaction-based Integration of MKM Services into End-User Applications

The Semantic Alliance (SAlly) Framework, first presented at MKM 2012, allows integration of Mathematical Knowledge Management services into typical applications and end-user workflows. From an architecture allowing invasion of spreadsheet programs, it grew into a middle-ware connecting spreadsheet, CAD, text and image processing environments with MKM services. The architecture presented in the original paper proved to be quite resilient as it is still used today with only minor changes. This paper explores extensibility challenges we have encountered in the process of developing new services and maintaining the plugins invading end-user applications. After an analysis of the underlying problems, I present an augmented version of the SAlly architecture that addresses these issues and opens new opportunities for document type agnostic MKM services.Comment: 14 pages, 7 figure

Using Games to Create Language Resources: Successes and Limitations of the Approach

Abstract One of the more novel approaches to collaboratively creating language resources in recent years is to use online games to collect and validate data. The most significant challenges collaborative systems face are how to train users with the necessary expertise and how to encourage participation on a scale required to produce high quality data comparable with data produced by “traditional ” experts. In this chapter we provide a brief overview of collaborative creation and the different approaches that have been used to create language resources, before analysing games used for this purpose. We discuss some key issues in using a gaming approach, including task design, player motivation and data quality, and compare the costs of each approach in terms of development, distribution and ongoing administration. In conclusion, we summarise the benefits and limitations of using a gaming approach to resource creation and suggest key considerations for evaluating its utility in different research scenarios

Revision 1 Size and position of the healthy meniscus, and its Correlation with sex, height, weight, and bone area- a cross-sectional study

<p>Abstract</p> <p>Background</p> <p>Meniscus extrusion or hypertrophy may occur in knee osteoarthritis (OA). However, currently no data are available on the position and size of the meniscus in asymptomatic men and women with normal meniscus integrity.</p> <p>Methods</p> <p>Three-dimensional coronal DESSwe MRIs were used to segment and quantitatively measure the size and position of the medial and lateral menisci, and their correlation with sex, height, weight, and tibial plateau area. 102 knees (40 male and 62 female) were drawn from the Osteoarthritis Initiative "non-exposed" reference cohort, including subjects without symptoms, radiographic signs, or risk factors for knee OA. Knees with MRI signs of meniscus lesions were excluded.</p> <p>Results</p> <p>The tibial plateau area was significantly larger (p < 0.001) in male knees than in female ones (+23% medially; +28% laterally), as was total meniscus surface area (p < 0.001, +20% medially; +26% laterally). Ipsi-compartimental tibial plateau area was more strongly correlated with total meniscus surface area in men (r = .72 medially; r = .62 laterally) and women (r = .67; r = .75) than contra-compartimental or total tibial plateau area, body height or weight. The ratio of meniscus versus tibial plateau area was similar between men and women (p = 0.22 medially; p = 0.72 laterally). Tibial coverage by the meniscus was similar between men and women (50% medially; 58% laterally), but "physiological" medial meniscal extrusion was greater in women (1.83 ± 1.06mm) than in men (1.24mm ± 1.18mm; p = 0.011).</p> <p>Conclusions</p> <p>These data suggest that meniscus surface area strongly scales with (ipsilateral) tibial plateau area across both sexes, and that tibial coverage by the meniscus is similar between men and women.</p

Human intelligence in the process of semantic content creation

Despite significant progress over the last years the large-scale adoption of semantic technologies is still to come. One of the reasons for this state of affairs is assumed to be the lack of useful semantic content, a prerequisite for almost every IT system or application using semantics. Through its very nature, this content can not be created fully automatically, but requires, to a certain degree, human contribution. The interest of Internet users in semantics, and in particular in creating semantic content, is, however, low. This is understandable if we think of several characteristics exposed by many of the most prominent semantic technologies, and the applications thereof. One of these characteristics is the high barrier of entry imposed. Interacting with semantic technologies today requires specific skills and expertise on subjects which are not part of the mainstream IT knowledge portfolio. A second characteristic are the incentives that are largely missing in the design of most semantic applications. The benefits of using machine-understandable content are in most applications fully decoupled from the effort of creating and maintaining this content. In other words, users do not have a motivation to contribute to the process. Initiatives in the areas of the Social Semantic Web acknowledged this problem, and identified mechanisms to motivate users to dedicate more of their time and resources to participate in the semantic content creation process. Still, even if incentives are theoretically in place, available human labor is limited and must only be used for those tasks that are heavily dependent on human intervention, and cannot be reliably automated. In this article, we concentrate on this step in between. As a first contribution, we analyze the process of semantic content creation in order to identify those tasks that are inherently human-driven. When building semantic applications involving these specific tasks, one has to install incentive schemes that are likely to encourage users to perform exactly these tasks that crucially rely on manual input. As a second contribution of the article, we propose incentives or incentive-driven tools that can be used to increase user interest in semantic content creation tasks. We hope that our findings will be adopted as recommendations for establishing a fundamentally new form of design of semantic applications by the semantic technologies community

Human intelligence in the process of semantic content creation

Despite significant progress over the last years the large-scale adoption of semantic technologies is still to come. One of the reasons for this state of affairs is assumed to be the lack of useful semantic content, a prerequisite for almost every IT system or application using semantics. Through its very nature, this content can not be created fully automatically, but requires, to a certain degree, human contribution. The interest of Internet users in semantics, and in particular in creating semantic content, is, however, low. This is understandable if we think of several characteristics exposed by many of the most prominent semantic technologies, and the applications thereof. One of these characteristics is the high barrier of entry imposed. Interacting with semantic technologies today requires specific skills and expertise on subjects which are not part of the mainstream IT knowledge portfolio. A second characteristic are the incentives that are largely missing in the design of most semantic applications. The benefits of using machine-understandable content are in most applications fully decoupled from the effort of creating and maintaining this content. In other words, users do not have a motivation to contribute to the process. Initiatives in the areas of the Social Semantic Web acknowledged this problem, and identified mechanisms to motivate users to dedicate more of their time and resources to participate in the semantic content creation process. Still, even if incentives are theoretically in place, available human labor is limited and must only be used for those tasks that are heavily dependent on human intervention, and cannot be reliably automated. In this article, we concentrate on this step in between. As a first contribution, we analyze the process of semantic content creation in order to identify those tasks that are inherently human-driven. When building semantic applications involving these specific tasks, one has to install incentive schemes that are likely to encourage users to perform exactly these tasks that crucially rely on manual input. As a second contribution of the article, we propose incentives or incentive-driven tools that can be used to increase user interest in semantic content creation tasks. We hope that our findings will be adopted as recommendations for establishing a fundamentally new form of design of semantic applications by the semantic technologies community

SpotTheLink: a game for ontology alignment

The interoperability of data depends on the availability of alignments among different ontologies. Various approaches to match, merge and integrate ontologies and, more recently, to interlink RDF data sets were developed over. Even though the research area has matured, the full automation of the ontology alignment process is not feasible and the human user is indispensable. Such tasks involve mainly bootstrapping the underlying methods and for validating and enhancing their results. The question of acquiring such input still remains to be solved, in particular when it comes to the motivators and incentives that are likely to make people dedicate labor to ontology alignment tasks. In this paper we build on previous work of ours on using casual games to tackle this problem. We present SpotTheLink, the latest release of the OntoGame framework, which allows for the definition of mappings between Semantic Web ontologies as part of a collaborative game experience.We illustrate the idea of SpotTheLink in an instance of the game aiming to align DBpedia and PROTON, and explain the game background mechanics by which players’ inputs are translated into SKOS-based ontology mappings. A summary of findings of SpotTheLink user evaluation and the experiences we gained throughout the entire life span of OntoGame allow us to derive a number of best practices and guidelines for the design of incentives-minded semantic-content-authoring technology, in which human and computational intelligence are smoothly interwoven

SeaFish: a game for collaborative and visual image annotation and interlinking

Many tasks in semantic content creation, from building and aligning vocabularies to annotation or data interlinking, still require human intervention. Even though automatic methods addressing the aforementioned challenges have reached a certain level of maturity, user input is still required at many ends of these processes. The idea of human computation is to rely on the human user for problems that are impossible to solve for computers. However, users need clear incentives in order to dedicate their time and manual labor to tasks. The OntoGame series uses games to hide abstract tasks behind entertaining user interfaces and gaming experiences in order to collect knowledge. SeaFish is a game for collaborative image annotation and interlinking without text. In this latest release of the OntoGame series, players have to select images that are related to a concept that is represented by an image (from DBpedia) from a collection of images (produced by querying flickr TM wrappr with the respective concept). The data collected by SeaFish is published as Linked Data on the Web. In this paper we outline the SeaFish game and demo