Human Intelligence in the Process of Semantic Content Creation

Abstract

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 34 World Wide Web (2010) 13:33-59 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.

User interfaces for semantic authoring of textual content: A systematic literature review

“…Semantic Gap. is a term coined to describe the discrepancy between low-level technical features of multimedia, which can be automatically processed to a great extent, and the high-level, meaning-bearing features a user is typically interested in [62]. As discussed in [13], semantic gaps in the process of constructing and managing digital content can be divided into three types namely human-to-machine, machine-to-machine, and machineto-human.…”

“…is a very general research field triggered by the advent of Web 2.0. It aims at bringing a social novelty, rather than a technical one by providing user-friendly tools to facilitate broad user participation in the process of creating semantic content [62]. The Social Semantic Web vision comprises many of the aforementioned domains and techniques.…”

“…These approaches which are also called Ontology Population [62] techniques aim to create semantic content based on a set of initial ontologies which are extended during the population process. When compared to the bottom-up approaches, these approaches deal with semantic representations from the beginning instead of lifting unstructured content to a semantic level.…”

“…Examples are Semantic Wikis and Semantic Tagging Systems (e.g. Faviki 25 ) which exploit Web 2.0 principles and technologies to facilitate broad user participation and collaboration in the process of creating semantically enriched or annotated content [62,29]. [15] divides semantic wikis into two main categories according to their connections with the ontologies: wikis for ontologies and ontologies for wikis.…”

“…Most of them employ natural language processing and information extraction techniques. These approaches differ in architecture, information extraction tools and methods, initial ontology, amount of manual work required to perform annotation, as well as performance [62,3].…”