1,721,181 research outputs found
Ghosts in the Semantic Web Machine?
No full textin this paper we present the concept of a ghost, a lightweight container that holds pointers to RDF resources (such as RDF vCards or FOAF) which represent the person associated with the ghost. The goal of the ghost approach is to provide a light weight jumping off point into the various bodies of metadata associated with an individual, which in turn can connect to various Semantic Web services in a local context
FlexiTerm: a flexible term recognition method
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125465.pdf (Publisher’s version ) (Open Access)BACKGROUND: The increasing amount of textual information in biomedicine requires effective term recognition methods to identify textual representations of domain-specific concepts as the first step toward automating its semantic interpretation. The dictionary look-up approaches may not always be suitable for dynamic domains such as biomedicine or the newly emerging types of media such as patient blogs, the main obstacles being the use of non-standardised terminology and high degree of term variation. RESULTS: In this paper, we describe FlexiTerm, a method for automatic term recognition from a domain-specific corpus, and evaluate its performance against five manually annotated corpora. FlexiTerm performs term recognition in two steps: linguistic filtering is used to select term candidates followed by calculation of termhood, a frequency-based measure used as evidence to qualify a candidate as a term. In order to improve the quality of termhood calculation, which may be affected by the term variation phenomena, FlexiTerm uses a range of methods to neutralise the main sources of variation in biomedical terms. It manages syntactic variation by processing candidates using a bag-of-words approach. Orthographic and morphological variations are dealt with using stemming in combination with lexical and phonetic similarity measures. The method was evaluated on five biomedical corpora. The highest values for precision (94.56%), recall (71.31%) and F-measure (81.31%) were achieved on a corpus of clinical notes. CONCLUSIONS: FlexiTerm is an open-source software tool for automatic term recognition. It incorporates a simple term variant normalisation method. The method proved to be more robust than the baseline against less formally structured texts, such as those found in patient blogs or medical notes. The software can be downloaded freely at http://www.cs.cf.ac.uk/flexiterm
Automatic extraction of personal experiences from patients' blogs: A case study in chronic obstructive pulmonary disease
No full textPeople with long-term illness such as chronic obstructive pulmonary disease (COPD) often use social media to document and share information, opinions and their experiences with others. Analysing the self-reported experiences of patients shared online has the potential to help medical researchers gain insight into some of the key issues affecting patients. However, the scale of health conversation taking place online poses considerable challenges to traditional content analysis. In this paper, we present a system which automates extraction of patient statements which refer to a personal experience. We applied a crowdsourcing methodology to create a set of 1770 annotated sentences from blog posts written by COPD patients. Our machine learning approach trained on lexical features successfully extracted sentences about patient experience with 93% precision and 80% recall (F-measure: 86. Automatic annotation of sentences about patient experience can facilitate subsequent content analysis by highlighting the most relevant sentences to this particular problem
Integrative Use of Information Extraction, Semantic Matchmaking and Adaptive Coupling Techniques in Support of Distributed Information Processing and Decision-Making
No full textIn order to press maximal cognitive benefit from their social, technological and informational environments, military coalitions need to understand how best to exploit available information assets as well as how best to organize their socially-distributed information processing activities. The International Technology Alliance (ITA) program is beginning to address the challenges associated with enhanced cognition in military coalition environments by integrating a variety of research and development efforts. In particular, research in one component of the ITA ('Project 4: Shared Understanding and Information Exploitation') is seeking to develop capabilities that enable military coalitions to better exploit and distribute networked information assets in the service of collective cognitive outcomes (e.g. improved decision-making). In this paper, we provide an overview of the various research activities in Project 4. We also show how these research activities complement one another in terms of supporting coalition-based collective cognition
Increasing Negotiation Performance at the Edge of the Network
No full textAutomated negotiation has been used in a variety of distributed settings, such as privacy in the Internet of Things (IoT) devices and power distribution in Smart Grids. The most common protocol under which these agents negotiate is the Alternating Offers Protocol (AOP). Under this protocol, agents cannot express any additional information to each other besides a counter offer. This can lead to unnecessarily long negotiations when, for example, negotiations are impossible, risking to waste bandwidth that is a precious resource at the edge of the network. While alternative protocols exist which alleviate this problem, these solutions are too complex for low power devices, such as IoT sensors operating at the edge of the network. To improve this bottleneck, we introduce an extension to AOP called Alternating Constrained Offers Protocol (ACOP), in which agents can also express constraints to each other. This allows agents to both search the possibility space more efficiently and recognise impossible situations sooner. We empirically show that agents using ACOP can significantly reduce the number of messages a negotiation takes, independently of the strategy agents choose. In particular, we show our method significantly reduces the number of messages when an agreement is not possible. Furthermore, when an agreement is possible it reaches this agreement sooner with no negative effect on the utility
CONOISE: Agent-Based Formation of Virtual Organisations
No full textVirtual organisations (VOs) are composed of a number of individuals, de- partments or organisations each of which has a range of capabilities and resources at their disposal. These VOs are formed so that resources may be pooled and services combined with a view to the exploitation of a per- ceived market niche. However, in the modern commercial environment it is essential to respond rapidly to changes in the market to remain com- petitive. Thus, there is a need for robust, exible systems to support the process of VO management. Within the CONOISE (www.conoise.org) project, agent-based models and techniques are being developed for the automated formation and maintenance of virtual organisations. In this paper we focus on a critical element of VO management: how an eective VO may be formed rapidly for a specied purpose
Conversational intelligence analysis
No full textSocial networks foster the development of social sensing to gather data about situations in the environment. Making sense of this information is, however, a challenge because the process is not linear and additional sensed information may be needed to better understand a situation. In this paper we explore how two complementary technologies, Moira and CISpaces, operate in unison to support collaboration among human-agent teams to iteratively gather and analyse information to improve situational awareness. The integrated system is developed for supporting intelligence analysis in a coalition environment. Moira is a conversational interface for information gathering, querying and evidence aggregation that supports cooperative data-driven analytics via Controlled Natural Language. CISpaces supports collaborative sensemaking among analysts via argumentation-based evidential reasoning to guide the identification of plausible hypotheses, including reasoning about provenance to explore credibility. In concert, these components enable teams of analysts to collaborate in constructing structured hypotheses with machine-based systems and external collaborators.</p
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