Supporting a distributed execution of clinical guidelines

Clinical guidelines (GL) play an important role in medical practice: the one of optimizing the quality of patient care on the basis of evidence based medicine. In order to achieve this goal, the interaction between different agents, who cooperate in the execution of the same GL, is a crucial issue. As a matter of fact, in many cases (e.g. in chronic disorders) the GL execution requires that patient treatment is not performed/completed in the hospital, but is continued in different contexts (e.g. at home, or in the general practitioner's ambulatory), under the responsibility of different agents. In this situation, the correct interaction and communication between the agents themselves is critical for the quality of care, and human resources coordination is a key issue to be addressed by the managers of the involved healthcare services. In this paper we describe how GLARE (Guideline Acquisition, Representation, and Execution), a computerized GL management system, has been extended in order to support such a need. In particular, we have provided: (i) an extension to GL actions representation languages, (ii) proper scheduling and (iii) querying services. By means of these enhancements we aimed at guaranteeing (1) treatment continuity and (2) responsibility assignment support in the various steps of a coordinated and distributed patient care process. We illustrate our approach by means of a practical case study.     

Ontology-driven web-based semantic similarity

Estimation of the degree of semantic similarity/distance between concepts is a very common problem in research areas such as natural language processing, knowledge acquisition, information retrieval or data mining. In the past, many similarity measures have been proposed, exploiting explicit knowledge—such as the structure of a taxonomy—or implicit knowledge—such as information distribution. In the former case, taxonomies and/or ontologies are used to introduce additional semantics; in the latter case, frequencies of term appearances in a corpus are considered. Classical measures based on those premises suffer from some problems: in the first case, their excessive dependency of the taxonomical/ontological structure; in the second case, the lack of semantics of a pure statistical analysis of occurrences and/or the ambiguity of estimating concept statistical distribution from term appearances. Measures based on Information Content (IC) of taxonomical concepts combine both approaches. However, they heavily depend on a properly pre-tagged and disambiguated corpus according to the ontological entities in order to compute accurate concept appearance probabilities. This limits the applicability of those measures to other ontologies –like specific domain ontologies- and massive corpus –like the Web-. In this paper, several of the presented issues are analyzed. Modifications of classical similarity measures are also proposed. They are based on a contextualized and scalable version of IC computation in the Web by exploiting taxonomical knowledge. The goal is to avoid the measures’ dependency on the corpus pre-processing to achieve reliable results and minimize language ambiguity. Our proposals are able to outperform classical approaches when using the Web for estimating concept probabilities.     

Combining task execution and background knowledge for the verification of medical guidelines

The use of a medical guideline can be seen as the execution of computational tasks, sequentially or in parallel, in the face of patient data. It has been shown that many of such guidelines can be represented as a ‘network of tasks’, i.e., as a number of steps that have a specific function or goal. To investigate the quality of such guidelines we propose a formalization of criteria for good practice medicine a guideline should comply to. We use this theory in conjunction with medical background knowledge to verify the quality of a guideline dealing with diabetes mellitus type 2 using the interactive theorem prover KIV. Verification using task execution and background knowledge is a novel approach to quality checking of medical guidelines.     

Ontology-driven,unsupervised instance population

The Semantic Web’s need for machine understandable content has led researchers to attempt to automatically acquire such content from a number of sources, including the web. To date, such research has focused on “document-driven” systems that individually process a small set of documents, annotating each with respect to a given ontology. This article introduces OntoSyphon, an alternative that strives to more fully leverage existing ontological content while scaling to extract comparatively shallow content from millions of documents. OntoSyphon operates in an “ontology-driven” manner: taking any ontology as input, OntoSyphon uses the ontology to specify web searches that identify possible semantic instances, relations, and taxonomic information. Redundancy in the web, together with information from the ontology, is then used to automatically verify these candidate instances and relations, enabling OntoSyphon to operate in a fully automated, unsupervised manner. A prototype of OntoSyphon is fully implemented and we present experimental results that demonstrate substantial instance population in three domains based on independently constructed ontologies. We show that using the whole web as a corpus for verification yields the best results, but that using a much smaller web corpus can also yield strong performance. In addition, we consider the problem of selecting the best class for each candidate instance that is discovered, and the problem of ranking the final results. For both problems we introduce new solutions and demonstrate that, for both the small and large corpora, they consistently improve upon previously known techniques.     

Ontology-driven visualization system for semantic searching

Technical manuals are very diverse, ranging from software to commodities, general instructions and technical manuals that deal with specific domains such as mechanical maintenance. Due to the vast amount of documentation, finding the information is a tedious and time consuming task, especially for the mechanics. It is also difficult to grasp relationships among contents in manuals. Many researchers have adopted ontology to solve these problems and semantically represent contents of manuals. However, if ontology becomes very large and complex, it is not easy to work with ontology. Visualization has been an effective way to grasp and manipulate ontology. In this research, we propose a new ontology model to represent and retrieve contents from the manuals. We have also designed a visualization system based on our proposed ontology. In order to model the ontology, we have analyzed aircraft maintenance process, extracted the concepts and defined relationships between concepts. After modeling ontology schema, all instances of ontology are created by instance creator. From here, raw data of maintenance manuals are preprocessed to well-formed format. Next, we create a set of rule mapping well-formed document and ontology schema. For the Component class, instance creator uses a classifier to separate all parts into Component and Primitive part class. If population task is complete, validity of data for created instances will be checked by JENA engine. The inference process will create inferred triples based on the ontology schema, and then the triples are saved into a triple repository. Our system then will use this triples repository to search necessary information and visualize the search results. We use the Prefuse toolkit to visualize the search results. With this, the mechanics can intuitively grasp the relationship between maintenance manuals using the provided information. This will allow the mechanics to easily obtain information for given tasks, reduce their time to search related information and understand the information through visualization.     

Ontology-driven collaborative annotation in shared workspaces

In the last decade, collaboration and sharing on the Web have become mainstream. Digital, remote interaction happens on a daily basis, not only to share digital resources, but also to create, manage and discuss them, in every possible situation where collaboration is required: from work teams to groups of friends, from community committees to no-profit organizations. In this paper we address the task of collaborative management of digital resources within a team, with a special focus on the task of semantic annotation, where team members, possibly supported by automated reasoning, enrich resources with properties that help in organizing, retrieving and creating connections between contents of different types. We focus in particular on the problem of reaching an agreement on the annotation itself among the participants. The paper presents a qualitative user study aimed at observing users behavior when faced with this task. The results of the study are then analyzed in order to draw guidelines, which are then implemented in a tool for collaborative annotation. This study is carried out in the context of the Semantic Table Plus Plus (Sem T++) Project, a framework supporting collaboration over thematic workspaces, whose goal is to enhance cooperation through awareness, enhanced communication and easy sharing of digital content.     

本体驱动的动态虚拟社区知识通信

知识通信是实现语义Web的有效途径之一。该文给出知识通信的定义和特点,构建了基于动态虚拟社区的P2P知识通信架构;为实现高效知识路由,建立了基于本体的任务元知识库、任务实例库、规则库、本体知识库,设计了具有发现、协调和定位服务资源等功能的路由服务Agent,建立了本体驱动、分散控制的快速、准确、高效的知识路由机制。     

本体驱动的半结构化Web生物数据抽取

提出由本体驱动,并根据文档结构和特征匹配来进行信息定位和信息抽取的方法,并实现了一个用户指导的交互式信息抽取原型系统。有效地解决了信息抽取中涉及的同义词,一词多义等语义问题,以及数据项不完整和排序不固定的问题。     

本体驱动的文本虚拟样本构造方法研究

构造虚拟样本能够为机器学习中的训练集融入先验知识,从而改善标注瓶颈问题.提出了一种本体驱动的文本虚拟样本构造方法.在确保类别不变性的前提下,该方法依据领域相关本体所明晰表达的领域知识,基于本体树的点、边、子树,从同义、父子、语义同构的多个词义关系角度实现了文本虚拟样本的构造.初步实验表明,该方法与原分类及类似方法相比具有更好的分类精度和推广能力.     

Ontology-driven discovery of geospatial evidence in web pages

When users need to find something on the Web that is related to a place, chances are place names will be submitted along with some other keywords to a search engine. However, automatic recognition of geographic characteristics embedded in Web documents, which would allow for a better connection between documents and places, remains a difficult task. We propose an ontology-driven approach to facilitate the process of recognizing, extracting, and geocoding partial or complete references to places embedded in text. Our approach combines an extraction ontology with urban gazetteers and geocoding techniques. This ontology, called OnLocus, is used to guide the discovery of geospatial evidence from the contents of Web pages. We show that addresses and positioning expressions, along with fragments such as postal codes or telephone area codes, provide satisfactory support for local search applications, since they are able to determine approximations to the physical location of services and activities named within Web pages. Our experiments show the feasibility of performing automated address extraction and geocoding to identify locations associated to Web pages. Combining location identifiers with basic addresses improved the precision of extractions and reduced the number of false positive results.     

Ontology-driven document enrichment: principles, tools and applications

In this paper, we present an approach to document enrichment, which consists of developing and integrating formal knowledge models with archives of documents, to provide intelligent knowledge retrieval and (possibly) additional knowledge-intensive services, beyond what is currently available using “standard” information retrieval and search facilities. Our approach is ontology-driven, in the sense that the construction of the knowledge model is carried out in a top-down fashion, by populating a given ontology, rather than in a bottom-up fashion, by annotating a particular document. In this paper, we give an overview of the approach and we examine the various types of issues (e.g. modelling, organizational and user interface issues) which need to be tackled to effectively deploy our approach in the workplace. In addition, we also discuss a number of technologies we have developed to support ontology-driven document enrichment and we illustrate our ideas in the domains of electronic news publishing, scholarly discourse and medical guidelines.     

Ontology-driven analysis of UML-based collaborative processes using OWL-DL and CPN

A key ingredient in system and organization modeling is modeling business processes that involve the collaborative participation of different teams within and outside the organization. Recently, the use of the Unified Modeling Language (UML) for collaborative business modeling has been increasing, thanks to its human-friendly visual representation of a rich set of structural and behavioral views, albeit its unclear semantics. In the meantime, the use of the Web Ontology Language (OWL) has also been emerging, thanks to its clearly-defined semantics, hence being amenable to automatic analysis and reasoning, although it is less human friendly than, and also perhaps not as rich as, the UML notation — especially concerning processes, or activities. In this paper, we view the UML and the OWL as being complementary to each other, and exploit their relative strengths. We provide a mapping between the two, through a set of mapping rules, which allow for the capture of UML activity diagrams in an OWL-ontology. This mapping, which results in a formalization of collaborative processes, also sets a basis for subsequent construction of executable models using the Colored Petri Nets (CPN) formalism. For this purpose, we also provide appropriate mappings from OWL-based ontological elements into CPN elements. A case study of a mortgage granting system is described, along with the potential benefits and limitations of our proposal.     

Ontology-driven geographic information integration: A survey of current approaches

Integrating different information sources is a growing research area within different application domains. This is particularly true for the geographic information domain which is facing new challenges because newer and better technologies are capturing large amounts of information about the Earth. This trend can be combined with increases in the distribution of GIS (Geographic Information Systems) on the Web, which is leading to the proliferation of different geospatial information repositories and the subsequent need to integrate information across repositories to get consistent information. To overcome this situation, many proposals use ontologies in the integration process. In this paper we analyze and compare the most widely referred proposals of geographic information integration, focusing on those using ontologies as semantic tools to represent the sources, and to facilitate the integration process.     

本体驱动的知晓内容和上下文的知识路由研究

语义Web提供了实现对Web异构信息源智能访问和处理的平台。然而在语义Web上分布着海量知识,如何在这些海量知识中按照用户的需求快速准确的定位目标知识已成为语义Web迫切需要解决的问题。针对此问题,文章提出了一种知晓内容和上下文的语义Web知识路由机制。该机制采用基于本体和概念格的推理技术,通过捕捉用户需求内容和上下文等语义信息,能够快速准确地实现目标知识与服务的发现、定位、访问以及初步评估。     

基于主体网格的本体驱动自主学习系统

网上学习系统大多只是教材的一种电子化,没有实现用户的个性化教学。该文提出建立基于本体驱动的智能知识导航图。在学习过程中,用户可定制个性化学习内容,主体网格平台可通过Agent动态搜集个性化信息,在参考已建立的知识点本体后,基于用户行为观察的方式创建个性化学习档案,并利用主体网格智能平台AGrIP的多Agent协作提供个性化的、智能自主学习系统,为用户提供更好的知识服务。     

Towards Ontology-driven Knowledge Synthesis for Heterogeneous Information Systems

Information integration enables delivery of the right information to the right user in a timely manner giving manufacturers a competitive edge in today’s global manufacturing market. However, as enterprise information is usually aggregated from a variety of heterogeneous information sources, without using an adequate integration framework it is difficult to extract pertinent information and apply current knowledge to assessing production situations and making informed decisions. This paper investigates a method of facilitating knowledge synthesis in a distributed computing environment. A formal model of domain ontology and knowledge base is presented, which aims at providing a vehicle for representing information and knowledge using a common shared semantics in a given application domain. As a result, a common knowledge representation based architecture is proposed, creating a foundation for establishing a systematic approach for ease of knowledge synthesis in a manufacturing environment     

Ontology-driven hypothesis generation to explain anomalous patient responses to treatment

Within the medical domain there are clear expectations as to how a patient should respond to treatments administered. When these responses are not observed it can be challenging for clinicians to understand the anomalous responses. The work reported here describes a tool which can detect anomalous patient responses to treatment and further suggest hypotheses to explain the anomaly. In order to develop this tool, we have undertaken a study to determine how Intensive Care Unit (ICU) clinicians identify anomalous patient responses; we then asked further clinicians to provide potential explanations for such anomalies. The high level reasoning deployed by the clinicians has been captured and generalised to form the procedural component of the ontology-driven tool. An evaluation has shown that the tool successfully reproduced the clinician’s hypotheses in the majority of cases. Finally, the paper concludes by describing planned extensions to this work.     

Ontology-driven coordination model for multiagent-based mobile workforce brokering systems

Coordination has been recognized by many researchers as the most important feature of multi-agent systems. Coordination is defined as managing interdependencies amongst activities (Malone and Crowston in ACM Comput. Surv. 26(1):87–119, 1994). The traditional approach of implementing a coordination mechanism is to hard-wire it into a coordination system at design time. However, in dynamic and open environments, many attributes of the system cannot be accurately identified at the design time. Therefore, dynamic coordination, capable of coordinating activities at run-time, has emerged. On the other hand, a successful dynamic coordination model for multi-agent systems requires knowledge sharing as well as common vocabulary. Therefore, an ontological approach is an appropriate way in proposing dynamic coordination models for multi-agent systems. In this paper, an Ontology-Driven Dynamic Coordination Model (O-DC) for Multiagent-Based Mobile Workforce Brokering Systems (MWBS) (Mousavi et al. in Int. J. Comput. Sci. 6:(5):557–565, 2010; Mousavi et al. in Proceedings of 4th IEEE international symposium on information technology, ITSim’10, Kuala Lumpur, Malaysia, 15–17 June 2010, vol. 3, pp. 1416–1421, 2010; Mousavi and Nordin in Proceedings of the IEEE international conference on electrical engineering and informatics, Bandung, Indonesia, 17–19 June 2007, pp. 294–297, 2007) is proposed and formulated. Subsequently, the applicability of O-DC is examined via simulation based on a real-world scenario.