Semantics,ontologies and eScience for the geosciences

Semantics, ontologies and eScience are key areas of research that aim to deal with the growing volume, number of sources and heterogeneity of geoscience data, information and knowledge. Following a workshop held at the eScience Institute in Edinburgh on the 7–9th of March 2008, this paper discusses some of the significant research topics and challenges for enhancing geospatial computing using semantic and grid technologies.     

MTCProv: a practical provenance query framework for many-task scientific computing

Scientific research is increasingly assisted by computer-based experiments. Such experiments are often composed of a vast number of loosely-coupled computational tasks that are specified and automated as scientific workflows. This large scale is also characteristic of the data that flows within such “many-task” computations (MTC). Provenance information can record the behavior of such computational experiments via the lineage of process and data artifacts. However, work to date has focused on lineage data models, leaving unsolved issues of recording and querying other aspects, such as domain-specific information about the experiments, MTC behavior given by resource consumption and failure information, or the impact of environment on performance and accuracy. In this work we contribute with MTCProv, a provenance query framework for many-task scientific computing that captures the runtime execution details of MTC workflow tasks on parallel and distributed systems, in addition to standard prospective and data derivation provenance. To help users query provenance data we provide a high level interface that hides relational query complexities. We evaluate MTCProv using an application in protein science, and describe how important query patterns such as correlations between provenance, runtime data, and scientific parameters are simplified and expressed.     

Nonintrusive collection and management of data provenance in scientific workflows

In this paper, we introduce an efficient mechanism to collect, store, and retrieve data provenance information in workflows of multiphysics simulations. Using notifications, we enable the nonintrusive collection of information about workflow events during workflow execution. Combining these events with workflow structure information, constant for every execution of a workflow, we obtain the data provenance information for the specific run of the workflow. Data provenance information is structured into a graph that represents workflow events on the basis of their causal dependency. We use a graph database to store this graph and utilize the traversal framework provided, to efficiently retrieve data provenance information from the graph by traversing backwards from a data object to every workflow event that is part of its provenance. Finally, we integrate data provenance information with semantics of workflow services to provide complete and meaningful data provenance information. Copyright © 2012 John Wiley & Sons, Ltd.     

科学工作流溯源表示和查询技术综述

溯源管理是科学工作流系统的核心功能之一。科学工作流语境下的溯源,可分为工作流定义溯源和工作流执行溯源,分别描述工作流定义和执行阶段的元数据、过程依赖及数据演化。本文重点关注工作流定义溯源和执行溯源的表示及查询技术,并阐释针对科学工作流领域内独有问题,如"黑盒"问题、依赖区分问题以及细粒度溯源等问题的解决方案。文中还将介绍现存的一些面向科学工作流的溯源系统,并提出对溯源技术未来的展望。     

Semantic Web-based geospatial knowledge transformation

Earth and space science research and applications typically involve collecting and analyzing large volumes of geospatial data much of which is derived from other existing data by applying a scientific workflow. Such a step-by-step process can be viewed as a process of geospatial knowledge transformation, which often involves hypotheses, inferences and integrations to derive user-specific data products from the knowledge of domain experts. Our research is focused on reducing the transformation effort by providing component inference and integration tools. The Semantic Web envisions a new standardized information infrastructure to enable interoperable machine-to-machine interactions and automatic or semi-automatic service chaining for deriving knowledge over networks. This paper describes a generic framework and implementation of how the Semantic Web proceeds through the life cycle of geospatial knowledge transformation, from geospatial modeling (knowledge formalization), through model instantiation (service chain) to model execution (data product). Our approach relies on semantic integrations. A number of ontologies used to capture domain knowledge are introduced in this paper as the basis of knowledge bases for describing and reasoning geospatial data and services. Also, a semantically enabled geospatial catalog service is described to enable more effective discovery, automation and integration of geospatial data and services.     

Ontology and rule-based natural language processing approach for interpreting textual regulations on underground utility infrastructure

The nation’s massive underground utility infrastructure must comply with a multitude of regulations. The regulatory compliance checking of underground utilities requires an objective and consistent interpretation of the regulations. However, utility regulations contain a variety of domain-specific terms and numerous spatial constraints regarding the location and clearance of underground utilities. It is challenging for the interpreters to understand both the domain and spatial semantics in utility regulations. To address the challenge, this paper adopts an ontology and rule-based Natural Language Processing (NLP) framework to automate the interpretation of utility regulations – the extraction of regulatory information and the subsequent transformation into logic clauses. Two new ontologies have been developed. The urban product ontology (UPO) is domain-specific to model domain concepts and capture domain semantics on top of heterogeneous terminologies in utility regulations. The spatial ontology (SO) consists of two layers of semantics – linguistic spatial expressions and formal spatial relations – for better understanding the spatial language in utility regulations. Pattern-matching rules defined on syntactic features (captured using common NLP techniques) and semantic features (captured using ontologies) were encoded for information extraction. The extracted information elements were then mapped to their semantic correspondences via ontologies and finally transformed into deontic logic (DL) clauses to achieve the semantic and logical formalization. The approach was tested on the spatial configuration-related requirements in utility accommodation policies. Results show it achieves a 98.2% precision and a 94.7% recall in information extraction, a 94.4% precision and a 90.1% recall in semantic formalization, and an 83% accuracy in logical formalization.     

Semantic integration of enterprise information systems using meta-metadata ontology

This paper proposes a non-domain-specific metadata ontology as a core component in a semantic model-based document management system (DMS), a potential contender towards the enterprise information systems of the next generation. What we developed is the core semantic component of an ontology-driven DMS, providing a robust semantic base for describing documents’ metadata. We also enabled semantic services such as automated semantic translation of metadata from one domain to another. The core semantic base consists of three semantic layers, each one serving a different view of documents’ metadata. The core semantic component’s base layer represents a non-domain-specific metadata ontology founded on ebRIM specification. The main purpose of this ontology is to serve as a meta-metadata ontology for other domain-specific metadata ontologies. The base semantic layer provides a generic metadata view. For the sake of enabling domain-specific views of documents’ metadata, we implemented two domain-specific metadata ontologies, semantically layered on top of ebRIM, serving domain-specific views of the metadata. In order to enable semantic translation of metadata from one domain to another, we established model-to-model mappings between these semantic layers by introducing SWRL rules. Having the semantic translation of metadata automated not only allows for effortless switching between different metadata views, but also opens the door for automating the process of documents long-term archiving. For the case study, we chose judicial domain as a promising ground for improving the efficiency of the judiciary by introducing the semantics in this field.     

基于领域本体的制造网格服务自动组合技术研究

为实现制造网格应用的自动化,提出了一个基于领域本体（ontology）的服务自动组合体系结构及其相应的算法。领域本体基于TOVE、STEP、PSL三个成熟的制造本体和网格服务概念模型构建。体系结构以语义结构良好的用户目标作为输入,输出预约的可执行工作流;通过基于领域本体推理的反向递归组合算法,能够实现本地仓库中工作流的重用和网格范围内服务的新工作流组合;支持抽象、具体两个层面的松耦合工作流组合（对应于编排（Choreography）和编制（Orchestration））;支持面向服务质量的工作流优化选择和工作流预约。实现了一个原型系统,实例的实验结果证明了该体系结构和算法的有效性。     

Automated data provenance capture in spreadsheets,with case studies

One of the most important tasks in eScience is capturing the provenance of data. While scientists frequently use off-the-shelf analysis tools to process and manipulate data, current provenance techniques such as those based on scientific workflows are typically not able to trace internal data manipulations that occur within these tools. In this paper, we focus on one such off-the-shelf tool, MS Excel, which is used by many scientists; specifically, we propose InSituTrac, an automated in situ provenance approach for spreadsheet data in Excel. Our framework captures data provenance unobtrusively in the background, allows for user annotations, provides undo/redo functionality at various levels of granularity, presents the captured provenance in an accessible format, and visualizes captured provenance to support analysis of the provenance log. We highlight several motivating use case scenarios which show how provenance queries can be answered by our approach. Finally, case studies with an atmospheric science research group and a fisheries research group suggest that the automated provenance approach is both efficient and useful to scientists.     

一种面向语义网服务的本体映射框架*

本体的异构性阻碍了语义网服务的互操作。从解决语义网服务中本体的异构问题出发,同时考虑到目前的本体映射系统大多效率不高、映射结果不够准确的问题,提出了一种适用于语义网服务的本体映射方法及系统框架。该方法利用机器学习技术来提高本体映射的自动化程度,利用综合评判技术修正映射结果,以提高本体映射的准确率。采用OAEI 2007的基准测试数据集benchmarks进行实验测试,结果表明本系统的性能基本达到预期效果,能够有效地解决语义网服务中的本体异构问题。     

Extracting semantic relations to enrich domain ontologies

Domain ontologies facilitate the organization, sharing and reuse of domain knowledge, and enable various vertical domain applications to operate successfully. Most methods for automatically constructing ontologies focus on taxonomic relations, such as is-kind-of and is-part-of relations. However, much of the domain-specific semantics is ignored. This work proposes a semi-unsupervised approach for extracting semantic relations from domain-specific text documents. The approach effectively utilizes text mining and existing taxonomic relations in domain ontologies to discover candidate keywords that can represent semantic relations. A preliminary experiment on the natural science domain (Taiwan K9 education) indicates that the proposed method yields valuable recommendations. This work enriches domain ontologies by adding distilled semantics.     

Intelligent dynamic workflow support for a ubiquitous Web service-based manufacturing environment

The ubiquitous environment is increasingly being considered as a platform for finding and integrating separate distributed services. Individuals or businesses can provide their services in the form of Web services in such a ubiquitous environment, and business processes that integrate such ubiquitous Web services can be formed using workflow technology. Workflow design and execution in such a dynamic and distributed environment needs to be very flexible in terms of incorporating changes. Web services may suddenly become unavailable and backup services may need to be found, or several providers of services may not be identifiable at the design time of the workflow. Therefore, dynamically finding and invoking Web services based on the workflow semantics need to be supported. BPEL4WS is the most popular and promising language among the workflow design languages for Web services. However, one of the problems with BPEL4WS is that it references the fixed WSDL file, which makes the workflow less flexible in dynamic and ubiquitous environments. Another problem is that it limits the amount of resources to only those that are specified. This makes it difficult to support semantics for finding similar or backup services in a ubiquitous network. This paper focuses on extending the BPEL4WS framework to include semantics by adding semantic constructs into WSDL and making use of ontologies in the BPEL4WS engine in order to support dynamic workflows suitable for ubiquitous environments.     

基于个人信息领域的语义信息抽取系统①

在利用本体进行信息抽取的基础上,提出了一个基于个人信息领域的语义信息抽取系统框架,将语义抽取从WEB领域扩展到个人信息领域;系统对个人信息领域内的网页,电子邮件,本地数据库和本地文件夹建立本体,根据本体之间的语义关联,实现个人信息领域内数据的交流。系统详细描述了语义信息抽取系统的实现过程,并以电子邮件为例重点介绍了语义信息抽取的算法。     

Ontology matching with semantic verification

Automated Semantic Matching of Ontologies with Verification (ASMOV) is a novel algorithm that uses lexical and structural characteristics of two ontologies to iteratively calculate a similarity measure between them, derives an alignment, and then verifies it to ensure that it does not contain semantic inconsistencies. In this paper, we describe the ASMOV algorithm, and then present experimental results that measure its accuracy using the OAEI 2008 tests, and that evaluate its use with two different thesauri: WordNet, and the Unified Medical Language System (UMLS). These results show the increased accuracy obtained by combining lexical, structural and extensional matchers with semantic verification, and demonstrate the advantage of using a domain-specific thesaurus for the alignment of specialized ontologies.     

Multiple coordinated views for searching and navigating Web content repositories

The advantages and positive effects of multiple coordinated views on search performance have been documented in several studies. This paper describes the implementation of multiple coordinated views within the Media Watch on Climate Change, a domain-specific news aggregation portal available at www.ecoresearch.net/climate that combines a portfolio of semantic services with a visual information exploration and retrieval interface. The system builds contextualized information spaces by enriching the content repository with geospatial, semantic and temporal annotations, and by applying semi-automated ontology learning to create a controlled vocabulary for structuring the stored information. Portlets visualize the different dimensions of the contextualized information spaces, providing the user with multiple views on the latest news media coverage. Context information facilitates access to complex datasets and helps users navigate large repositories of Web documents. Currently, the system synchronizes information landscapes, domain ontologies, geographic maps, tag clouds and just-in-time information retrieval agents that suggest similar topics and nearby locations.