Ontology supported competency system

Traditionally representation of competencies has been very difficult using computer-based techniques. This paper introduces competencies, how they are represented, and the related concept of competency frameworks and the difficulties in using traditional ontology techniques to formalise them. A “vaguely” formalised framework has been developed within the EU project TRACE and is presented. The framework can be used to represent different competencies and competency frameworks. Through a case study using an example from the IT sector, it is shown how these can be used by individuals and organisations to specify their individual competency needs. Furthermore it is described how these representations are used for comparisons between different specifications applying ontologies and ontology toolsets. The end result is a comparison that is not binary, but tertiary, providing “definite matches”, possible / partial matches, and “no matches” using a “traffic light” analogy.

Entity-based semantic search on conversational transcripts semantic

This paper describes the implementation of a semantic web search engine on conversation styled transcripts. Our choice of data is Hansard, a publicly available conversation style transcript of parliamentary debates. The current search engine implementation on Hansard is limited to running search queries based on keywords or phrases hence lacks the ability to make semantic inferences from user queries. By making use of knowledge such as the relationship between members of parliament, constituencies, terms of office, as well as topics of debates the search results can be improved in terms of both relevance and coverage. Our contribution is not algorithmic instead we describe how we exploit a collection of external data sources, ontologies, semantic web vocabularies and named entity extraction in the analysis of underlying semantics of user queries as well as the semantic enrichment of the search index thereby improving the quality of results.

A Brassica exon array for whole-transcript gene expression profiling

Affymetrix GeneChip (R) arrays are used widely to study transcriptional changes in response to developmental and environmental stimuli. GeneChip (R) arrays comprise multiple 25-mer oligonucleotide probes per gene and retain certain advantages over direct sequencing. For plants, there are several public GeneChip (R) arrays whose probes are localised primarily in 39 exons. Plant whole-transcript (WT) GeneChip (R) arrays are not yet publicly available, although WT resolution is needed to study complex crop genomes such as Brassica, which are typified by segmental duplications containing paralogous genes and/or allopolyploidy. Available sequence data were sampled from the Brassica A and C genomes, and 142,997 gene models identified. The assembled gene models were then used to establish a comprehensive public WT exon array for transcriptomics studies. The Affymetrix GeneChip (R) Brassica Exon 1.0 ST Array is a 5 mu M feature size array, containing 2.4 million 25-base oligonucleotide probes representing 135,201 gene models, with 15 probes per gene distributed among exons. Discrimination of the gene models was based on an E-value cut-off of 1E(-5), with <= 98 sequence identity. The 135 k Brassica Exon Array was validated by quantifying transcriptome differences between leaf and root tissue from a reference Brassica rapa line (R-o-18), and categorisation by Gene Ontologies (GO) based on gene orthology with Arabidopsis thaliana. Technical validation involved comparison of the exon array with a 60-mer array platform using the same starting RNA samples. The 135 k Brassica Exon Array is a robust platform. All data relating to the array design and probe identities are available in the public domain and are curated within the BrassEnsembl genome viewer at http://www.brassica.info/BrassEnsembl/index.html.

OntoMap: an ontology-based architecture to perform the semantic mapping between an interlingua and software components

This paper is about the use of natural language to communicate with computers. Most researches that have pursued this goal consider only requests expressed in English. A way to facilitate the use of several languages in natural language systems is by using an interlingua. An interlingua is an intermediary representation for natural language information that can be processed by machines. We propose to convert natural language requests into an interlingua [universal networking language (UNL)] and to execute these requests using software components. In order to achieve this goal, we propose OntoMap, an ontology-based architecture to perform the semantic mapping between UNL sentences and software components. OntoMap also performs component search and retrieval based on semantic information formalized in ontologies and rules.

Base Revision for Ontology Debugging

Belief Revision deals with the problem of adding new information to a knowledge base in a consistent way. Ontology Debugging, on the other hand, aims to find the axioms in a terminological knowledge base which caused the base to become inconsistent. In this article, we propose a belief revision approach in order to find and repair inconsistencies in ontologies represented in some description logic (DL). As the usual belief revision operators cannot be directly applied to DLs, we propose new operators that can be used with more general logics and show that, in particular, they can be applied to the logics underlying OWL-DL and Lite.

Design issues for knowledge artifacts

The notion of knowledge artifact has rapidly gained popularity in the fields of general knowledge management and more recently knowledge-based systems. The main goal on this paper is to propose and discuss a methodology for the design and implementation of knowledge-based systems founded on knowledge artifacts. We advocate that the systems built according to this methodology can be effective to convey the flow of knowledge between different communities of practice. Our methodology has been developed from the ground up, i.e. we have built some concrete systems based on the abstract notion of knowledge artifact and synthesized our methodology based on reflections upon our experiences building these systems. In this paper, we also describe the most relevant systems we have built and how they have guided us to the synthesis of our proposed methodology. (C) 2008 Elsevier B.V. All rights reserved.

Prototype Generation from Ontology Charts

Semantic Analysis is a business analysis method designed to capture system requirements. While these requirements may be represented as text, the method also advocates the use of Ontology Charts to formally denote the system's required roles, relationships and forms of communication. Following model driven engineering techniques, Ontology Charts can be transformed to temporal Database schemas, class diagrams and component diagrams, which can then be used to produce software systems. A nice property of these transformations is that resulting system design models lend themselves to complicated extensions that do not require changes to the design models. For example, resulting databases can be extended with new types of data without the need to modify the database schema of the legacy system. Semantic Analysis is not widely used in software engineering, so there is a lack of experts in the field and no design patterns are available. This make it difficult for the analysts to pass organizational knowledge to the engineers. This study describes an implementation that is readily usable by engineers, which includes an automated technique that can produce a prototype from an Ontology Chart. The use of such tools should enable developers to make use of Semantic Analysis with minimal expertise of ontologies and MDA.

A Harmonized Vocabulary For Water Quality

Interoperability of water quality data depends on the use of common models, schemas and vocabularies. However, terms are usually collected during different activities and projects in isolation of one another, resulting in vocabularies that have the same scope being represented with different terms, using different formats and formalisms, and published in various access methods. Significantly, most water quality vocabularies conflate multiple concepts in a single term, e.g. quantity kind, units of measure, substance or taxon, medium and procedure. This bundles information associated with separate elements from the OGC Observations and Measurements (O&M) model into a single slot. We have developed a water quality vocabulary, formalized using RDF, and published as Linked Data. The terms were extracted from existing water quality vocabularies. The observable property model is inspired by O&M but aligned with existing ontologies. The core is an OWL ontology that extends the QUDT ontology for Unit and QuantityKind definitions. We add classes to generalize the QuantityKind model, and properties for explicit description of the conflated concepts. The key elements are defined to be sub-classes or sub-properties of SKOS elements, which enables a SKOS view to be published through standard vocabulary APIs, alongside the full view. QUDT terms are re-used where possible, supplemented with additional Unit and QuantityKind entries required for water quality. Along with items from separate vocabularies developed for objects, media, and procedures, these are linked into definitions in the actual observable property vocabulary. Definitions of objects related to chemical substances are linked to items from the Chemical Entities of Biological Interest (ChEBI) ontology. Mappings to other vocabularies, such as DBPedia, are in separately maintained files. By formalizing the model for observable properties, and clearly labelling the separate concerns, water quality observations from different sources may be more easily merged and also transformed to O&M for cross-domain applications.

Optimized Water Demand Management Through Intelligent Sensing And Analytics: The WISDOM Approach

New business and technology platforms are required to sustainably manage urban water resources [1,2]. However, any proposed solutions must be cognisant of security, privacy and other factors that may inhibit adoption and hence impact. The FP7 WISDOM project (funded by the European Commission - GA 619795) aims to achieve a step change in water and energy savings via the integration of innovative Information and Communication Technologies (ICT) frameworks to optimize water distribution networks and to enable change in consumer behavior through innovative demand management and adaptive pricing schemes [1,2,3]. The WISDOM concept centres on the integration of water distribution, sensor monitoring and communication systems coupled with semantic modelling (using ontologies, potentially connected to BIM, to serve as intelligent linkages throughout the entire framework) and control capabilities to provide for near real-time management of urban water resources. Fundamental to this framework are the needs and operational requirements of users and stakeholders at domestic, corporate and city levels and this requires the interoperability of a number of demand and operational models, fed with data from diverse sources such as sensor networks and crowsourced information. This has implications regarding the provenance and trustworthiness of such data and how it can be used in not only the understanding of system and user behaviours, but more importantly in the real-time control of such systems. Adaptive and intelligent analytics will be used to produce decision support systems that will drive the ability to increase the variability of both supply and consumption [3]. This in turn paves the way for adaptive pricing incentives and a greater understanding of the water-energy nexus. This integration is complex and uncertain yet being typical of a cyber-physical system, and its relevance transcends the water resource management domain. The WISDOM framework will be modeled and simulated with initial testing at an experimental facility in France (AQUASIM – a full-scale test-bed facility to study sustainable water management), then deployed and evaluated in in two pilots in Cardiff (UK) and La Spezia (Italy). These demonstrators will evaluate the integrated concept providing insight for wider adoption.

Harmonization Of Vocabularies For Water Data

Observational data encodes values of properties associated with a feature of interest, estimated by a specified procedure. For water the properties are physical parameters like level, volume, flow and pressure, and concentrations and counts of chemicals, substances and organisms. Water property vocabularies have been assembled at project, agency and jurisdictional level. Organizations such as EPA, USGS, CEH, GA and BoM maintain vocabularies for internal use, and may make them available externally as text files. BODC and MMI have harvested many water vocabularies alongside others of interest in their domain, formalized the content using SKOS, and published them through web interfaces. Scope is highly variable both within and between vocabularies. Individual items may conflate multiple concerns (e.g. property, instrument, statistical procedure, units). There is significant duplication between vocabularies. Semantic web technologies provide the opportunity both to publish vocabularies more effectively, and achieve harmonization to support greater interoperability between datasets. - Models for vocabulary items (property, substance/taxon, process, unit-of-measure, etc) may be formalized OWL ontologies, supporting semantic relations between items in related vocabularies; - By specializing the ontology elements from SKOS concepts and properties, diverse vocabularies may be published through a common interface; - Properties from standard vocabularies (e.g. OWL, SKOS, PROV-O and VAEM) support mappings between vocabularies having a similar scope - Existing items from various sources may be assembled into new virtual vocabularies However, there are a number of challenges: - use of standard properties such as sameAs/exactMatch/equivalentClass require reasoning support; - items have been conceptualised as both classes and individuals, complicating the mapping mechanics; - re-use of items across vocabularies may conflict with expectations concerning URI patterns; - versioning complicates cross-references and re-use. This presentation will discuss ways to harness semantic web technologies to publish harmonized vocabularies, and will summarise how many of the challenges may be addressed.

Ontology-based metadata for e-learning content

Nowadays, the popularity of the Web encourages the development of Hypermedia Systems dedicated to e-learning. Nevertheless, most of the available Web teaching systems apply the traditional paper-based learning resources presented as HTML pages making no use of the new capabilities provided by the Web. There is a challenge to develop educative systems that adapt the educative content to the style of learning, context and background of each student. Another research issue is the capacity to interoperate on the Web reusing learning objects. This work presents an approach to address these two issues by using the technologies of the Semantic Web. The approach presented here models the knowledge of the educative content and the learner’s profile with ontologies whose vocabularies are a refinement of those defined on standards situated on the Web as reference points to provide semantics. Ontologies enable the representation of metadata concerning simple learning objects and the rules that define the way that they can feasibly be assembled to configure more complex ones. These complex learning objects could be created dynamically according to the learners’ profile by intelligent agents that use the ontologies as the source of their beliefs. Interoperability issues were addressed by using an application profile of the IEEE LOM- Learning Object Metadata standard.