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This paper gives an outline of my PhD thesis which describes the integration of managing uncertainty into ontology mapping. Ontology mapping is one of the most important tasks for ontology interoperability and its main aim is to find semantic relationships between entities (i.e. concept, attribute, and relation) of two ontologies, However, in the process of mapping, uncertainty and incompleteness of semantics in the syntactic representation and description of relations between entities in ontologies will lead to imprecise results. If we want to obtain better results, it becomes more significant for the ontology mapping to be able to deal with uncertainty. [ABSTRACT FROM AUTHOR]

The Semantic Web (SW) has been envisioned to enable software tools or Web Services, respectively, to process information provided on the Web automatically. For this purpose, languages for representing the semantics of data by means of ontologies have been proposed such as RDF(S) and OWL. While the semantics of RDF(S) requires a non-standard model-theory that goes beyond first order logics, OWL is intended to model subsets of first order logics. OWL consists of three variants that are layered on each other. The less expressive variants OWL-Light and OWL-DL correspond to the Description Logics $\mathcal{SHIF}$(D) and $\mathcal{SHOIN}$(D) [1], respectively, and thus to subsets of First Order Logics [2]. [ABSTRACT FROM AUTHOR]

The use of tags to describe Web resources in a collaborative manner has experienced rising popularity among Web users in recent years. The product of such activity is given the name folksonomy, which can be considered as a scheme of organizing information in the users' own way. This research work attempts to analyze tripartite graphs - graphs involving users, tags and resources - of folksonomies and discuss how these elements acquire their semantics through their associations with other elements, a process we call mutual contextualization. By studying such process, we try to identify solutions to problems such as tag disambiguation, retrieving documents of similar topics and discovering communities of users. This paper describes the basis of the research work, mentions work done so far and outlines future plans. [ABSTRACT FROM AUTHOR]

Ontology mapping is to find semantic correspondences between similar elements of different ontologies. It is critical to achieve semantic interoperability in the WWW. This paper proposes a new generic and scalable ontology mapping approach based on propagation theory, information retrieval technique and artificial intelligence model. The approach utilizes both linguistic and structural information, measures the similarity of different elements of ontologies in a vector space model, and deals with constraints using the interactive activation network. The results of pilot study, the PRIOR, are promising and scalable. [ABSTRACT FROM AUTHOR]

Only few well-maintained domain ontologies can be found on the Web. The likely reasons for the lack of useful domain ontologies include that (1) informal means to convey intended meaning more efficiently are used for ontology specification only to a very limited extent, (2) many relevant domains of discourse show a substantial degree of conceptual dynamics, (3) ontology representation languages are hard to understand for the majority of (potential) ontology users and domain experts, and (4) the community does not have control over the ontology evolution. In this thesis, we propose to (1) ground a methodology for community-grounded ontology building on the culture and philosophy of wikis by giving users who have no or little expertise in ontology engineering the opportunity to contribute in all stages of the ontology lifecycle and (2) exploit the combination of human and computational intelligence to discover and resolve inconsistencies and align lightweight domain ontologies. The contribution of this thesis is a methodology and prototype for community-grounded building and evolution of lightweight domain ontologies. [ABSTRACT FROM AUTHOR]

Unsatisfactory performance of ontology mapping methods on many tasks seems to indicate the need for employing some background knowledge. My PhD work focuses on exploiting pattern-like structures for this purpose. Design patterns are related to different styles of modelling ontology structure and/or naming of concepts. On the other hand, mapping patterns emerge as result of the mapping process. Preliminary results and schedule of future work are presented. [ABSTRACT FROM AUTHOR]

We propose a logic-based framework to model negotiations among agents in scenarios with fully incomplete information. In particular, we address multi-issue bilateral negotiations, with issues are expressed and related to each other via an OWL ontology. In particular we use OWL-DL sub-language and exploit its formal semantics, based on $\mathcal{SHOIN}(D)$. Agents goals are expressed through OWL-DL as (complex) concept expressions, and the worth of goals as weights over concepts. Noteworthily, we adopt a very general setting with incomplete information by letting agents keep both goals and worths of goals as private information. Moreover we propose a protocol for agents to negotiate on and reach an agreement, exploiting inference services. Agents, according to the protocol, can pursue different sets of strategies —depending on their type— and we illustrate and motivate two possible strategies. [ABSTRACT FROM AUTHOR]

Our research explores the possibilities for factoring culture into user models, working towards cultural adaptivity in the Semantic Web. The aim is to automate the localization process by acquiring details about an individual's cultural background. The paper shows how we have based our approach on research findings from related fields, outlining the connection between the cultural user and domain model ontologies, and a test environment. Furthermore, we describe a preliminary plan of how to validate our cultural user model by comparison to our previous experience in the manual adaptation of imbuto. [ABSTRACT FROM AUTHOR]

Recent work in the field of middleware technology proposes semantic spaces as a tool for coping with the scalability, heterogeneity and dynamism issues of large scale distributed environments. Reflective middleware moreover offers answers to the needs for adaptivity and self-determination of systems. Based on experiences with traditional middleware we argue that ontology-driven management is a major advancement for semantic spaces and provides the fundamental means for reflection. In this research we look at ontology-based metadata to turn semantic spaces into reflective middleware. [ABSTRACT FROM AUTHOR]

This paper describes a system capable of semi-automatically filling an XML template from free texts in the clinical domain (practice guidelines). The XML template includes semantic information not explicitly encoded in the text (pairs of conditions and actions/recommendations). Therefore, there is a need to compute the exact scope of conditions over text sequences expressing the required actions. We present in this paper the rules developed for this task. We show that the system yields good performance when applied to the analysis of French practice guidelines. [ABSTRACT FROM AUTHOR]

The move towards a semantic web has been in progress for many years and more recently there have been applications that make use of semantic web technology. One of the features that made the Web so easy to use is the ability to search web pages in a matter of seconds through the use of search engines. Now that the use of OWL and RDF as a knowledge representation format is increasing, the possibility appears to improve the quality of searching by using the semantic web to enhance the ‘ordinary' Web. This paper outlines an architecture for using distributed knowledge bases to assist and improve searching on the web. [ABSTRACT FROM AUTHOR]

Potluck is a web user interface (Figure 1) that lets casual users- those without programming skills and data modeling expertise-repurpose heterogeneous Semantic Web data. It lets users merge, navigate, visualize, and clean up data all at the same time, using direct visual manipulation. This iterative process of integrating the data while constructing useful visualizations is desirable when the user is unfamiliar with the data at the beginning -a common case-and wishes to get immediate value out of the data without having to spend the overhead of completely and perfectly integrating the data first. [ABSTRACT FROM AUTHOR]

Many enterprise systems face the same kind of information processing problems that exist on the web in general, and creating semantic solutions often involve constructing an enterprise ontology. Ontology engineering in turn needs to be semi-automatic in order to reduce the effort and need for expertise. By introducing knowledge reuse in ontology construction the construction effort can be further reduced and the quality of the ontology output increased. The proposed research focuses on a hybrid approach for ontology construction based on the methodology of case-based reasoning in combination with ontology patterns. [ABSTRACT FROM AUTHOR]

Revyu.com is a live, publicly accessible reviewing and rating Web site, designed to be usable by humans whilst transparently generating machine-readable RDF metadata for the Semantic Web, based on their input. The site uses Semantic Web specifications such as RDF and SPARQL, and the latest Linked Data best practices to create a major node in a potentially Web-wide ecosystem of reviews and related data. Throughout the implementation of Revyu design decisions have been made that aim to minimize the burden on users, by maximizing the reuse of external data sources, and allowing less structured human input (in the form of Web2.0-style tagging) from which stronger semantics can later be derived. Links to external sources such as DBpedia are exploited to create human-oriented mashups at the HTML level, whilst links are also made in RDF to ensure Revyu plays a first class role in the blossoming Web of Data. The site is available at . [ABSTRACT FROM AUTHOR]

As an increasing number of applications on the web contain some elements of spatial data, there is a need to efficiently integrate Semantic Web technologies and spatial data processing. This paper describes a prototype system for storing spatial data and Semantic Web data together in a SPatially-AUgmented Knowledgebase (SPAUK) without sacrificing query efficiency. The goals are motivated through use several use cases. The prototype's design and architecture are described, and resulting performance improvements are discussed. [ABSTRACT FROM AUTHOR]

The main objective of the CHIP project is to demonstrate how Semantic Web technologies can be deployed to provide personalized access to digital museum collections. We illustrate our approach with the digital database ARIA of the Rijksmuseum Amsterdam. For the semantic enrichment of the Rijksmuseum ARIA database we collaborated with the CATCH STITCH project to produce mappings to Iconclass, and with the MultimediaN E-culture project to produce the RDF/OWL of the ARIA and Adlib databases. The main focus of CHIP is on exploring the potential of applying adaptation techniques to provide personalized experience for the museum visitors both on the Web site and in the museum. [ABSTRACT FROM AUTHOR]

Data warehouse is now widely used in business analysis and decision making processes. To adapt the rapidly changing business environment, we develop a tool to make data warehouses more business-friendly by using Semantic Web technologies. The main idea is to make business semantics explicit by uniformly representing the business metadata (i.e. conceptual enterprise data model and multidimensional model) with an extended OWL language. Then a mapping from the business metadata to the schema of the data warehouse is built. When an analysis request is raised, a customized data mart with data populated from the data warehouse can be automatically generated with the help of this built-in knowledge. This tool, called Enterprise Information Asset Workbench (EIAW), is deployed at the Taikang Life Insurance Company, one of the top five insurance companies of China. User feedback shows that OWL provides an excellent basis for the representation of business semantics in data warehouse, but many necessary extensions are also needed in the real application. The user also deemed this tool very helpful because of its flexibility and speeding up data mart deployment in face of business changes. [ABSTRACT FROM AUTHOR]

The ICT landscape is developing into a highly-interactive distributed environment in which people interact with multiple devices (e.g. portable devices such as mobile phones and home equipment such as TV's) and multiple applications (e.g. computer programs such as Web browsers and dedicated Web services) [1]. Globally, the industry is being driven by the shift away from old models - from physical space to digital space. New methods emerge for getting content such as TV programs via the Web. Almost half of the people want to watch TV content on their PC's; they want to make a bridge between a TV and a PC, perhaps even sitting in a home office [2]. The information overload is enormous and the content presented is hardly adapted to the prior knowledge, to the preferences and to the current situation of the user. [ABSTRACT FROM AUTHOR]

Grouping is an attractive interaction metaphor for users to create reference collections of Web resources they are interested in. Each grouping activity has a certain semantics: things which were previously unrelated are now connected with others via the group. We present the GroupMe! application which allows users to group and arrange multimedia Web resources they are interested in. GroupMe! has an easy-to-use interface for gathering and grouping of resources, and allows users to tag everything they like. The semantics of any user interaction is captured, transformed and stored as adequate RDF descriptions. As an example application of this automatically derived RDF content, we show the enhancement of search for tagged Web resources, which evaluates the grouping information to deduce additional contextual information about the resources. GroupMe! is available via http://www.groupme.org. [ABSTRACT FROM AUTHOR]

Business Intelligence (BI) requires the acquisition and aggregation of key pieces of knowledge from multiple sources in order to provide valuable information to customers or feed statistical BI models and tools. The massive amount of information available to business analysts makes information extraction and other natural language processing tools key enablers for the acquisition and use of that semantic information. We describe the application of ontology-based extraction and merging in the context of a practical e-business application for the EU MUSING Project where the goal is to gather international company intelligence and country/region information. The results of our experiments so far are very promising and we are now in the process of building a complete end-to-end solution. [ABSTRACT FROM AUTHOR]

An ontology provides a precise specification of the vocabulary used by a community of interest (COI). Multiple communities of interest may describe the same concept using the same or different terms. When such communities interact, ontology alignment and translation is required. This is typically a time consuming process. This paper describes Snoggle, an open source tool designed to ease development of ontology translation rules, and discusses its application to geospatial ontologies. [ABSTRACT FROM AUTHOR]

This paper describes a large case study that explores the applicability of ontology reasoning to problems in the medical domain. We investigate whether it is possible to use such reasoning to automate common clinical tasks that are currently labor intensive and error prone, and focus our case study on improving cohort selection for clinical trials. An obstacle to automating such clinical tasks is the need to bridge the semantic gulf between raw patient data, such as laboratory tests or specific medications, and the way a clinician interprets this data. Our key insight is that matching patients to clinical trials can be formulated as a problem of semantic retrieval. We describe the technical challenges to building a realistic case study, which include problems related to scalability, the integration of large ontologies, and dealing with noisy, inconsistent data. Our solution is based on the SNOMED CT® ontology, and scales to one year of patient records (approx. 240,000 patients). [ABSTRACT FROM AUTHOR]

Semantic Web conferences such as ESWC and ISWC offer prime opportunities to test and showcase semantic technologies. Conference metadata about people, papers and talks is diverse in nature and neither too small to be uninteresting or too big to be unmanageable. Many metadata-related challenges that may arise in the Semantic Web at large are also present here. Metadata must be generated from sources which are often unstructured and hard to process, and may originate from many different players, therefore suitable workflows must be established. Moreover, the generated metadata must use appropriate formats and vocabularies, and be served in a way that is consistent with the principles of linked data. This paper reports on the metadata efforts from ESWC and ISWC, identifies specific issues and barriers encountered during the projects, and discusses how these were approached. Recommendations are made as to how these may be addressed in the future, and we discuss how these solutions may generalize to metadata production for the Semantic Web at large. [ABSTRACT FROM AUTHOR]

This paper introduces a framework to add a semantic web layer to legacy organizational information, and describes its application to the use case provided by the Italian National Research Council (CNR) intraweb. Building on a traditional web-based view of information from different legacy databases, we have performed a semantic porting of data into a knowledge base, dependent on an OWL domain ontology. We have enriched the knowledge base by means of text mining techniques, in order to discover on-topic relations. Several reasoning techniques have been applied, in order to infer relevant implicit relationships. Finally, the ontology and the knowledge base have been deployed on a semantic wiki by means of the WikiFactory tool, which allows users to browse the ontology and the knowledge base, to introduce new relations, to revise wrong assertions in a collaborative way, and to perform semantic queries. In our experiments, we have been able to easily implement several functionalities, such as expert finding, by simply formulating ad-hoc queries from either an ontology editor or the semantic wiki interface. The result is an intelligent and collaborative front end, which allow users to add information, fill gaps, or revise existing information on a semantic basis, while keeping the knowledge base automatically updated. [ABSTRACT FROM AUTHOR]