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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]

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]

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]

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]

Instance-based ontology mapping is a promising family of solutions to a class of ontology alignment problems. It crucially depends on measuring the similarity between sets of annotated instances. In this paper we study how the choice of co-occurrence measures affects the performance of instance-based mapping. To this end, we have implemented a number of different statistical co-occurrence measures. We have prepared an extensive test case using vocabularies of thousands of terms, millions of instances, and hundreds of thousands of co-annotated items. We have obtained a human Gold Standard judgement for part of the mapping-space. We then study how the different co-occurrence measures and a number of algorithmic variations perform on our benchmark dataset as compared against the Gold Standard. Our systematic study shows excellent results of instance-based matching in general, where the more simple measures often outperform more sophisticated statistical co-occurrence measures. [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]

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]

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]

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]

As more and more reusable structured data appears on the Web, casual users will want to take into their own hands the task of mashing up data rather than wait for mash-up sites to be built that address exactly their individually unique needs. In this paper, we present Potluck, a Web user interface that lets casual users -those without programming skills and data modeling expertise-mash up data themselves. Potluck is novel in its use of drag and drop for merging fields, its integration and extension of the faceted browsing paradigm for focusing on subsets of data to align, and its application of simultaneous editing for cleaning up data syntactically.Potluck also lets the user construct rich visualizations of data in-place as the user aligns and cleans up the data. 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. A user study on Potluck indicated that it was usable and learnable, and elicited excitement from programmers who, even with their programming skills, previously had great difficulties performing data integration. [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]

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]

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]

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]

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]

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]

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]

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]

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 shows how semantic web techniques can be applied to solving problems of distributed content creation, discovery, linking, aggregation, and reuse in health information portals, both from end-users' and content publishers' viewpoints. As a case study, the national semantic health portal HealthFinland is presented. It provides citizens with intelligent searching and browsing services to reliable and up-to-date health information created by various health organizations in Finland. The system is based on a shared semantic metadata schema, ontologies, and ontology services. The content includes metadata about thousands of web documents such as web pages, articles, reports, campaign information, news, services, and other information related to health. [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]

Ontologies proliferate with the growth of the Semantic Web. However, most of data on the Web are still stored in relational databases. Therefore, it is important to establish interoperability between relational databases and ontologies for creating a Web of data. An effective way to achieve interoperability is finding mappings between relational database schemas and ontologies. In this paper, we propose a new approach to discovering simple mappings between a relational database schema and an ontology. It exploits simple mappings based on virtual documents, and eliminates incorrect mappings via validating mapping consistency. Additionally, it also constructs a special type of semantic mappings, called contextual mappings, which is useful for practical applications. Experimental results demonstrate that our approach performs well on several data sets from real world domains. [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]

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]

We describe a novel approach by which software can assess the ability of a confederation of heterogeneous systems to interoperate to achieve a given purpose. The approach uses ontologies and knowledge bases (KBs) to capture the salient characteristics of systems, on the one hand, and of tasks for which these systems will be employed, on the other. Rules are used to represent the conditions under which the capabilities provided by systems can fulfill the capabilities needed to support the roles and interactions that make up each task. An Analyzer component employs these KBs and rules to determine if a given confederation will be adequate, to generate suitable confederations from a collection of available systems, to pre-diagnose potential interoperability problems that might arise, and to suggest system configuration options that will help to make interoperability possible. We have demonstrated the feasibility of this approach using a prototype Analyzer and KBs. [ABSTRACT FROM AUTHOR]

This paper presents a semantic case-based reasoning framework for text categorization. Text categorization is the task of classifying text documents under predefined categories. Accidentology is our application field and the goal of our framework is to classify documents describing real road accidents under predefined road accident prototypes, which also are described by text documents. Accidents are described by accident reports while accident prototypes are described by accident scenarios. Thus, text categorization is done by assigning each accident report to an accident scenario, which highlights particular mechanisms leading to accident. We propose a textual case-based reasoning approach (TCBR), which allows us to integrate both textual and domain knowledge aspects in order to carry out this categorization. CBR solves a new problem (target case) by identifying its similarity to one or several previously solved problems (source cases) stored in a case base and by adapting their known solutions. Cases of our framework are created from text. Most of TCBR applications create cases from text by using Information Retrieval techniques, which leads to knowledge-poor descriptions of cases. We show that using semantic resources (two ontologies of accidentology) makes possible to overcome this difficulty, and allows us to enrich cases by using formal knowledge. In this paper, we argue that semantic resources are likely to improve the quality of cases created from text, and, therefore, such resources can support the reasoning cycle. We illustrate this claim with our framework developed to classify documents in the accidentology domain. [ABSTRACT FROM AUTHOR]

Governments often hold very rich data and whilst much of this information is published and available for re-use by others, it is often trapped by poor data structures, locked up in legacy data formats or in fragmented databases. One of the great benefits that Semantic Web (SW) technology offers is facilitating the large scale integration and sharing of distributed data sources. At the heart of information policy in the UK, the Office of Public Sector Information (OPSI) is the part of the UK government charged with enabling the greater re-use of public sector information. This paper describes the actions, findings, and lessons learnt from a pilot study, involving several parts of government and the public sector. The aim was to show to government how they can adopt SW technology for the dissemination, sharing and use of its data. [ABSTRACT FROM AUTHOR]

DBpedia is a community effort to extract structured information from Wikipedia and to make this information available on the Web. DBpedia allows you to ask sophisticated queries against datasets derived from Wikipedia and to link other datasets on the Web to Wikipedia data. We describe the extraction of the DBpedia datasets, and how the resulting information is published on the Web for human- and machine-consumption. We describe some emerging applications from the DBpedia community and show how website authors can facilitate DBpedia content within their sites. Finally, we present the current status of interlinking DBpedia with other open datasets on the Web and outline how DBpedia could serve as a nucleus for an emerging Web of open data. [ABSTRACT FROM AUTHOR]

Semantic search promises to provide more accurate result than present-day keyword search. However, progress with semantic search has been delayed due to the complexity of its query languages. In this paper, we explore a novel approach of adapting keywords to querying the semantic web: the approach automatically translates keyword queries into formal logic queries so that end users can use familiar keywords to perform semantic search. A prototype system named ‘SPARK' has been implemented in light of this approach. Given a keyword query, SPARK outputs a ranked list of SPARQL queries as the translation result. The translation in SPARK consists of three major steps: term mapping, query graph construction and query ranking. Specifically, a probabilistic query ranking model is proposed to select the most likely SPARQL query. In the experiment, SPARK achieved an encouraging translation result. [ABSTRACT FROM AUTHOR]

Several proposals have been put forward to support distributed agent cooperation in the Semantic Web, by allowing concepts and roles in one ontology be reused in another ontology. In general, these proposals reduce the autonomy of each ontology by defining the semantics of the ontology to depend on the semantics of the other ontologies. We propose a new framework for managing autonomy in a set of cooperating ontologies (or ontology space). In this framework, each language entity (concept/role/individual) in an ontology may have its meaning assigned either locally with respect to the semantics of its own ontology, to preserve the autonomy of the ontology, or globally with respect to the semantics of any neighbouring ontology in which it is defined, thus enabling semantic cooperation between multiple ontologies. In this way, each ontology has a "subjective semantics" based on local interpretation and a "foreign semantics" based on semantic binding to neighbouring ontologies. We study the properties of these two semantics and describe the conditions under which entailment and satisfiability are preserved. We also introduce two reasoning mechanisms under this framework: "cautious reasoning" and "brave reasoning". Cautious reasoning is done with respect to a local ontology and its neighbours (those ontologies in which its entities are defined); brave reasoning is done with respect to the transitive closure of this relationship. This framework is independent of ontology languages. As a case study, for Description Logic $\mathcal{ALCN}$ we present two tableau-based algorithms for performing each form of reasonings and prove their correctness. [ABSTRACT FROM AUTHOR]

This paper deals with the problem of exploring hierarchical semantics from social annotations. Recently, social annotation services have become more and more popular in Semantic Web. It allows users to arbitrarily annotate web resources, thus, largely lowers the barrier to cooperation. Furthermore, through providing abundant meta-data resources, social annotation might become a key to the development of Semantic Web. However, on the other hand, social annotation has its own apparent limitations, for instance, 1) ambiguity and synonym phenomena and 2) lack of hierarchical information. In this paper, we propose an unsupervised model to automatically derive hierarchical semantics from social annotations. Using a social bookmark service Del.icio.us as example, we demonstrate that the derived hierarchical semantics has the ability to compensate those shortcomings. We further apply our model on another data set from Flickr to testify our model's applicability on different environments. The experimental results demonstrate our model's efficiency. [ABSTRACT FROM AUTHOR]

As an extension to the current Web, Semantic Web will not only contain structured data with machine understandable semantics but also textual information. While structured queries can be used to find information more precisely on the Semantic Web, keyword searches are still needed to help exploit textual information. It thus becomes very important that we can combine precise structured queries with imprecise keyword searches to have a hybrid query capability. In addition, due to the huge volume of information on the Semantic Web, the hybrid query must be processed in a very scalable way. In this paper, we define such a hybrid query capability that combines unary tree-shaped structured queries with keyword searches. We show how existing information retrieval (IR) index structures and functions can be reused to index semantic web data and its textual information, and how the hybrid query is evaluated on the index structure using IR engines in an efficient and scalable manner. We implemented this IR approach in an engine called Semplore. Comprehensive experiments on its performance show that it is a promising approach. It leads us to believe that it may be possible to evolve current web search engines to query and search the Semantic Web. Finally, we breifly describe how Semplore is used for searching Wikipedia and an IBM customer's product information. [ABSTRACT FROM AUTHOR]

Hierarchical classifications are used pervasively by humans as a means to organize their data and knowledge about the world. One of their main advantages is that natural language labels, used to describe their contents, are easily understood by human users. However, at the same time, this is also one of their main disadvantages as these same labels are ambiguous and very hard to be reasoned about by software agents. This fact creates an insuperable hindrance for classifications to being embedded in the Semantic Web infrastructure. This paper presents an approach to converting classifications into lightweight ontologies, and it makes the following contributions: (i) it identifies the main NLP problems related to the conversion process and shows how they are different from the classical problems of NLP; (ii) it proposes heuristic solutions to these problems, which are especially effective in this domain; and (iii) it evaluates the proposed solutions by testing them on DMoz data. [ABSTRACT FROM AUTHOR]

The ability to compute the differences that exist between two RDF models is an important step to cope with the evolving nature of the Semantic Web (SW). In particular, RDF Deltas can be employed to reduce the amount of data that need to be exchanged and managed over the network and hence build advanced SW synchronization and versioning services. By considering Deltas as sets of change operations, in this paper we study various RDF comparison functions in conjunction with the semantics of the underlying change operations and formally analyze their possible combinations in terms of correctness, minimality, semantic identity and redundancy properties. [ABSTRACT FROM AUTHOR]

For the development of Semantic Web technology, researchers and developers in the Semantic Web community need to focus on the areas in which human reasoning is particularly difficult. Two studies in this paper demonstrate that people are predisposed to use class-inclusion labels for inductive judgments. This tendency appears to stem from a general characteristic of human reasoning - using heuristics to solve problems. The inference engines and interface designs that incorporate human reasoning need to integrate this general characteristic underlying human induction. [ABSTRACT FROM AUTHOR]

Extracting semantic relations is of great importance for the creation of the Semantic Web content. It is of great benefit to semi-automatically extract relations from the free text of Wikipedia using the structured content readily available in it. Pattern matching methods that employ information redundancy cannot work well since there is not much redundancy information in Wikipedia, compared to the Web. Multi-class classification methods are not reasonable since no classification of relation types is available in Wikipedia. In this paper, we propose PORE (Positive-Only Relation Extraction), for relation extraction from Wikipedia text. The core algorithm B-POL extends a state-of-the-art positive-only learning algorithm using bootstrapping, strong negative identifi cation, and transductive inference to work with fewer positive training exam ples. We conducted experiments on several relations with different amount of training data. The experimental results show that B-POL can work effectively given only a small amount of positive training examples and it significantly out per forms the original positive learning approaches and a multi-class SVM. Furthermore, although PORE is applied in the context of Wiki pedia, the core algorithm B-POL is a general approach for Ontology Population and can be adapted to other domains. [ABSTRACT FROM AUTHOR]

"[Reasoner] performance can be scary, so much so, that we cannot deploy the technology in our products." - Michael Shepard. What are typical OWL users to do when their favorite reasoner never seems to return? In this paper, we present our first steps considering this problem. We describe the challenges and our approach, and present a prototype tool to help users identify reasoner performance bottlenecks with respect to their ontologies. We then describe 4 case studies on synthetic and real-world ontologies. While the anecdotal evidence suggests that the service can be useful for both ontology developers and reasoner implementors, much more is desired. [ABSTRACT FROM AUTHOR]

Automatic knowledge reuse for Semantic Web applications imposes several challenges on ontology search. Existing ontology retrieval systems merely return a lengthy list of relevant single ontologies, which may not completely cover the specified user requirements. Therefore, there arises an increasing demand for a tool or algorithm with a mechanism to check concept adequacy of existing ontologies with respect to a user query, and then recommend a single or combination of ontologies which can entirely fulfill the requirements. Thus, this paper develops an algorithm, namely combiSQORE to determine whether the available collection of ontologies is able to completely satisfy a submitted query and return a single or combinative ontology that guarantees query coverage. In addition, it ranks the returned answers based on their conceptual closeness and query coverage. The experimental results show that the proposed algorithm is simple, efficient and effective. [ABSTRACT FROM AUTHOR]

In this paper we describe RDFSync, a methodology for efficient synchronization and merging of RDF models. RDFSync is based on decomposing a model into Minimum Self-Contained graphs (MSGs). After illustrating theory and deriving properties of MSGs, we show how a RDF model can be represented by a list of hashes of such information fragments. The synchronization procedure here described is based on the evaluation and remote comparison of these ordered lists. Experimental results show that the algorithm provides very significant savings on network traffic compared to the fileoriented synchronization of serialized RDF graphs. Finally, we provide the design and report the implementation of a protocol for executing the RDFSync algorithm over HTTP. [ABSTRACT FROM AUTHOR]

Developers of Semantic Web applications face a challenge with respect to the decentralised publication model: where to find statements about encountered resources. The "linked data" approach, which mandates that resource URIs should be de-referenced and yield metadata about the resource, helps but is only a partial solution. We present Sindice, a lookup index over resources crawled on the Semantic Web. Our index allows applications to automatically retrieve sources with information about a given resource. In addition we allow resource retrieval through inverse-functional properties, offer full-text search and index SPARQL endpoints. [ABSTRACT FROM AUTHOR]

Current information retrieval (IR) approaches do not formally capture the explicit meaning of a keyword query but provide a comfortable way for the user to specify information needs on the basis of keywords. Ontology-based approaches allow for sophisticated semantic search but impose a query syntax more difficult to handle. In this paper, we present an approach for translating keyword queries to DL conjunctive queries using background knowledge available in ontologies. We present an implementation which shows that this interpretation of keywords can then be used for both exploration of asserted knowledge and for a semantics-based declarative query answering process. We also present an evaluation of our system and a discussion of the limitations of the approach with respect to our underlying assumptions which directly points to issues for future work. [ABSTRACT FROM AUTHOR]

In different areas ontologies have been developed and many of these ontologies contain overlapping information. Often we would therefore want to be able to use multiple ontologies. To obtain good results, we need to find the relationships between terms in the different ontologies, i.e. we need to align them. Currently, there already exist a number of different alignment strategies. However, it is usually difficult for a user that needs to align two ontologies to decide which of the different available strategies are the most suitable. In this paper we propose a method that provides recommendations on alignment strategies for a given alignment problem. The method is based on the evaluation of the different available alignment strategies on several small selected pieces from the ontologies, and uses the evaluation results to provide recommendations. In the paper we give the basic steps of the method, and then illustrate and discuss the method in the setting of an alignment problem with two well-known biomedical ontologies. We also experiment with different implementations of the steps in the method. [ABSTRACT FROM AUTHOR]

Ontology-based applications play an increasingly important role in the public and corporate Semantic Web. While today there exist a range of tools and technologies to support specific ontology engineering and management activities, architectural design guidelines for building ontology-based applications are missing. In this paper, we present an architecture for ontology-based applications—covering the complete ontology-lifecycle—that is intended to support software engineers in designing and developing ontology-based applications. We illustrate the use of the architecture in a concrete case study using the NeOn toolkit as one implementation of the architecture. [ABSTRACT FROM AUTHOR]

The discovery of suitable Web services for a given task is one of the central operations in Service-oriented Architectures (SOA), and research on Semantic Web services (SWS) aims at automating this step. For the large amount of available Web services that can be expected in real-world settings, the computational costs of automated discovery based on semantic matchmaking become important. To make a discovery engine a reliable software component, we must thus aim at minimizing both the mean and the variance of the duration of the discovery task. For this, we present an extension for discovery engines in SWS environments that exploits structural knowledge and previous discovery results for reducing the search space of consequent discovery operations. Our prototype implementation shows significant improvements when applied to the Stanford SWS Challenge scenario and dataset. [ABSTRACT FROM AUTHOR]

In this paper we address the problem of migrating instances between heterogeneous overlapping ontologies. The instance migration problem arises when one wants to reclassify a set of instances of a source ontology into a semantically related target ontology. Our approach exploits mappings between ontologies, which are used to reconcile both conceptual and individual level heterogeneity, and further used to draw the migration process. We ground the approach on a distributed description logic (DDL), in which ontologies are formally encoded as DL knowledge bases and mappings as bridge rules and individual correspondences. From the theoretical side, we study the task of reasoning with instance data in DDL composed of $\mathcal{SHIQ}$ ontologies and define a correct and complete distributed tableaux inference procedure. From the practical side, we upgrade the DRAGO DDL reasoner for dealing with instances and further show how it can be used to drive the migration of instances between heterogeneous ontologies. [ABSTRACT FROM AUTHOR]

The process of instantiating an ontology with high-quality and up-to-date instance information manually is both time consuming and prone to error. Automatic ontology instantiation from Web sources is one of the possible solutions to this problem and aims at the computer supported population of an ontology through the exploitation of (redundant) information available on the Web. In this paper we present AllRight, a comprehensive ontology instantiating system. In particular, the techniques implemented in AllRight are designed for application scenarios, in which the desired instance information is given in the form of tables and for which existing Information Extraction (IE) approaches based on statistical or natural language processing methods are not directly applicable. Within AllRight, we have therefore developed new techniques for dealing with tabular instance data and combined these techniques with existing methods. The system supports all necessary steps for ontology instantiation, i.e. web crawling, name extraction, document clustering as well as fact extraction and validation. AllRight has been successfully evaluated in the popular domains of digital cameras and notebooks leading to a about eighty percent accuracy of the extracted facts given only a very limited amount of seed knowledge. [ABSTRACT FROM AUTHOR]