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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]

This paper presents a tableau approach for deciding description logics outside the scope of OWL DL/1.1 and current state-of-the-art tableau-based description logic systems. In particular, we define a sound and complete tableau calculus for the description logic $\mathcal{ALBO}$ and show that it provides a basis for decision procedures for this logic and numerous other description logics with full role negation. $\mathcal{ALBO}$ is the extension of $\mathcal{ALC}$ with the Boolean role operators, inverse of roles, domain and range restriction operators and it includes full support for nominals (individuals). $\mathcal{ALBO}$ is a very expressive description logic which subsumes Boolean modal logic and the two-variable fragment of first-order logic and reasoning in it is NExpTime-complete. An important novelty is the use of a generic, unrestricted blocking rule as a replacement for standard loop checking mechanisms implemented in description logic systems. An implementation of our approach exists in the system. [ABSTRACT FROM AUTHOR]

The increased availability of online knowledge has led to the design of several algorithms that solve a variety of tasks by harvesting the Semantic Web, i.e., by dynamically selecting and exploring a multitude of online ontologies. Our hypothesis is that the performance of such novel algorithms implicitly provides an insight into the quality of the used ontologies and thus opens the way to a task-based evaluation of the Semantic Web. We have investigated this hypothesis by studying the lessons learnt about online ontologies when used to solve three tasks: ontology matching, folksonomy enrichment, and word sense disambiguation. Our analysis leads to a suit of conclusions about the status of the Semantic Web, which highlight a number of strengths and weaknesses of the semantic information available online and complement the findings of other analysis of the Semantic Web landscape. [ABSTRACT FROM AUTHOR]

This paper presents a method for making metadata conforming to heterogeneous schemas semantically interoperable. The idea is to make the knowledge embedded in the schema structures interoperable and explicit by transforming the schemas into a shared, event-based representation of knowledge about the real world. This enables and simplifies accurate reasoning services such as cross-domain semantic search, browsing, and recommending. A case study of transforming three different schemas and datasets is presented. An implemented knowledge-based recommender system utilizing the results in the semantic portal CultureSampo was found useful in a preliminary user study. [ABSTRACT FROM AUTHOR]

Ontologies play a core role in the success of the Semantic Web as they provide a shared vocabulary for different resources and applications. Developing an error-free ontology is a difficult task. A common kind of error for an ontology is logical contradiction or incoherence. In this paper, we propose some approaches to measuring incoherence in DL-based ontologies. These measures give an ontology engineer important information for maintaining and evaluating ontologies. We implement the proposed approaches using the KAON2 reasoner and provide some preliminary but encouraging empirical results. [ABSTRACT FROM AUTHOR]

We present a semantic-based approach to multi-issue bilateral negotiation for e-commerce. We use Description Logics to model advertisements, and relations among issues as axioms in a TBox. We then introduce a logic-based alternating-offers protocol, able to handle conflicting information, that merges non-standard reasoning services in Description Logics with utility thoery to find the most suitable agreements. We illustrate and motivate the theoretical framework, the logical language, and the negotiation protocol. [ABSTRACT FROM AUTHOR]

In this paper, we present a new approach to web search personalization based on user collaboration and sharing of information about web documents. The proposed personalization technique separates data collection and user profiling from the information system whose contents and indexed documents are being searched for, i.e. the search engines, and uses social bookmarking and tagging to re-rank web search results. It is independent of the search engine being used, so users are free to choose the one they prefer, even if their favorite search engine does not natively support personalization. We show how to design and implement such a system in practice and investigate its feasibility and usefulness with large sets of real-word data and a user study. [ABSTRACT FROM AUTHOR]

Recently the World Wide Web Consortium (W3C) produced a standard set of "Semantic Annotations for WSDL and XML Schema" (SAWSDL). SAWSDL provides a standard means by which WSDL documents can be related to semantic descriptions, such as those provided by OWL-S (OWL for Services) and other Semantic Web services frameworks. We argue that the value of SAWSDL cannot be realized until its use is specified, and its benefits explained, in connection with a particular framework. This paper is an important first step toward meeting that need, with respect to OWL-S. We explain what OWL-S constructs are appropriate for use with the various SAWSDL annotations, and provide a rationale and guidelines for their use. In addition, we discuss some weaknesses of SAWSDL, and identify some ways in which OWL-S could evolve so as to integrate more smoothly with SAWSDL. [ABSTRACT FROM AUTHOR]

In recent years, CNL (Controlled Natural Language) has received much attention with regard to ontology-based knowledge acquisition systems. CNLs, as subsets of natural languages, can be useful for both humans and computers by eliminating ambiguity of natural languages. Our previous work, OntoPath [10], proposed to edit natural language-like narratives that are structured in RDF (Resource Description Framework) triples, using a domain-specific ontology as their language constituents. However, our previous work and other systems employing CFG for grammar definition have difficulties in enlarging the expression capacity. A newly developed editor, which we propose in this paper, permits grammar definitions through CFG-LD (Context-Free Grammar with Lexical Dependency) that includes sequential and semantic structures of the grammars. With CFG describing the sequential structure of grammar, lexical dependencies between sentence elements can be designated in the definition system. Through the defined grammars, the implemented editor guides users' narratives in more familiar expressions with a domain-specific ontology and translates the content into RDF triples. [ABSTRACT FROM AUTHOR]

We study the continuous evaluation of conjunctive triple pattern queries over RDF data stored in distributed hash tables. In a continuous query scenario network nodes subscribe with long-standing queries and receive answers whenever RDF triples satisfying their queries are published. We present two novel query processing algorithms for this scenario and analyze their properties formally. Our performance goal is to have algorithms that scale to large amounts of RDF data, distribute the storage and query processing load evenly and incur as little network traffic as possible. We discuss the various performance tradeoffs that occur through a detailed experimental evaluation of the proposed algorithms. [ABSTRACT FROM AUTHOR]

This research explores three SPARQL-based techniques to solve Semantic Web tasks that often require similarity measures, such as semantic data integration, ontology mapping, and Semantic Web service matchmaking. Our aim is to see how far it is possible to integrate customized similarity functions (CSF) into SPARQL to achieve good results for these tasks. Our first approach exploits virtual triples calling property functions to establish virtual relations among resources under comparison; the second approach uses extension functions to filter out resources that do not meet the requested similarity criteria; finally, our third technique applies new solution modifiers to post-process a SPARQL solution sequence. The semantics of the three approaches are formally elaborated and discussed. We close the paper with a demonstration of the usefulness of our iSPARQL framework in the context of a data integration and an ontology mapping experiment. [ABSTRACT FROM AUTHOR]

Despite the success of the Web Ontology Language OWL, the development of expressive means for querying OWL knowledge bases is still an open issue. In this paper, we investigate how a very natural and desirable form of queries—namely conjunctive ones—can be used in conjunction with OWL such that one of the major design criteria of the latter—namely decidability—can be retained. More precisely, we show that querying the tractable fragment $\mathcal{EL}^{\mathord{+}\mathord{+}}$ of OWL 1.1 is decidable. We also provide a complexity analysis and show that querying unrestricted $\mathcal{EL}^{\mathord{+}\mathord{+}}$ is undecidable. [ABSTRACT FROM AUTHOR]

Natural language interfaces offer end-users a familiar and convenient option for querying ontology-based knowledge bases. Several studies have shown that they can achieve high retrieval performance as well as domain independence. This paper focuses on usability and investigates if NLIs are useful from an end-user's point of view. To that end, we introduce four interfaces each allowing a different query language and present a usability study benchmarking these interfaces. The results of the study reveal a clear preference for full sentences as query language and confirm that NLIs are useful for querying Semantic Web data. [ABSTRACT FROM AUTHOR]

Finding the justifications of an entailment (that is, all the minimal set of axioms sufficient to produce an entailment) has emerged as a key inference service for the Web Ontology Language (OWL). Justifications are essential for debugging unsatisfiable classes and contradictions. The availability of justifications as explanations of entailments improves the understandability of large and complex ontologies. In this paper, we present several algorithms for computing all the justifications of an entailment in an OWL-DL Ontology and show, by an empirical evaluation, that even a reasoner independent approach works well on real ontologies. [ABSTRACT FROM AUTHOR]

The development of ontologies involves continuous but relatively small modifications. Existing ontology reasoners, however, do not take advantage of the similarities between different versions of an ontology. In this paper, we propose a technique for incremental reasoning—that is, reasoning that reuses information obtained from previous versions of an ontology—based on the notion of a module. Our technique does not depend on a particular reasoning calculus and thus can be used in combination with any reasoner. We have applied our results to incremental classification of OWL DL ontologies and found significant improvement over regular classification time on a set of real-world ontologies. [ABSTRACT FROM AUTHOR]

In this paper we present a solution for "weaving the claim web", i.e. the creation of knowledge networks via so-called claims stated in scientific publications created with the SALT (Semantically Annotated ) framework. To attain this objective, we provide support for claim identification, evolved the appropriate ontologies and defined a claim citation and reference mechanism. We also describe a prototypical claim search engine, which allows to reference to existing claims and hence, weave the web. Finally, we performed a small-scale evaluation of the authoring framework with a quite promising outcome. [ABSTRACT FROM AUTHOR]

We present the architecture of an end-to-end semantic search engine that uses a graph data model to enable interactive query answering over structured and interlinked data collected from many disparate sources on the Web. In particular, we study distributed indexing methods for graph-structured data and parallel query evaluation methods on a cluster of computers. We evaluate the system on a dataset with 430 million statements collected from the Web, and provide scale-up experiments on 7 billion synthetically generated statements. [ABSTRACT FROM AUTHOR]

OBO is an ontology language that has often been used for modeling ontologies in the life sciences. Its definition is relatively informal, so, in this paper, we provide a clear specification for OBO syntax and semantics via a mapping to OWL. This mapping also allows us to apply existing Semantic Web tools and techniques to OBO. We show that Semantic Web reasoners can be used to efficiently reason with OBO ontologies. Furthermore, we show that grounding the OBO language in formal semantics is useful for the ontology development process: using an OWL reasoner, we detected a likely modeling error in one OBO ontology. [ABSTRACT FROM AUTHOR]

This paper presents a controlled language for ontology editing and a software implementation, based partly on standard NLP tools, for processing that language and manipulating an ontology. The input sentences are analysed deterministically and compositionally with respect to a given ontology, which the software consults in order to interpret the input's semantics; this allows the user to learn fewer syntactic structures since some of them can be used to refer to either classes or instances, for example. A repeated-measures, task-based evaluation has been carried out in comparison with a well-known ontology editor; our software received favourable results for basic tasks. The paper also discusses work in progress and future plans for developing this language and tool. [ABSTRACT FROM AUTHOR]

Wikipedia, a killer application in Web 2.0, has embraced the power of collaborative editing to harness collective intelligence. It can also serve as an ideal Semantic Web data source due to its abundance, influence, high quality and well-structuring. However, the heavy burden of up-building and maintaining such an enormous and ever-growing online encyclopedic knowledge base still rests on a very small group of people. Many casual users may still feel difficulties in writing high quality Wikipedia articles. In this paper, we use RDF graphs to model the key elements in Wikipedia authoring, and propose an integrated solution to make Wikipedia authoring easier based on RDF graph matching, expecting making more Wikipedians. Our solution facilitates semantics reuse and provides users with: 1) a link suggestion module that suggests and auto-completes internal links between Wikipedia articles for the user; 2) a category suggestion module that helps the user place her articles in correct categories. A prototype system is implemented and experimental results show significant improvements over existing solutions to link and category suggestion tasks. The proposed enhancements can be applied to attract more contributors and relieve the burden of professional editors, thus enhancing the current Wikipedia to make it an even better Semantic Web data source. [ABSTRACT FROM AUTHOR]

We present a simple method to extract information from search engine snippets. Although the techniques presented are domain independent, this work focuses on extracting biographical information of historical persons from multiple unstructured sources on the Web. We first similarly find a list of persons and their periods of life by querying the periods and scanning the retrieved snippets for person names. Subsequently, we find biographical information for the persons extracted. In order to get insight in the mutual relations among the persons identified, we create a social network using co-occurrences on the Web. Although we use uncontrolled and unstructured Web sources, the information extracted is reliable. Moreover we show that Web Information Extraction can be used to create both informative and enjoyable applications. [ABSTRACT FROM AUTHOR]

Ontology mapping is the key to data interoperability in the semantic web. This problem has received a lot of research attention, however, the research emphasis has been mostly devoted to automating the mapping process, even though the creation of mappings often involve the user. As industry interest in semantic web technologies grows and the number of widely adopted semantic web applications increases, we must begin to support the user. In this paper, we combine data gathered from background literature, theories of cognitive support and decision making, and an observational case study to propose a theoretical framework for cognitive support in ontology mapping tools. We also describe a tool called CogZ that is based on this framework. [ABSTRACT FROM AUTHOR]

An important open question in the semantic Web is the precise relationship between the RDF(S) semantics and the semantics of standard knowledge representation formalisms such as logic programming and description logics. In this paper we address this issue by considering embeddings of RDF and RDFS in logic. Using these embeddings, combined with existing results about various fragments of logic, we establish several novel complexity results. The embeddings we consider show how techniques from deductive databases and description logics can be used for reasoning with RDF(S). Finally, we consider querying RDF graphs and establish the data complexity of conjunctive querying for the various RDF entailment regimes. [ABSTRACT FROM AUTHOR]

Design patterns are widely-used software engineering abstractions which define guidelines for modeling common application scenarios. Ontology design patterns are the extension of software patterns for knowledge acquisition in the Semantic Web. In this work we present a design pattern for representing relevance depending on context in OWL ontologies, i.e. to assert which knowledge from the domain ought to be considered in a given scenario. Besides the formal semantics and the features of the pattern, we describe a reasoning procedure to extract relevant knowledge in the resulting ontology and a plug-in for Protégé which assists pattern use. [ABSTRACT FROM AUTHOR]

The approach of using ontology reasoning to cleanse the output of information extraction tools was first articulated in SemantiClean. A limiting factor in applying this approach has been that ontology reasoning to find inconsistencies does not scale to the size of data produced by information extraction tools. In this paper, we describe techniques to scale inconsistency detection, and illustrate the use of our techniques to produce a consistent subset of a knowledge base with several thousand inconsistencies. [ABSTRACT FROM AUTHOR]

The amount of ontologies and meta data available on the Web is constantly growing. The successful application of machine learning techniques for learning of ontologies from textual data, i.e. mining for the Semantic Web, contributes to this trend. However, no principal approaches exist so far for mining from the Semantic Web. We investigate how machine learning algorithms can be made amenable for directly taking advantage of the rich knowledge expressed in ontologies and associated instance data. Kernel methods have been successfully employed in various learning tasks and provide a clean framework for interfacing between non-vectorial data and machine learning algorithms. In this spirit, we express the problem of mining instances in ontologies as the problem of defining valid corresponding kernels. We present a principled framework for designing such kernels by means of decomposing the kernel computation into specialized kernels for selected characteristics of an ontology which can be flexibly assembled and tuned. Initial experiments on real world Semantic Web data enjoy promising results and show the usefulness of our approach. [ABSTRACT FROM AUTHOR]

In open and distributed environments ontology mapping provides interoperability between interacting actors. However, conventional mapping systems focus on acquiring static information, and on mapping whole ontologies, which is infeasible in open systems. This paper shows that the interactions themselves between the actors can be used to predict mappings, simplifying dynamic ontology mapping. The intuitive idea is that similar interactions follow similar conventions and patterns, which can be analysed. The computed model can be used to suggest the possible mappings for the exchanged messages in new interactions. The suggestions can be evaluate by any standard ontology matcher: if they are accurate, the matchers avoid evaluating mappings unrelated to the interaction. The minimal requirement in order to use this system is that it is possible to describe and identify the interaction sequences: the OpenKnowledge project has produced an implementation that demonstrates this is possible in a fully peer-to-peer environment. [ABSTRACT FROM AUTHOR]

Many scientific problems can be represented as computational workflows of operations that access remote data, integrate heterogeneous data, and analyze and derive new data. Even when the data access and processing operations are implemented as web or grid services, workflows are often constructed manually in languages such as BPEL. Adding semantic descriptions of the services enables automatic or mixed-initiative composition. In most previous work, these descriptions consists of semantic types for inputs and outputs of services or a type for the service as a whole. While this is certainly useful, we argue that is not enough to model and construct complex data workflows. We present a planning approach to automatically constructing data processing workflows where the inputs and outputs of services are relational descriptions in an expressive logic. Our workflow planner uses relational subsumption to connect the output of a service with the input of another. This modeling style has the advantage that adaptor services, so-called shims, can be automatically inserted into the workflow where necessary. [ABSTRACT FROM AUTHOR]

Semantic descriptions of non-textual media available on the web can be used to facilitate retrieval and presentation of media assets and documents containing them. While technologies for multimedia semantic descriptions already exist, there is as yet no formal description of a high quality multimedia ontology that is compatible with existing (semantic) web technologies. We explain the complexity of the problem using an annotation scenario. We then derive a number of requirements for specifying a formal multimedia ontology before we present the developed ontology, COMM, and evaluate it with respect to our requirements. We provide an API for generating multimedia annotations that conform to COMM. [ABSTRACT FROM AUTHOR]

Semantic Web databases allow efficient storage and access to RDF statements. Applications are able to use expressive query languages in order to retrieve relevant metadata to perform different tasks. However, access to metadata may not be public to just any application or service. Instead, powerful and flexible mechanisms for protecting sets of RDF statements are required for many Semantic Web applications. Unfortunately, current RDF stores do not provide fine-grained protection. This paper fills this gap and presents a mechanism by which complex and expressive policies can be specified in order to protect access to metadata in multi-service environments. [ABSTRACT FROM AUTHOR]