The Semantic Web: Research and Applications

Semantic search seems to be an elusive and fuzzy target to IR, SW and NLP researchers. One reason is that this challenge lies in between all those fields, which implies a broad scope of issues and technologies that must be mastered. In this extended abstract we survey the work of Yahoo! Research at Barcelona to approach this problem. Our research is intended to produce a virtuous feedback circuit by using machine learning for capturing semantics, and, ultimately, for better search.

The motivation behind the development of RDF was, to borrow the words Tim Berners-Lee used for the Semantic Web, “to have a common and minimal language to enable to map large quantities of existing data onto it so that the data can be analyzed in ways never dreamed of by its creators.” To bring to reality this vision, the processing of RDF data at big scale must be viable. This challenge amounts essentially to develop the theory and practice of RDF databases.

In under a decade the internet has changed our lives. Now we can shop, bank, date, research, learn and communicate online and every time we do we leave behind a trail of personal information. Organisations have a wealth of structured information about individuals on large numbers of databases. What does the intersection of this information mean for the individual? How much of your personal data is out there and more importantly, just who has access to it? As stories of identity theft and online fraud fill the media internet users are becoming increasingly nervous about their online data security. Also what opportunities arise for individuals to exploit this information for their own benefit?

One relevant aspect in the development of the Semantic Web framework is the achievement of a real inter-agents communication capability at the semantic level. The agents should be able to communicate and understand each other using standard communication protocols freely, that is, without needing a laborious a priori preparation, before the communication takes place.

For that setting we present in this paper a proposal that promotes to describe standard communication protocols using Semantic Web technology (specifically, OWL-DL and SWRL). Those protocols are constituted by communication acts. In our proposal those communication acts are described as terms that belong to a communication acts ontology, that we have developed, called

CommOnt

. The intended semantics associated to the communication acts in the ontology is expressed through social commitments that are formalized as fluents in the Event Calculus.

In summary, OWL-DL reasoners and rule engines help in our proposal for reasoning about protocols. We define some comparison relationships (dealing with notions of equivalence and specialization) between protocols used by agents from different systems.

Instant Messaging (IM) is in addition to Web and Email the most popular service on the Internet. With xOperator we present a strategy and implementation which deeply integrates Instant Messaging networks with the Semantic Web. The xOperator concept is based on the idea of creating an overlay network of collaborative information agents on top of social IM networks. It can be queried using a controlled and easily extensible language based on AIML templates. Such a deep integration of semantic technologies and Instant Messaging bears a number of advantages and benefits for users when compared to the separated use of Semantic Web technologies and IM, the most important ones being context awareness as well as provenance and trust. We showcase how the xOperator approach naturally facilitates contacts and calendar management as well as access to large scale heterogeneous information sources.

Ontology-Driven Software Development (ODSD) advocates using ontologies for capturing knowledge about a software system at development time. So far, ODSD approaches have mainly focused on the unambiguous representation of domain models during the system analysis phase. However, the design and implementation phases can equally benefit from the logical foundations and reasoning facilities provided by the Ontology technological space. This applies in particular to Model-Driven Software Development (MDSD) which employs models as first class entities throughout the entire software development process. We are currently developing a tool suite called HybridMDSD that leverages Semantic Web technologies to integrate different domain-specific modeling languages based on their ontological foundations. To this end, we have defined a new upper ontology for software models that complements existing work in conceptual and business modeling. This paper describes the structure and axiomatization of our ontology and its underlying conceptualization. Further, we report on the experiences gained with validating the integrity and consistency of software models using a Semantic Web reasoning architecture. We illustrate practical solutions to the implementation challenges arising from the open-world assumption in OWL and lack of nonmonotonic queries in SWRL.

Business Process Management (BPM) aims at supporting the whole life-cycle necessary to deploy and maintain business processes in organisations. An important step of the BPM life-cycle is the analysis of the processes deployed in companies. However, the degree of automation currently achieved cannot support the level of adaptation required by businesses. Initial steps have been performed towards including some sort of automated reasoning within Business Process Analysis (BPA) but this is typically limited to using taxonomies. We present a core ontology aimed at enhancing the state of the art in BPA. The ontology builds upon a Time Ontology and is structured around the process, resource, and object perspectives as typically adopted when analysing business processes. The ontology has been extended and validated by means of an Events Ontology and an Events Analysis Ontology aimed at capturing the audit trails generated by Process-Aware Information Systems and deriving additional knowledge.

Participants at both end of the communication channel must share common pictogram interpretation to communicate. However, because pictogram interpretation can be ambiguous, pictogram communication can sometimes be difficult. To assist human task of selecting pictograms more likely to be interpreted as intended, we propose a

semantic relevance measure

which calculates how relevant a pictogram is to a given interpretation. The proposed measure uses pictogram interpretations and frequencies gathered from a web survey to define probability and similarity measurement of interpretation words. Moreover, the proposed measure is applied to categorized pictogram interpretations to enhance retrieval performance. Five pictogram categories are created using the five first level categories defined in the Concept Dictionary of EDR Electronic Dictionary. Retrieval performance among not-categorized interpretations, categorized and not-weighted interpretations, and categorized and weighted interpretations using semantic relevance measure were compared, and the categorized and weighted semantic relevance retrieval approach exhibited the highest

F

1

measure and recall.

Dissemination, an important phase of scientific research, can be seen as a communication process between scientists. They expose and support their findings, while discussing claims stated in related scientific publications. However, due to the increasing number of publications, finding a starting point for such a discussion represents a real challenge. At same time, browsing can also be difficult since the communication spans accross multiple publications on the open Web. In this paper we propose a semantic claim federation infrastructure, named KonneX

SALT

, as a solution for both issues mentioned above: ()finding claims in scientific publications, and() providing support for browsing by starting with a claim and then following the links in an argumentation discourse network (ADN) (in our case, by making use of transclusion).In addition, we join the web of linked open data, by linking the metadata contained in KonneX

SALT

with some of the known repositories of scientific publications.

This paper presents the vision and results of creating a national level cross-domain ontology and ontology service infrastructure in Finland. The novelty of the infrastructure is based on two ideas. First, a system of open source core ontologies is being developed by transforming thesauri into mutually aligned lightweight ontologies, including a large top ontology that is extended by various domain specific ontologies. Second, the

ONKI

Ontology Server framework for publishing ontologies as ready to use services has been designed and implemented.

ONKI

provides legacy and other applications with ready to use functionalities for using ontologies on the HTML level by Ajax and semantic widgets. The idea is to use

ONKI

for creating mash-up applications in a way analogous to using Google or Yahoo Maps, but in our case external applications are mashed-up with ontology support.

Content annotations in semantic cultural heritage portals commonly make spatiotemporal references to historical regions and places using names whose meanings are different in different times. For example, historical administrational regions such as countries, municipalities, and cities have been renamed, merged together, split into parts, and annexed or moved to and from other regions. Even if the names of the regions remain the same (e.g., “Germany”), the underlying regions and their relationships to other regions may change (e.g., the regional borders of “Germany” at different times). As a result, representing and finding the right ontological meanings for historical geographical names on the semantic web creates severe problems both when annotating contents and during information retrieval. This paper presents a model for representing the meaning of changing geospatial resources. Our aim is to enable precise annotation with temporal geospatial resources and to enable semantic search and browsing using related names from other historical time periods. A simple model and metadata schema is presented for representing and maintaining geospatial changes from which an explicit time series of temporal part-of ontologies can be created automatically. The model has been applied successfully to represent the complete change history of municipalities in Finland during 1865–2007. The resulting ontology time series is used in the semantic cultural heritage portal

CultureSampo

to support faceted semantic search of contents and to visualize historical regions on overlaying maps originating from different historical eras.

In this paper, we introduce a formal email workflow model based on traditional email, which enables the user to define and execute ad-hoc workflows in an intuitive way. This model paves the way for semantic annotation of implicit, well-defined workflows, thus making them explicit and exposing the missing information in a machine processable way. Grounding this work within the Social Semantic Desktop [1] via appropriate ontologies means that this information can be exploited for the benefit of the user. This will have a direct impact on their personal information management - given email is not just a major channel of data exchange between desktops, but it also serves as a virtual working environment where people collaborate. Thus the presented workflow model will have a concrete manifestation in the creation, organization and exchange of semantic desktop data.

Knowledge work in many fields requires examining several aspects of a collection of documents to attain meaningful understanding that is not explicitly available. Despite recent advances in document corpus visualization research, there is still a lack of principled approaches which enable the users to personalize the exploratory analysis process. In this paper, we present IVEA (

I

nformation

V

isualization for

E

xploratory Document Collection

A

nalysis), an innovative visualization tool which employs the PIMO (Personal Information Model) ontology to provide the knowledge workers with an interactive interface allowing them to browse for information in a personalized manner. Not only does the tool allow the users to integrate their interests into the exploration and analysis of a document collection, it also enables them to incrementally enrich their PIMO ontologies with new entities matching their evolving interests in the process, and thus benefiting the users not only in their future experiences with IVEA but also with other PIMO-based applications. The usability of the tool was preliminarily evaluated and the results were sufficiently encouraging to make it worthwhile to conduct a larger-scale usability study.

Images play a vital role in scientific studies. An image repository would become a costly and meaningless data graveyard without descriptive metadata. We adapted EPrints, a conventional repository software system, to create a biological research image repository for a local research group, in order to publish images with structured metadata with a minimum of development effort. However, in its native installation, this repository cannot easily be linked with information from third parties, and the user interface has limited flexibility. We address these two limitations by providing Semantic Web access to the contents of this image repository, causing the image metadata to become programmatically accessible through a SPARQL endpoint and enabling the images and their metadata to be presented in more flexible faceted browsers, jSpace and Exhibit. We show the feasibility of publishing image metadata on the Semantic Web using existing tools, and examine the inadequacies of the Semantic Web browsers in providing effective user interfaces. We highlight the importance of a loosely coupled software framework that provides a lightweight solution and enables us to switch between alternative components.

In the semantic web environment, where several independent ontologies are used in order to describe knowledge and data, ontologies have to be aligned by defining mappings among the elements of one ontology and the elements of another ontology. Very often, mappings are not derived from the semantics of the ontologies that are compared. Rather, mappings are computed by evaluating the similarity of the ontologies terminology and/or of their syntactic structure. In this paper, we propose a new mapping validation approach. The approach is based on the notion of probabilistic mappings and on the use of probabilistic reasoning techniques to enforce a semantic interpretation of similarity mappings as probabilistic and hypothetical relations among ontology elements.

Driven by application requirements and using well-understood theoretical results, we describe a novel methodology and a tool for modular ontology design. We support the user in the

safe

use of imported symbols and in the

economic

import of the relevant part of the imported ontology. Both features are supported in a well-understood way: safety guarantees that the semantics of imported concepts is not changed, and economic import guarantees that no difference can be observed between importing the whole ontology and importing the relevant part.

We introduce domain-restricted RDF (dRDF) which allows to associate an RDF graph with a fixed, finite domain that interpretations for it may range over. We show that dRDF is a real extension of RDF and discuss impacts on the complexity of entailment in dRDF. The entailment problem represents the key reasoning task for RDF and is well known to be NP-complete. Remarkably, we show that the restriction of domains in dRDF raises the complexity of entailment from NP- to

${\Pi^P_2}$

-completeness. In order to lower complexity of entailment for both domain-restricted and unrestricted graphs, we take a closer look at the graph structure. For cases where the structure of RDF graphs is restricted via the concept of bounded treewidth, we prove that the entailment is tractable for unrestricted graphs and coNP-complete for domain-restricted graphs.

The semantics of OWL-DL and its subclasses are based on the classical semantics of first-order logic, in which the interpretation domain may be an infinite set. This constitutes a serious expressive limitation for such ontology languages, since, in many real application scenarios for the Semantic Web, the domain of interest is actually finite, although the exact cardinality of the domain is unknown. Hence, in these cases the formal semantics of the OWL-DL ontology does not coincide with its intended semantics. In this paper we start filling this gap, by considering the subclasses of OWL-DL which correspond to the logics of the

DL-Lite

family, and studying reasoning over finite models in such logics. In particular, we mainly consider two reasoning problems: deciding satisfiability of an ontology, and answering unions of conjunctive queries (UCQs) over an ontology. We first consider the description logic

${\textit{DL-Lite}_R}$

and show that, for the two above mentioned problems, finite model reasoning coincides with classical reasoning, i.e., reasoning over arbitrary, unrestricted models. Then, we analyze the description logics

${\textit{DL-Lite}_F}$

and

${\textit{DL-Lite}_A}$

. Differently from

${\textit{DL-Lite}_R}$

, in such logics finite model reasoning does not coincide with classical reasoning. To solve satisfiability and query answering over finite models in these logics, we define techniques which reduce polynomially both the above reasoning problems over finite models to the corresponding problem over arbitrary models. Thus, for all the

DL-Lite

languages considered, the good computational properties of satisfiability and query answering under the classical semantics also hold under the finite model semantics. Moreover, we have effectively and easily implemented the above techniques, extending the

DL-Lite

reasoner QuOnto with support for finite model reasoning.

The description logic

$\ensuremath{\mathcal{EL}^+}$

has recently proved practically useful in the life science domain with presence of several large-scale biomedical ontologies such as

Snomed ct

. To deal with ontologies of this scale, standard reasoning of classification is essential but not sufficient. The ability to extract relevant fragments from a large ontology and to incrementally classify it has become more crucial to support ontology design, maintenance and re-use. In this paper, we propose a pragmatic approach to module extraction and incremental classification for

$\ensuremath{\mathcal{EL}^+}$

ontologies and report on empirical evaluations of our algorithms which have been implemented as an extension of the

CEL

reasoner.

To support the reuse and combination of ontologies in Semantic Web applications, it is often necessary to obtain smaller ontologies from existing larger ontologies. In particular, applications may require the omission of many terms, e.g., concept names and role names, from an ontology. However, the task of omitting terms from an ontology is challenging because the omission of some terms may affect the relationships between the remaining terms in complex ways. We present the first solution to this problem by adapting the technique of forgetting, previously used in other domains. Specifically, we present a semantic definition of forgetting for description logics in general, which generalizes the standard definition for classical logic. We then introduce algorithms that implement forgetting in both DL-Lite TBoxes and ABoxes, and in DL-Lite knowledge bases. We prove that the algorithms are correct with respect to the semantic definition of forgetting, and that they run in polynomial time.

In this paper, we argue that implementing the grand vision of the Semantic Web would greatly benefit from a service which can enable the reuse of globally unique URIs across semantic datasets produced in a fully decentralized and open environment. Such a service, which we call

Entity Name System

(ENS), stores pre–existing URIs and makes them available for reuse mainly – but not only – in Semantic Web contents and applications. The ENS will make the integration of semantic datasets much easier and faster, and will foster the development of a whole family of applications which will exploit the data level integration through global URIs for implementing smart semantic-based solutions.

A layered architecture for the Semantic Web that adheres to software engineering principles and the fundamental aspects of layered architectures will assist in the development of Semantic Web specifications and applications. The most well-known versions of the layered architecture that exist within literature have been proposed by Berners-Lee. It is possible to indicate inconsistencies and discrepancies in the different versions of the architecture, leading to confusion, as well as conflicting proposals and adoptions by the Semantic Web community. A more recent version of a Semantic Web layered architecture, namely the CFL architecture, was proposed in 2007 by Gerber, van der Merwe and Barnard [23], which adheres to software engineering principles and addresses several of the concerns evident from previous versions of the architecture. In this paper we evaluate this recent architecture, both by scrutinising the shortcomings of previous architectures and evaluating the approach used for the development of the latest architecture. Furthermore, the architecture is applied to usage scenarios to evaluate the usefulness thereof.

In order to overcome the limitations of deductive logic-based approaches to deriving operational knowledge from ontologies, especially when data come from distributed sources, inductive (instance-based) methods may be better suited, since they are usually efficient and noise-tolerant. In this paper we propose an inductive method for improving the instance retrieval and enriching the ontology population. By casting retrieval as a classification problem with the goal of assessing the individual class-memberships w.r.t. the query concepts, we propose an extension of the

k-Nearest Neighbor

algorithm for OWL ontologies based on an

entropic

distance measure. The procedure can classify the individuals w.r.t. the known concepts but it can also be used to retrieve individuals belonging to query concepts. Experimentally we show that the behavior of the classifier is comparable with the one of a standard reasoner. Moreover we show that new knowledge (not logically derivable) is induced. It can be suggested to the knowledge engineer for validation, during the ontology population task.

The original Semantic Web vision was explicit in the need for intelligent autonomous agents that would represent users and help them navigate the Semantic Web. We argue that an essential feature for such agents is the capability to analyse data and learn. In this paper we outline the challenges and issues surrounding the application of clustering algorithms to Semantic Web data. We present several ways to extract instances from a large RDF graph and computing the distance between these. We evaluate our approaches on three different data-sets, one representing a typical relational database to RDF conversion, one based on data from a ontologically rich Semantic Web enabled application, and one consisting of a crawl of FOAF documents; applying both supervised and unsupervised evaluation metrics. Our evaluation did not support choosing a single combination of instance extraction method and similarity metric as superior in all cases, and as expected the behaviour depends greatly on the data being clustered. Instead, we attempt to identify characteristics of data that make particular methods more suitable.

The paper presents a clustering method which can be applied to populated ontologies for discovering interesting groupings of resources therein. The method exploits a simple, yet effective and language-independent, semi-distance measure for individuals, that is based on their underlying semantics along with a number of dimensions corresponding to a set of concept descriptions (discriminating features committee). The clustering algorithm is a partitional method and it is based on the notion of medoids w.r.t. the adopted semi-distance measure. Eventually, it produces a hierarchical organization of groups of individuals. A final experiment demonstrates the validity of the approach using absolute quality indices. We propose two possible exploitations of these clusterings: concept formation and detecting concept drift or novelty.

Organizations working in a multilingual environment demand multilingual ontologies. To solve this problem we propose LabelTranslator, a system that automatically localizes ontologies. Ontology localization consists of adapting an ontology to a concrete language and cultural community.

LabelTranslator takes as input an ontology whose labels are described in a source natural language and obtains the most probable translation into a target natural language of each ontology label. Our main contribution is the automatization of this process which reduces human efforts to localize an ontology manually. First, our system uses a translation service which obtains automatic translations of each ontology label (name of an ontology term) from/into English, German, or Spanish by consulting different linguistic resources such as lexical databases, bilingual dictionaries, and terminologies. Second, a ranking method is used to sort each ontology label according to similarity with its lexical and semantic context.

The experiments performed in order to evaluate the quality of translation show that our approach is a good approximation to automatically enrich an ontology with multilingual information.

The mathematical nature of description logics has meant that domain experts find them hard to understand. This forms a significant impediment to the creation and adoption of ontologies. This paper describes Rabbit, a Controlled Natural Language that can be translated into OWL with the aim of achieving both comprehension by domain experts and computational preciseness. We see Rabbit as complementary to OWL, extending its reach to those who need to author and understand domain ontologies but for whom descriptions logics are difficult to comprehend even when expressed in more user-friendly forms such as the Manchester Syntax. The paper outlines the main grammatical aspects of Rabbit, which can be broadly classified into declarations, concept descriptions and definitions, and elements to support interoperability between ontologies. The paper also describes the human subject testing that has been performed to date and indicates the changes currently being made to the language following this testing. Further modifications have been based on practical experience of the application of Rabbit for the development of operational ontologies in the domain of topography.

Accessing structured data such as that encoded in ontologies and knowledge bases can be done using either syntactically complex formal query languages like SPARQL or complicated form interfaces that require expensive customisation to each particular application domain. This paper presents the QuestIO system – a natural language interface for accessing structured information, that is domain independent and easy to use without training. It aims to bring the simplicity of Google’s search interface to conceptual retrieval by automatically converting short conceptual queries into formal ones, which can then be executed against any semantic repository.

QuestIO was developed specifically to be robust with regard to language ambiguities, incomplete or syntactically ill-formed queries, by harnessing the structure of ontologies, fuzzy string matching, and ontology-motivated similarity metrics.

This paper presents an automatic method for differentiating between instances and classes in a large scale taxonomy induced from the Wikipedia category network. The method exploits characteristics of the category names and the structure of the network. The approach we present is the first attempt to make this distinction automatically in a large scale resource. In contrast, this distinction has been made in WordNet and Cyc based on manual annotations. The result of the process is evaluated against ResearchCyc. On the subnetwork shared by our taxonomy and ResearchCyc we report 84.52% accuracy.

Evaluation of ontology alignments is in practice done in two ways: (1) assessing individual correspondences and (2) comparing the alignment to a reference alignment. However, this type of evaluation does not guarantee that an application which uses the alignment will perform well. In this paper, we contribute to the current ontology alignment evaluation practices by proposing two alternative evaluation methods that take into account some characteristics of a usage scenario without doing a full-fledged end-to-end evaluation. We compare different evaluation approaches in three case studies, focussing on methodological issues. Each case study considers an alignment between a different pair of ontologies, ranging from rich and well-structured to small and poorly structured. This enables us to conclude on the use of different evaluation approaches in different settings.

Thesaurus alignment plays an important role in realising efficient access to heterogeneous Cultural Heritage data. Current ontology alignment techniques, however, provide only limited value for such access as they consider little if any requirements from realistic use cases or application scenarios. In this paper, we focus on two real-world scenarios in a library context: thesaurus merging and book re-indexing. We identify their particular requirements and describe our approach of deploying and evaluating thesaurus alignment techniques in this context. We have applied our approach for the Ontology Alignment Evaluation Initiative, and report on the performance evaluation of participants’ tools wrt. the application scenario at hand. It shows that evaluations of tools requires significant effort, but when done carefully, brings many benefits.

For the effective alignment of ontologies, the computation of equivalence relations between elements of ontologies is not enough: Subsumption relations play a crucial role as well. In this paper we propose the "Classification-Based Learning of Subsumption Relations for the Alignment of Ontologies" (

CSR

) method. Given a pair of concepts from two ontologies, the objective of

CSR

is to identify patterns of concepts’ features that provide evidence for the subsumption relation among them. This is achieved by means of a classification task, using state of the art supervised machine learning methods. The paper describes thoroughly the method, provides experimental results over an extended version of benchmarking series and discusses the potential of the method.

With currently available tools and languages, translating between an existing XML format and RDF is a tedious and error-prone task. The importance of this problem is acknowledged by the W3C GRDDL working group who faces the issue of extracting RDF data out of existing HTML or XML files, as well as by the Web service community around SAWSDL, who need to perform lowering and lifting between RDF data from a semantic client and XML messages for a Web service. However, at the moment, both these groups rely solely on XSLT transformations between RDF/XML and the respective other XML format at hand. In this paper, we propose a more natural approach for such transformations based on merging XQuery and SPARQL into the novel language XSPARQL.We demonstrate that XSPARQL provides concise and intuitive solutions for mapping between XML and RDF in either direction, addressing both the use cases of GRDDL and SAWSDL. We also provide and describe an initial implementation of an XSPARQL engine, available for user evaluation.

A lot of work has been done in the area of data stream processing. Most of the previous approaches regard only relational or XML based streams but do not cover semantically richer RDF based stream elements. In our work, we extend SPARQL, the W3C recommendation for an RDF query language, to process RDF data streams. To describe the semantics of our enhancement, we extended the logical SPARQL algebra for stream processing on the foundation of a temporal relational algebra based on multi-sets and provide an algorithm to transform SPARQL queries to the new extended algebra. For each logical algebra operator, we define executable physical counterparts. To show the feasibility of our approach, we implemented it within our

Odysseus

framework in the context of wind power plant monitoring.

This research explores a new method for Semantic Web service matchmaking based on iSPARQL strategies, which enables to query the Semantic Web with techniques from traditional information retrieval. The strategies for matchmaking that we developed and evaluated can make use of a plethora of similarity measures and combination functions from SimPack—our library of similarity measures. We show how our combination of structured and imprecise querying can be used to perform hybrid Semantic Web service matchmaking. We analyze our approach thoroughly on a large OWL-S service test collection and show how our initial strategies can be improved by applying machine learning algorithms to result in very effective strategies for matchmaking.

Exploiting the complex structure of relational data enables to build better models by taking into account the additional information provided by the links between objects. We extend this idea to the Semantic Web by introducing our novel SPARQL-ML approach to perform data mining for Semantic Web data. Our approach is based on traditional SPARQL and statistical relational learning methods, such as Relational Probability Trees and Relational Bayesian Classifiers.

We analyze our approach thoroughly conducting three sets of experiments on synthetic as well as real-world data sets. Our analytical results show that our approach can be used for any Semantic Web data set to perform instance-based learning and classification. A comparison to kernel methods used in Support Vector Machines shows that our approach is superior in terms of classification accuracy.

In this contribution a system is presented, which provides access to distributed data sources using Semantic Web technology. While it was primarily designed for data sharing and scientific collaboration, it is regarded as a base technology useful for many other Semantic Web applications. The proposed system allows to retrieve data using SPARQL queries, data sources can register and abandon freely, and all RDF Schema or OWL vocabularies can be used to describe their data, as long as they are accessible on the Web. Data heterogeneity is addressed by RDF-wrappers like D2R-Server placed on top of local information systems. A query does not directly refer to actual endpoints, instead it contains graph patterns adhering to a virtual data set. A mediator finally pulls and joins RDF data from different endpoints providing a transparent on-the-fly view to the end-user.

The SPARQL protocol has been defined to enable systematic data access to remote endpoints. However, remote SPARQL queries require the explicit notion of endpoint URIs. The presented system allows users to execute queries without the need to specify target endpoints. Additionally, it is possible to execute join and union operations across different remote endpoints. The optimization of such distributed operations is a key factor concerning the performance of the overall system. Therefore, proven concepts from database research can be applied.

A fundamental problem related to graph structured databases is searching for substructures. One issue with respect to optimizing such searches is the ability to estimate the frequency of substructures within a query graph. In this work, we present and evaluate two techniques for estimating the frequency of subgraphs from a summary of the data graph. In the first technique, we assume that edge occurrences on edge sequences are position independent and summarize only the most informative dependencies. In the second technique, we prune small subgraphs using a valuation scheme that blends information about their importance and estimation power. In both techniques, we assume conditional independence to estimate the frequencies of larger subgraphs. We validate the effectiveness of our techniques through experiments on real and synthetic datasets.

Integrated access to multiple distributed and autonomous RDF data sources is a key challenge for many semantic web applications. As a reaction to this challenge, SPARQL, the W3C Recommendation for an RDF query language, supports querying of multiple RDF graphs. However, the current standard does not provide transparent query federation, which makes query formulation hard and lengthy. Furthermore, current implementations of SPARQL load all RDF graphs mentioned in a query to the local machine. This usually incurs a large overhead in network traffic, and sometimes is simply impossible for technical or legal reasons. To overcome these problems we present DARQ, an engine for federated SPARQL queries. DARQ provides transparent query access to multiple SPARQL services, i.e., it gives the user the impression to query one single RDF graph despite the real data being distributed on the web. A service description language enables the query engine to decompose a query into sub-queries, each of which can be answered by an individual service. DARQ also uses query rewriting and cost-based query optimization to speed-up query execution. Experiments show that these optimizations significantly improve query performance even when only a very limited amount of statistical information is available. DARQ is available under GPL License at

http://darq.sf.net/

.

In semantic web applications where query initiators and information providers do not necessarily share the same ontology, semantic interoperability generally relies on ontology matching or schema mappings. Information exchange is then not only enabled by the established correspondences (the “shared” parts of the ontologies) but, in some sense, limited to them. Then, how the “unshared” parts can also contribute to and improve information exchange ? In this paper, we address this question by considering a system where documents and queries are represented by semantic vectors. We propose a specific query expansion step at the query initiator’s side and a query interpretation step at the document provider’s. Through these steps, unshared concepts contribute to evaluate the relevance of documents wrt. a given query. Our experiments show an important improvement of retrieval relevance when concepts of documents and queries are not shared. Even if the concepts of the initial query are not shared by the document provider, our method still ensures 90% of the precision and recall obtained when the concepts are shared.

This paper describes hybrid search, a search method supporting both document and knowledge retrieval via the flexible combination of ontology-based search and keyword-based matching. Hybrid search smoothly copes with lack of semantic coverage of document content, which is one of the main limitations of current semantic search methods. In this paper we define hybrid search formally, discuss its compatibility with the current semantic trends and present a reference implementation: K-Search. We then show how the method outperforms both keyword-based search and pure semantic search in terms of precision and recall in a set of experiments performed on a collection of about 18.000 technical documents. Experiments carried out with professional users show that users understand the paradigm and consider it very powerful and reliable. K-Search has been ported to two applications released at Rolls-Royce plc for searching technical documentation about jet engines.

The Semantic Desktop is a means to support users in Personal Information Management (PIM). It provides an excellent test bed for Semantic Web technology: resources (

e. g.

, persons, projects, messages, documents) are distributed amongst multiple systems, ontologies are used to link and annotate them. Finding information is a core element in PIM. For the end user, the search interface has to be intuitive to use, natural language queries provide a simple mean to express requests. State of the art semantic search engines focus on fact retrieval or on semantic document retrieval. We combine both approaches to search the Semantic Desktop exploiting all available information. Our semantic search engine, built on

semantic teleporting

and

spreading activation

, is able to answer natural language queries with facts,

e. g.

, a specific phone number, and/or relevant documents. We evaluated our approach on ESWC 2007 data in comparison with Google site search.

The increasing amount of data on the Semantic Web offers opportunities for semantic search. However, formal query hinders the casual users in expressing their information need as they might be not familiar with the query’s syntax or the underlying ontology. Because keyword interfaces are easier to handle for casual users, many approaches aim to translate keywords to formal queries. However, these approaches yet feature only very basic query ranking and do not scale to large repositories. We tackle the scalability problem by proposing a novel clustered-graph structure that corresponds to only a summary of the original ontology. The so reduced data space is then used in the exploration for the computation of top-

k

queries. Additionally, we adopt several mechanisms for query ranking, which can consider many factors such as the query length, the relevance of ontology elements w.r.t. the query and the importance of ontology elements. The experimental results performed against our implemented system Q2Semantic show that we achieve good performance on many datasets of different sizes.

Context-awareness is a highly desired feature across several application domains. Semantic Web Services (SWS) technologies address context-adaptation by enabling the automatic discovery of distributed Web services for a given task based on comprehensive semantic representations. Whereas SWS technology supports the allocation of resources based on semantics, it does not entail the discovery of appropriate SWS representations for a given situation. Describing the complex notion of a situation in all its facets through symbolic SWS representation facilities is a costly task which may never lead to semantic completeness and introduces ambiguity issues. Moreover, even though not any real-world situation completely equals another, it has to be matched to a finite set of parameter descriptions within SWS representations to enable context-adaptability. To overcome these issues, we propose Conceptual Situation Spaces (CSS) to facilitate the description of situation characteristics as members in geometrical vector spaces following the idea of Conceptual Spaces. CSS enable fuzzy similarity-based matchmaking between real-world situation characteristics and predefined situation descriptions. Following our vision, the latter are part of semantic Situation-Driven Process (SDP) descriptions, which define a composition of SWS Goals suitable to support the course of an evolving situation. Particularly, we refer to the WSMO approach for SWS. Consequently, our approach extends the expressiveness of WSMO by enabling the automatic discovery, composition and execution of achievable goals for a given situation. To prove the feasibility, we apply our approach to the domain of eLearning and provide a proof-of-concept prototype.

UDDI registries are included as a standard offering within the product suite of any major SOA vendor, serving as the foundation for establishing design-time and run-time SOA governance. Despite the success of the UDDI specification and its rapid uptake by the industry, the capabilities of its offered service discovery facilities are rather limited. The lack of machine-understandable semantics in the technical specifications and classification schemes used for retrieving services, prevent UDDI registries from supporting fully automated and thus truly effective service discovery. This paper presents the implementation of a semantically-enhanced registry that builds on the UDDI specification and augments its service publication and discovery facilities to overcome the aforementioned limitations. The proposed solution combines the use of SAWSDL for creating semantically annotated descriptions of service interfaces and the use of OWL-DL for modelling service capabilities and for performing matchmaking via DL reasoning.

In Web Service Composition (WSC) problems, the composition process generates a composition (i.e., a plan) of atomic services, whose execution achieves some objectives on the Web. Existing research on Web service composition generally assumed that these objectives are absolute; i.e., the service-composition algorithms must achieve all of them in order to generate successful outcomes; otherwise, the composition process fails altogether. The most straightforward example is the use of OWL-S process models that specifically tell a composition algorithm how to achieve a functionality on the Web. However, in many WSC problems, it is also desirable to achieve users’ preferences that are not absolute objectives; instead, a solution composition generated by a WSC algorithm must satisfy those preferences as much as possible. In this paper, we first describe a way to augment Web Service Composition process, where services are described as OWL-S process models, with qualitative user preferences. We achieve this by mapping a given set of process models and preferences into a planning language for representing Hierarchical Task Networks (HTNs). We then present

SCUP

, our new WSC planning algorithm that performs a best-first search over the possible HTN-style task decompositions, by heuristically scoring those decompositions based on ontological reasoning over the input preferences. Finally, we discuss our experimental results on

SCUP

.

In the e-Science context, workflow technologies provide a problem-solving environment for researchers by facilitating the creation and execution of experiments from a pool of available services. In this paper we will show how Semantic Web technologies can be used to overcome a limitation of current workflow languages by capturing experimental constraints and goals, which we term

scientist’s intent

. We propose an ontology driven framework for capturing such intent based on workflow metadata combined with SWRL rules. Through the use of an example we will present the key benefits of the proposed framework in terms of enriching workflow output, assisting workflow execution and provenance support. We conclude with a discussion of the issues arising from application of this approach to the domain of social simulation.

Several approaches to semantic Web services, including OWL-S, SWSF, and WSMO, have been proposed in the literature with the aim to enable automation of various tasks related to Web services, including discovery, contracting, enactment, monitoring, and mediation. The ability to specify processes and to reason about them is central to these initiatives. In this paper we analyze the WSMO choreography model, which is based on Abstract State Machines (ASMs), and propose a methodology for generating WSMO choreography from visual specifications. We point out the limitations of the current WSMO model and propose a faithful extension that is based on Concurrent Transaction Logic (CTR). The advantage of a CTR-based model is that it uniformly captures a number of aspects that previously required separate mechanisms or were not captured at all. These include process specification, contracting for services, service enactment, and reasoning.

Current efforts in Semantic Web Services do not sufficiently address the industrial developments of SOA technology in regards to bottom-up modeling of services, that is, building incremental layers on top of existing service descriptions. An important step in this direction has been made in the W3C by the SAWSDL WG proposing a framework for annotating WSDL services with arbitrary semantic descriptions. We build on the SAWSDL layer and define WSMO-Lite service ontology, narrowing down the use of SAWSDL as an annotation mechanism for WSMO-Lite. Ultimately, our goal is to allow incremental steps on top of existing service descriptions, enhancing existing SOA capabilities with intelligent and automated integration.

Increasing amounts of RDF data are available on the Web for consumption by Semantic Web browsers and indexing by Semantic Web search engines. Current Semantic Web publishing practices, however, do not directly support efficient discovery and high-performance retrieval by clients and search engines. We propose an extension to the Sitemaps protocol which provides a simple and effective solution: Data publishers create Semantic Sitemaps to announce and describe their data so that clients can choose the most appropriate access method. We show how this protocol enables an extended notion of authoritative information across different access methods.

This paper concerns versioning services over Semantic Web (SW) repositories. We propose a novel storage index (based on partial orders), called POI, that exploits the fact that RDF Knowledge Bases (KB) have not a unique serialization (as it happens with texts). POI can be used for storing several (version-related or not) SW KBs. We discuss the benefits and drawbacks of this approach in terms of storage space and efficiency both analytically and experimentally in comparison with the existing approaches (including the change-based approach). For the latter case we report experimental results over synthetic data sets. POI offers notable space saving as well as efficiency in various cross version operations. It is equipped with an efficient version insertion algorithm and could be also exploited in cases where the set of KBs does not fit in main memory.

Recommender systems face up to current information overload by selecting automatically items that match the personal preferences of each user. The so-called content-based recommenders suggest items similar to those the user liked in the past, by resorting to syntactic matching mechanisms. The rigid nature of such mechanisms leads to recommend only items that bear a strong resemblance to those the user already knows. In this paper, we propose a novel content-based strategy that diversifies the offered recommendations by employing reasoning mechanisms borrowed from the Semantic Web. These mechanisms discover extra knowledge about the user’s preferences, thus favoring more accurate and flexible personalization processes. Our approach is generic enough to be used in a wide variety of personalization applications and services, in diverse domains and recommender systems. The proposed reasoning-based strategy has been empirically evaluated with a set of real users. The obtained results evidence computational feasibility and significant increases in recommendation accuracy w.r.t. existing approaches where our reasoning capabilities are disregarded.

The need for adaptive and personalized Rich Internet Application puts a new dimension to already existing approaches of Adaptive Hypermedia Systems. Instead of computing the adaptation steps at the server, Rich Internet Applications need a client-side approach that can react immediately on user input. In this paper we present a novel approach that holistically combines page annotations, semantic Web usage mining, user modeling, ontologies and rules to adapt AJAX pages. The focus of our pater is the conceptual introduction of the autonomous client. An autonomous client directly executes all necessary adaptation steps based on a user model, without requesting any logic on the server. In order to realize this, we use ontologies to annotate Rich Internet Applications and to describe the user model as well as semantic Web usage mining for detecting adaptation rules. Additionally, we provide a detailed overview and evaluation of how we moved resource-intensive ontology processing and rules execution from the server to the client.

Most of the challenges faced when building the Semantic Web require a substantial amount of human labor and intelligence. Despite significant advancement in ontology learning and human language technology, the tasks of ontology construction, semantic annotation, and establishing alignments between multiple ontologies remain highly dependent on human intelligence. This means that individuals need to contribute time and sometimes other resources. Unfortunately, we observe a serious lack of user involvement in the aforementioned tasks, which may be due to the absence of motivations for people who contribute. As a novel solution, we (1) propose to masquerade the core tasks of weaving the Semantic Web behind online, multi-player game scenarios, in order to create proper incentives for human users to get involved. Doing so, we adopt the findings from the already famous “games with a purpose” by von Ahn, who has shown that presenting a useful task, which requires human intelligence, in the form of an online game can motivate a large amount of people to work heavily on this task, and this for free. Then, we (2) describe our generic OntoGame platform, and (3) several gaming scenarios for various tasks plus our respective prototypes. Based on the analysis of user data and interviews with players, we provide preliminary evidence that users (4) enjoy the games and are willing to dedicate their time to those games, (5) are able to produce high-quality conceptual choices. Eventually we show how users entertaining themselves by online games can unknowingly help weave and maintain the Semantic Web.

Geospatial Web services allow to access and to process Geospatial data. Despite significant standardisation efforts, severe heterogeneity and interoperability problems remain. The SWING environment leverages the Semantic Web Services (SWS) paradigm to address these problems. The environment supports the entire life-cycle of Geospatial SWS. To this end, it integrates a genuine end-user tool, a tool for developers of new Geospatial Web services, a commercial service Catalogue, the Web Service Execution Environment platform (WSMX), as well as an annotation tool. The demonstration includes three usage scenarios of increasing complexity, involving the semantic annotation of a legacy service, the semantic discovery of a Geospatial SWS, as well as the composition of a new Geospatial SWS.

One of the main problems when creating execution-level process models is finding implementations for process activities. Carrying out this activity manually can be time consuming, since it involves searching in large service repositories. We present Maestro for BPMN, a tool that allows to annotate and automatically compose activities within business processes. We explain the main assumptions and algorithms underlying the tool, and we overview what will be demonstrated at ESWC.

Relational databases are valuable sources for ontology learning. Methods and tools have been proposed to generate ontologies from such structured input. However, a major persisting limitation is the derivation of ontologies with flat structure that simply mirror the schema of the source databases. In this paper, we show how the RDBToOnto tool can be used to derive accurate ontologies by taking advantage of both the database schema and the data, and more specifically through identification of taxonomies hidden in the data. This extensible tool supports an iterative approach that allows progressive refinement of the learning process through user-defined constraints.

Creating and designing an ontology is a complex task requiring discussions between domain and ontology engineering experts as well as the users of an ontology. We present the Cicero tool, that facilitates efficient discussions and accelerates the convergence to decisions. Furthermore, by integrating it with an ontology editor, it helps to improve the documentation of an ontology.

Instant Messaging is in addition to Web and Email the most popular service on the Internet. With xOperator we demonstrate the implementation of a strategy which deeply integrates Instant Messaging networks with the Semantic Web. The xOperator concept is based on the idea of creating an overlay network of collaborative information agents on top of social IM networks. It can be queried using a controlled and easily extensible language based on AIML templates. Such a deep integration of semantic technologies and Instant Messaging bears a number of advantages and benefits for users when compared to the separated use of Semantic Web technologies and IM, the most important ones being context awareness as well as provenance and trust. Our demonstration showcases how the xOperator approach naturally facilitates enterprise and personal information management as well as access to large scale heterogeneous information sources.

This demo proposal briefly presents LabelTranslator, a system that suggests translations of ontology labels, with the purpose of localizing ontologies. LabelTranslator takes as input an ontology whose labels are described in a source natural language and obtains the most probable translation of each ontology label into a target natural language. Our main contribution is the automatization of this process, which reduces human efforts to localize manually the ontology.

RKB Explorer is a Semantic Web application that is able to present unified views of a significant number of heterogeneous data sources. We have developed an underlying information infrastructure which is mediated by ontologies and consists of many independent triplestores, each publicly available through both SPARQL endpoints and resolvable URIs. To realise this synergy of disparate information sources, we have deployed tools to identify co-referent URIs, and devised an architecture to allow the information to be represented and used. This paper provides a brief overview of the system including the underlying infrastructure, and a number of associated tools for both knowledge acquisition and publishing.

PowerMagpie is a tool that brings semantic interpretation to classical web pages by dynamically—

i.e.

during browsing—selecting and making use of a wide range of online available ontologies. We introduce the idea of extending browsing through semantic, ontology-based interpretation. Then, we provide a brief description of the architecture. In the end we underline which aspects of the available online semantic data are demonstrated, what the user may learn and which are the future directions.

Collaborative tagging systems like Flickr and del.icio.us provide centralized content annotation and sharing which is simple to use and attracts many people. A combination of tagging with peer-to-peer systems overcomes typical limitations of centralized systems, however, decentralization also hampers the efficient computation of global statistics facilitating user navigation. We present Tagster, a peer-to-peer based tagging system that provides a solution to this challenge. We describe a typical scenario that demonstrates the advantages of distributed content sharing with Tagster.

The development of software is not an easy task and the availability of adequate tool support is an important step towards reducing the effort that a developer must put into the Software Development Cycle. As an emerging technology, it is vital that Semantic Web Services can be quickly and easily created by developers to ensure that this new technology can be easily adopted. In this demo the process of developing Semantic Web Service descriptions, through the WSMO paradigm, using the Web Service Modeling Toolkit (WSMT) will be presented.

For document-centric work, meta-information in form of annotations has proven useful to enhance search and other retrieval tasks.

The Mymory project uses a web-based workbench based on the semantic wiki

Kaukolu

that allows annotating texts both with concepts modeled in the user’s personal information model and other ontologies in a flexible way. Annotations get enriched with contextual information gathered by a context elicitation component. Reading annotations are created with the help of an eyetracker.

In the demonstration, we use contextualized annotations and semantic search using annotations in order to support knowledge workers in the domain of software licenses.

The demonstration presents Pronto - a prototype of a non-monotonic probabilistic reasoner for very expressive Description Logics. Pronto is built on top of the OWL DL reasoner Pellet, and is capable of performing default probabilistic reasoning in the Semantic Web. It can handle uncertainty in terminological and assertional DL axioms. The demonstration covers Pronto’s features and capabilities as well as current challenges and limitations. It describes how an involved realistic problem of breast cancer risk assessment can be formalized in terms of probabilistic reasoning in Pronto. As an important outcome, it is anticipated that attendees should learn and better understand the potential of ontology based approaches to modeling problems involving reasoning under uncertainty.

Semantic Browsing provides contextualized dynamically generated Web content customizing the knowledge to better meet user expectations. The real-world medical digital library, the National electronic Library of Infection (NeLI, www.neli.org.uk), enriched with an infection domain ontology enables new semantic services to be developed qualitatively. In this paper, we will address the use of group profiling to customize semantic browsing by integrating distributed knowledge sources. The service is evaluated by web server logs analysis, dynamically enhancing the profiles and by qualitative feedback from real users of the NeLI portal.

SWiM is a semantic wiki for collaboratively building, editing and browsing mathematical knowledge represented in the domain-specific structural semantic markup language OMDoc. It motivates users to contribute to collections of mathematical knowledge by instantly sharing the benefits of knowledge-powered services with them. SWiM is currently being used for authoring content dictionaries, i. e. collections of uniquely identified mathematical symbols, and prepared for managing a large-scale proof formalisation effort.

We believe that the possibility to use SPARQL as a front end to heterogeneous data without significant cost in performance or expressive power is key to RDF taking its rightful place as the lingua franca of data integration. To this effect, we demonstrate how RDF and SPARQL can tackle a mix of standard relational workload and data mining in public data sources.

We discuss extending SPARQL for business intelligence (BI) workloads and relate experiences on running SPARQL against relational and native RDF databases. We use the well known TPC H benchmark as our reference schema and workload. We define a mapping of the TPC H schema to RDF and restate the queries as BI extended SPARQL. To this effect, we define aggregation and nested queries for SPARQL.

We demonstrate that it is possible to perform the TPC H workload restated in SPARQL against an existing RDBMS without loss of performance or expressivity and without changes to the RDBMS.

Finally, we demonstrate how to combine TPC-H or XBRL financial reports with RDF data from CIA factbook and DBpedia.

In this demo we present a first implementation of Semantic Web Pipes, a powerful tool to build RDF-based mashups. Semantic Web pipes are defined in XML and when executed they fetch RDF graphs on the Web, operate on them, and produce an RDF output which is itself accessible via a stable URL. Humans can also use pipes directly thanks to HTML wrapping of the pipe parameters and outputs. The implementation we will demo includes an online AJAX pipe editor and execution engine. Pipes can be published and combined thus fostering collaborative editing and reuse of data mashups.

In this demo description, we present

Konduit

, a desktop-based platform for visual programming with RDF data. Based on the idea of the semantic desktop, non-technical users can create, manipulate and mash-up RDF data with Konduit, and thus generate simple applications or workflows, which are aimed to simplify their everyday work by automating repetitive tasks. The platform allows to combine data from both Web and desktop and integrate it with existing desktop functionality, thus bringing us closer to a convergence of Web and desktop.

We present a demo of SCARLET, a technique for discovering relations between two concepts by

harvesting the Semantic Web

, i.e.,

automatically

finding and exploring

multiple

and

heterogeneous

online ontologies. While we have primarily used SCARLET’s relation discovery functionality to support ontology matching and enrichment tasks, it is also available as a stand alone component that can potentially be integrated in a wide range of applications. This demo will focus on presenting SCARLET’s functionality and its different parametric settings that can influence the trade-off between its accuracy and time performance.

We present ODEWiki, a technology for the development of Semantic Wikis, which has a combined set of added-value features over other existing semantic wikis in the state of the art. Namely, ODEWiki interoperates with an existing semantic portal technology (ODESeW), it manages inconsistencies raised because of the distributed nature of knowledge base development and maintenance, it uses RDFa for the annotation of the resulting wiki pages, it follows a WYSIWYG approach, and it allows decoupling wiki pages and ontology instances, that is, a wiki page may contain one or several ontology instances. Although some of these features appear in some of the state-of-the-art semantic wikis, but they are not combined together in a single solution.

Within the health care and life sciences (HCLS) domain, a plethora of phenomena exists that range across the whole “vertical scale” of biomedicine. To accurately research and describe those phenomena, a tremendous amount of highly heterogeneous data have been produced and collected with various research methodologies encompassing the genetic, molecular, tissue, and organ level. An initial step to provide researchers with access to this data has been through creating integrated views on existing and open biomedical datasets published on the Web. In order to make the next step, we need to now create easy-to-use yet powerful applications that enable researchers to efficiently query, integrate and analyze those datasets.

Future information, knowledge and content infrastructures which provide highly automated support in fulfilling users goals will most likely rely on some form of GRID computing. In combination with Semantic Web technologies and Semantic Web Services, such infrastructure will be much enhanced to form the Semantic Grid. Last but not least, the content, which can range from multimedia data to intelligent objects, can and must be interpreted by the services of the Semantic Grid. In this demo we will detail the GRISINO Common Infrastructure, an integrated infrastructure for Semantic Web Services, Intelligent Content Objects and Grid, that are brought together in the search for an emerging solution for next generation distributed applications.

We demonstrate results presentation and query refinement functions of the SemSearch engine for semantic web portals and intranets.

The IMAGINATION project provides image-based navigation for digital cultural and scientific resources. Users can click on parts of an image to find other, interesting images to a given context. In this paper, we present the core parts of the IMAGINATION application. To allow the navigation through images, this application automatically generates high quality semantic metadata. Therefore it combines automated processes for person and object detection, face detection and identification in images together with text mining techniques that exploit domain specific ontologies.

We demonstrate Web Service Execution Environment (WSMX), a semantic middleware platform for runtime service discovery, mediation and execution, applied to SWS Challenge scenarios. We show the modelling principles as well as execution aspects of the WSMX semantic technology addressing the real-world requirements.

We describe ViCoS, a tool for constructing and visualising conceptual spaces in the area of language documentation. ViCoS allows users to enrich existing lexical information about the words of a language with conceptual knowledge. Their work towards language-based, informal ontology building must be supported by easy-to-use workflows and supporting software, which we will demonstrate.