The Semantic Web: ESWC 2013 Satellite Events

ESWC 2013 was held in Montpellier in May 2013 and the main program was preceded by two days of workshops and tutorials. This short report gives an overview of the 11 workshops and 7 tutorials that were attended by over 170 researchers before the start of the main conference, and describes the procedure adopted to select the proposals.

ParlBench is an RDF benchmark modelling a large scale electronic publishing scenario. The benchmark offers large collections of the Dutch parliamentary proceedings together with information about members of the parliament and political parties. The data is real, but free of intellectual property rights issues. On top of the benchmark data sets several application benchmarks as well as targeted micro benchmarks can be developed. This paper describes the benchmark data sets and 19 analytical queries covering a wide range of SPARQL constructs. The potential use of ParlBench is demonstrated by executing the queries for 8 different scaling of the benchmark data sets on Virtuoso RDF store. Measured on a standard laptop, data loading times varied from 43 seconds (for 1% of the data set) to 48 minutes (for the complete data set), and execution of the complete set of queries (570 queries in total) varied from 9 minutes to 13 hours.

Microblogs are increasingly gaining attention as an important information source in emergency management. Nevertheless, it is still difficult to reuse this information source during emergency situations, because of the sheer amount of unstructured data. Especially for detecting small scale events like car crashes, there are only small bits of information, thus complicating the detection of relevant information.

We present a solution for a real-time identification of small scale incidents using microblogs, thereby allowing to increase the situational awareness by harvesting additional information about incidents. Our approach is a machine learning algorithm combining text classification and semantic enrichment of microblogs. An evaluation based shows that our solution enables the identification of small scale incidents with an accuracy of 89% as well as the detection of all incidents published in real-time Linked Open Government Data.

Ontologies, like any other model, change over time due to modifications in the modeled domain, deeper understanding of the domain by the modeler, error corrections, simple refactoring or shift of modeling granularity level. Local changes usually impact the remainder of the ontology as well as any other data and metadata defined over it. The massive size of ontologies and their possible fast update rate requires automatic adaptation methods for relieving ontology engineers from a manual intervention, in order to allow them to focus mainly on high-level inspection. This paper, in spirit of the

Principle of minimal change

, proposes a fully automatic ontology adaptation approach that reacts to ontology updates and computes sound reformulations of ontological axioms triggered by the presence of certain preconditions. The rule-based adaptation algorithm covers up to

$\mathcal{SROIQ}$

DL.

Linked Data repositories offer a wealth of structured facts, useful for a wide array of application scenarios. However, retrieving this data using

Sparql

queries yields a number of challenges, such as limited endpoint capabilities and availability, or high latency for connecting to it. To cope with these challenges, we argue that it is advantageous to cache data that is relevant for future information needs. However, instead of retaining only results of previously issued queries, we aim at retrieving data that is potentially interesting for subsequent requests in advance. To this end, we present different methods to modify the structure of a query so that the altered query can be used to retrieve additional related information. We evaluate these approaches by applying them to requests found in real-world

Sparql

query logs.

The reasons why an author cites other publications are varied: an author can cite previous works to gain assistance of some sort in the form of background information, ideas, methods, or to review, critique or refute previous works. The problem is that the best possible way to retrieve the nature of citations is very time consuming: one should read article by article to assign a particular characterisation to each citation. In this paper we propose an algorithm, called

CiTalO

, to infer automatically the function of citations by means of Semantic Web technologies and NLP techniques. We also present some preliminary experiments and discuss some strengths and limitations of this approach.

This paper introduces YASGUI, a user-friendly SPARQL client. We compare YASGUI with other SPARQL clients, and show the added value and ease of integrating Web APIs, services, and new technologies such as HTML5. Finally, we discuss some of the challenges we encountered in using these technologies for a building robust and feature rich web application.

The domain complexity and structural- and semantic heterogeneity of biodiversity data, as well as idiosyncratic legacy data-creation processes, present significant integration and interoperability challenges. In this paper we describe a case-study of ontology-driven semantic mediation using records of flower-visiting insects from three natural history collections in South Africa. We establish a conceptual domain model for flower-visiting, expressed in an OWL ontology, and use it to semantically enrich the three data-stores. We show how this enrichment allows for the creation of an integrated flower-visiting dataset. We discuss how the ontology captures both implicit and explicit knowledge, and we show how the ontology can be used to identify and analyze high-level flower-visiting behaviour. We propose that a system that employs this ontology for semantic enrichment and semantic mediation may be used to automatically construct flower-visiting and pollination networks, the manually constructed equivalents of which are routinely used by domain scientists to analyze their data.

Linked Data is a gigantic, constantly growing and extremely valuable resource, but its usage is still heavily dependent on (i) the familiarity of end users with RDF’s graph data model and its query language, SPARQL, and (ii) knowledge about available datasets and their contents. Intelligent keyword search over Linked Data is currently being investigated as a means to overcome these barriers to entry in a number of different approaches, including semantic search engines and the automatic conversion of natural language questions into structured queries. Our work addresses the specific challenge of mapping keywords to Linked Data resources, and proposes a novel method for this task. By exploiting the graph structure within Linked Data we determine which properties between resources are useful to discover, or directly express, semantic similarity. We also propose a novel scoring function to rank results. Experiments on a publicly available dataset show a 17% improvement in Mean Reciprocal Rank over the state of the art.

Understanding ontology evolution is becoming an active topic of interest for ontology engineers, e.g., there exist large collaboratively-developed ontologies but, unlike in software engineering, comparatively little is understood about the dynamics of historical changes, especially at a fine level of granularity. Only recently has there been a systematic analysis of changes across ontology versions, but still at a coarse-grained level. The National Cancer Institute (NCI) Thesaurus (NCIt) is a large, collaboratively-developed ontology, used for various Web and research-related purposes, e.g., as a medical research controlled vocabulary. The NCI has published ten years worth of monthly versions of the NCIt as Web Ontology Language (OWL) documents, and has also published reports on the content of, development methodology for, and applications of the NCIt. In this paper, we carry out a fine-grained analysis of asserted axiom dynamics throughout the evolution of the NCIt from 2003 to 2012. From this, we are able to identify axiomatic editing patterns that suggest significant regression editing events in the development history of the NCIt.

The recently started EU FP7-funded project Optique will develop an end-to-end OBDA system providing scalable end-user access to industrial Big Data stores. This paper presents an initial architectural specification for the Optique system along with the individual system components.

The demonstration and poster track is an opportunity for researchers and practitioners to present their innovative prototypes, practical developments, on-going projects, lessons learned and late-breaking results. This year we had a very exciting track with thirty-five poster and thirty-two demo submissions. All poster and demonstration papers were peer reviewed by at least 2 reviewers, resulting in twenty accepted posters and twenty-four accepted demos.

The Linked Open Data (LOD) cloud has grown to an enormous source for semantic data. Its distributed and decentralized approach is one reason for its success but also poses challenges. A main difficulty is to identify those data sources on the LOD cloud which provide the information a user is actually interested in. With LODatio, we have developed a prototype retrieval system to support users in finding the right data sources for a given schema-oriented information need. Beyond classical search system functions such as retrieval, ranking, result set size estimation and providing result snippets, LODatio provides sophisticated support for the users in refining and expanding their information need.

Payola is a framework for Linked Data analysis and visualization. The goal of the project is to provide end users with a tool enabling them to analyze Linked Data in a user-friendly way and without knowledge of SPARQL query language. This goal can be achieved by populating the framework with variety of domain-specific analysis and visualization plugins. The plugins can be shared and reused among the users as well as the created analyses. The analyses can be executed using the tool and the results can be visualized using a variety of visualization plugins. The visualizations can be further customized according to ontologies used in the resulting data. The framework is highly extensible and uses modern technologies such as HTML5 and Scala. In this paper we show two use cases, one general and one from the domain of public procurement.

With the increased use of ontologies in semantically-enabled applications, the issues of debugging and aligning ontologies have become increasingly important. The quality of the results of such applications is directly dependent on the quality of the ontologies and mappings between the ontologies they employ. A key step towards achieving high quality ontologies and mappings is discovering and resolving modeling defects, e.g., wrong or missing relations and mappings. In this demonstration paper we present a system for aligning taxonomies, the most used kind of ontologies, and debugging taxonomies and their alignments, where ontology alignment is treated as a special kind of debugging.

Choosing the tools for the management of large and semi-structured knowledge bases has always been considered as a quite crafty task. This is due to the emergence of different solutions in a short period of time, and also to the lack of benchmarking available solutions. In this paper, we use ALASKA, a logical framework, that enables the comparison of different storage solutions at the same logical level. ALASKA translates different data representation languages such as relational databases, graph structures or RDF triples into logics. We use the platform to load semi-structured knowledge bases, store, and perform conjunctive queries over relational and non-relational storage systems.

In this paper we describe an extension of the

MoKi

tool able to support the management of multilingual ontologies. The multilingual features of

MoKi

are based on an integration with Dictionary Based Translation ad Machine Translation technologies. Also the collaborative features of

MoKi

are used to support the interaction between domain experts in order to discuss and agree on the translations of the terms to be used in each language.

Link discovery plays a central role in the implementation of the Linked Data vision. In this demo paper, we present SAIM, a tool that aims to support users during the creation of high-quality link specifications. The tool implements a simple but effective workflow to creating initial link specifications. In addition, SAIM implements a variety of state-of-the-art machine-learning algorithms for unsupervised, semi-supervised and supervised instance matching on structured data. We demonstrate SAIM by using benchmark data such as the OAEI datasets.

Linked Data has become an essential part of the Semantic Web. A lot of Linked Data is already available in the Linked Open Data cloud, which keeps growing due to an influx of new data from research and open government activities. However, it is still quite difficult to access this wealth of semantically enriched data directly without having in-depth knowledge about SPARQL and related semantic technologies. In this paper, we present the Linked Data Query Wizard, a prototype that provides a Linked Data interface for non-expert users, focusing on keyword search as an entry point and a tabular interface providing simple functionality for filtering and exploration.

There is currently no agreement on common shared representations of audio features in the field of music information retrieval. The Audio Feature Ontology has been developed as part of a harmonised library of modular ontologies to solve the problem of interoperability between music related data sources. We demonstrate a software framework which combines this ontology and related Semantic Web technologies with data extraction and analysis software, in order to enhance audio feature extraction workflows.

Discovery Hub is an exploratory search engine that helps users explore topics of interests for learning and leisure purposes. It makes use of a semantic spreading activation algorithm coupled with a sampling technique so that it does not require a preprocessing step.

Google Fusion Tables (GFT) is a data management, integration and visualization service provided by Google. Users can upload their structured data, integrate it with other people’s data, and visualize it on various tools provided, such as Google Maps, charts or graphs. Every GFT table constitutes a data silo that is not commonly linked to other data sources. A way to enable data to be linked and reused is by exposing it as (virtual) RDF dataset (for instance, using R2RML) and to query it using SPARQL. In this work, we present a system that exposes GFT tables as SPARQL endpoints, enabling federated SPARQL queries with data from other SPARQL endpoints.

We present an extension of QAKiS, a system for open domain Question Answering over linked data, that allows to query DBpedia multilingual chapters. Such chapters can contain different information with respect to the English version, e.g. they provide more specificity on certain topics, or fill information gaps. QAKiS exploits the alignment between properties carried out by DBpedia contributors as a mapping from Wikipedia terms to a common ontology, to exploit information coming from DBpedia multilingual chapters, broadening therefore its coverage. For the demo, English, French and German DBpedia chapters are the RDF data sets to be queried using a natural language interface.

Despite its rising popularity, using and managing Linked Data remains a challenge for developers of mainstream web and mobile applications. In this demo we present "LDtogo", a framework that makes it easy for application administrators to integrate and maintain Linked Data when building new applications or when re-using existing ones. LDtogo does this by 1) supporting data processing by means of plug-ins, 2) providing an easy-to-use interface to create a customized API wrapper for applications and 3) using only technologies available on common web hosting platforms (e.g. LAMP hosting environments). Its modular structure and support for standards are important properties.

University data is typically stored in separate data silos even though the data is implicitly richly related together. Such data has a large and diverse user base, including faculty members, students, industrial partners, alumnis, collaborating universities, and media. In this paper, we demonstrate two tools for understanding and using the contents of linked university data. The first tool, Visualization Playground (VISU), supports querying and visualizing the data for example for illustrating emerging trends in universities (e.g., about publications) and for comparing differences. The second tool, Vocabulary Visualizer (

V

2

), demonstrates the usage of shared vocabularies in the Linked University Data Cloud. It reveals what kinds of data different universities have published, and what terms are used to describe the contents. Such analysis is a basis for facilitating design of Linked Data applications across university data boundaries.

Linked geospatial data has recently received attention as researchers and practitioners have started tapping the wealth of geospatial information available on the Web. With the rapid population of the Web of data with geospatial information, applications to manage it have also started to emerge. What the semantic geospatial web lacks, though, compared to the technological arsenal of the traditional GIS area are the tools that aid researchers and practitioners in making use of this ocean of geospatial data. In this demo paper, we present Sextant, a web-based tool that enables exploration of linked geospatial data as well as creation, sharing, and collaborative editing of thematic maps by combining linked geospatial data and other geospatial information available in standard OGC file formats.

This paper describes and demonstrates a distributional semantic search service infrastructure for Linked Dataspaces. The center of the approach relies on the use of a distributional semantics infrastructure to provide semantic search and query services over data for users and applications, improving data accessibility over the Dataspace. By accessing the services through a REST API, users can semantically index and search over data using the distributional semantic knowledge embedded in the reference corpus. The use of distributional semantic models, which rely on the automatic extraction from large corpora, supports a comprehensive and approximative semantic matching mechanism with a low associated adaptation cost for the inclusion of new data sources.

XSPARQL is a query language which facilitates query, integration and transformation between XML and RDF data formats. Although XSPARQL supports semantic data integration by providing uniform access over XML and RDF, but it requires users to be familiar with both of its underlying query languages (e.g XQuery and SPARQL). In this system demo, we show how mashup-based techniques can be used for auto generation and execution of XSPARQL queries. XSPARQL-Viz provides an easy to use

drag and drop

visual query editor, which supports novice users in designing complex mappings between XML and RDF and based on these mappings auto generates and executes XSPARQL queries. Results can also be visualised as a graph, table or list.

We present LinDA: an extensible, data driven platform where analytical tools can be integrated to process, analyse and describe data sets of Linked Open Data. To date, we have integrated four different tools: the computation of a VoID description, the computation of schema-level index (SchemEX), an information theoretic analysis of the data and the construction of a formal concept lattice. A demo prototype of LinDA is publicly available and allows for uploading data sets for analysis. A web frontend allows users to interact with the system, access the results of previously analysed data sets or to upload new data sets for the analysis tools to operate on.

Bibliographic citation is one of the most important activities of an author in the production of any scientific work. The reasons that an author cites other publications are varied: to gain assistance of some sort, to review, critique or refute previous works, etc. In this paper we propose a tool, called

CiTalO

, to infer automatically the nature of citations by means of Semantic Web technologies and NLP techniques. Such a characterisation makes citations more effective for linking, disseminating, exploring and evaluating research.

This demo presents

Graphia

, an information extraction pipe-line targeting an RDF representation of unstructured data in the form of structured discourse graphs (SDGs). It combines natural language processing and information extraction techniques with the use of linked open data resources and semantic web technologies to enable discourse representation as a set of contextualized relationships between entities.

Cross lingual querying of financial and business data from multi-lingual sources requires that inherent challenges posed by the diversity of financial concepts and languages used in different jurisdictions be addressed. Ontologies can be used to semantically align financial concepts and integrate financial facts with other company information from multilingual, semi-structured and unstructured Open Data sources. Availability as Linked Data then allows cross-lingual interrogation of the interlinked multi-lingual data set. This paper presents how the use of semantics and Linked Data enables the alignment and integration of business and financial facts provided by the different European Business Registers. The demonstrator allows business users to query multilingual data, perform comparisons, and review generated financial metrics.

In this paper, we demonstrate the RBA (

R

2RML

B

y

A

ssertion) tool which automatically generates customized R2RML mappings based on a set of semantic mappings that model the relationship between the relational database schema and a target ontology in RDF. The semantic mappings are specified by a set of correspondence assertions, which are simple to understand.

In this paper we demonstrate the potentiality of Tìpalo, a tool for automatically typing DBpedia entities. Tìpalo identifies the most appropriate types for an entity in DBpedia by interpreting its definition extracted from its corresponding Wikipedia abstract. Tìpalo relies on FRED, a tool for ontology learning from natural language text, and on a set of graph-pattern-based heuristics which work on the output returned by FRED in order to select the most appropriate types for a DBpedia entity. The tool returns a RDF graph composed of rdf:type, rdfs:subClassOf, owl:sameAs, and owl:equivalentTo statements providing typing information about the entity. Additionally the types are aligned to two lists of top-level concepts, i.e., Wordnet supersenses and a subset of DOLCE Ultra Lite classes. Tìpalo is available as a Web-based tool and exposes its API as HTTP REST services.

Social platforms open a window to what is happening in the world in near real-time: (micro-)posts and media items are shared by people to report their feelings and their activities related to any type of events. Such an information can be collected and analyzed in order to get the big picture of an event from the crowd point of view. In this paper, we present a general framework to capture and analyze micro-posts containing media items relevant to a search term. We describe the results of an experiment that consists in collecting fresh social media posts (posts containing media items) from numerous social platforms in order to generate the story of the “2013 Italian Election”. Items are grouped in meaningful time intervals that are further analyzed through deduplication, clusterization, and visual representation. The final output is a storyboard that provides a satirical summary of the elections as perceived by the crowd. A screencast showing an example of these functionalities is published at

http://youtu.be/jIMdnwMoWnk

while the system is publicly available at

http://mediafinder.eurecom.fr/story/elezioni2013

.

FRED is an online tool for converting text into internally well-connected and quality linked-data-ready ontologies in web-service-acceptable time. It implements a novel approach for ontology design from natural language sentences. In this paper we present a demonstration of such tool combining Discourse Representation Theory (DRT), linguistic frame semantics, and Ontology Design Patterns (ODP). The tool is based on Boxer which implements a DRT-compliant deep parser. The logical output of Boxer enriched with semantic data from Verbnet or Framenet frames is transformed into RDF/OWL by means of a mapping model and a set of heuristics following ODP best-practice [5] of OWL ontologies and RDF data design.

A crucial task in a researchers’ daily work is the analysis of primary research data to estimate the evolution of certain fields or technologies, e.g. tables in publications or tabular benchmark results. Due to a lack of comparability and reliability of published primary research data, this becomes more and more time-consuming leading to contradicting facts, as has been shown for ad-hoc retrieval [1]. The CODE project [2] aims at contributing to a Linked Science Data Cloud by integrating unstructured research information with semantically represented research data. Through crowdsourcing techniques, data centric tasks like data extraction, integration and analysis in combination with sustainable data marketplace concepts will establish a

sustainable, high-impact ecosystem

.

Current spoken dialogue systems (SDS) for mobile search are mostly domainspecific and make use of static knowledge. Consequently they do not take into account the interests, location and contextual situation of the concrete user. We propose PARLANCE, which is a more dynamic and personalized SDS that incorporates 1) a dynamic knowledge base consisting of modular ontologies that are enriched incrementally with information extracted from the Web; 2) and an evolving user profile. This allows the system to provide answers that are more tailored to the concrete user and to exploit the Web as a source of information, which can improve the quality of experience for the user. The PARLANCE SDS aims to guide the user in his search for information by providing answers that are: (1) Hyperlocal: The current geographical location of the user is taken into account to provide points of interest (POIs) in the neighborhood; (2) Dynamic: New concepts and entities are learned at runtime and included in the appropriate modular ontologies; (3) Personalized: Potential relevant answers adapted to user’s queries are selected and ranked according to user preferences. Complementary, a form of social search is performed by looking at interests of similar user in the neighborhood (i.e. collaborative filtering). The central component in the PARLANCE architecture is the Interaction Manager (IM) which probabilistically decides on the most appropriate next answer to be provided to the user. The IM exploits information from the Semantic Web by interacting with the Knowledge Base (KB), the Web Content Analyzer (WCA) and the Local Search (LS) components, which will be detailed in the next section.

Complex expressions, as used in mathematics and logics, account for a large part of human knowledge. It is therefore desirable to allow for their representation and search in RDF. We propose an approach that fulfills three objectives: (1) the accurate representation of expressions in standard RDF, so that expressive search is made possible, (2) the automated generation of human-readable labels for expressions, and (3) the compatibility with legacy data (e.g., OWL/RDF, SPIN).

Typically, only positive data can be represented in RDF. However, negative knowledge representation is required in some application domains such as food allergies, software incompatibility and school absence. We present an approach to represent and query RDF data with negative data. We provide the syntax, semantics and an example. We argue that this approach fits into the open-world semantics of RDF according to the notion of certain answers.

Content injection methods rely on understanding community dynamics (i.e. attention factors) in order to publish content that community users will engage with (e.g. product-related posts), however such methods require re-training should the community’s discussed topics change. In this paper we present an examination of the semantic evolution of community forums by measuring the topical specificity of online community forums and then tracking changes in the concepts discussed within the forums over time. Our results indicate that general discussion communities tend to diverge in their semantics, while topically-specific communities do not. These findings inform content injection methods on model longevity and the need for adaptation for general communities.

Semantic similarity measures within a reference ontology have been used in a few ontology alignment (OA) systems. Most use a single reference ontology, typically WordNet, and a single similarity measure within it. The mediating matcher with semantic similarity (MMSS) was added to AgreementMaker to incorporate the selection of a semantic similarity measure and the combination of multiple reference ontologies in an adaptable fashion. The results of experiments using the MMSS on the anatomy track of the Ontology Alignment Evaluation Initiative (OAEI) are reported. A variety of semantic similarity measures are applied within multiple reference ontologies. Using multiple reference ontologies with the MMSS improved alignment results. All information-content based semantic similarity measures produced better alignment results than a path-based semantic similarity measure.

The growth of the Web of Data (WoD) has primarily been funded by subsidies. New datasets are financed via public funding or research programs. This may eventually limit the growth and could hamper data quality for lack of clear incentives. We propose

MaTriX

, a market-based SPARQL broker over the WoD as an economically viable growth option. Similar to others, queries are associated with a budget and minimal result-set quality. The broker then employs auction mechanisms to find a set of data providers that jointly deliver the results. Preliminary results shows that mixing free and commercial providers exhibits superior: consumer surplus, producer profit, total welfare, and recall.

The aim of the Shared Open Vocabulary for Audio Research and Retrieval project is to foster greater agreement on the representation of content-based audio features within music research communities. The Audio Feature Ontology has been developed for this purpose as part of a library of modular ontologies in order to increase interoperability, reproducibility and sustainability in music information retrieval workflows. The ontology provides a descriptive framework for expressing different conceptualisations of the audio features domain and allows for publishing content-derived information about audio recordings.

Discovery Hub is a novel application that processes DBpedia for exploratory search purpose. It implements on-the-fly semantic spreading activation and sampling over linked data sources to suggest ranked topics of interest to the user.

Juridical information is important to organizations and individuals alike and is linked to from all walks of life. The Finnish government has published the Finlex Data Bank for searching and browsing legislation documents. However, the data there is not yet open, is based on a traditional XML schema, and does not conform to new semantic metadata standards. There are many difficulties in maintaining and using the site in, e.g., data harvesting, interoperability, querying, and linking that could be mitigated by the Semantic Web technologies. This paper presents an approach and a project—including first results—for publishing and using the Finnish legislation as a 5-star Linked Open Data service.

We see the local content from peers organized in directories (i.e., on local ordered lists) containing local representations of entities from the real world (e.g., persons, locations, events). Different local representations can give different “versions” of the same real world entity and use different names to refer to it (e.g., Fausto Giunchiglia, Giunchiglia F., Prof. Giunchiglia). Although the names used in these directories connect data that could complement each other, there are no links that allow peers to share and search across them. We propose a Distributed Directory of Entities that makes explicit these connecting links and allows peers to: (i) maintain their data locally and (ii) find the different versions of a real world entity based on any name used in the network. The model we present exploits the name as the central (multi-value) attribute of entities and aims to convince readers of the importance of such names in a peer-to-peer scenario.

Accessing the

relevant

data in Big Data scenarios is increasingly difficult both for end-user and IT-experts, due to the

volume, variety

, and

velocity

dimensions of Big Data.This brings a hight cost overhead in data access for large enterprises. For instance, in the oil and gas industry, IT-experts spend 30-70% of their time gathering and assessing the quality of data [1]. The Optique project (

http://www.optique-project.eu/

) advocates a next generation of the well known

Ontology-Based Data Access

(OBDA) approach to address the Big Data dimensions and in particular the data access problem. The project aims at solutions that reduce the cost of data access dramatically.

The Linked Data movement has focused on building cross-domain interoperability by creating and using (typically)

owl:sameAs

mappings between the datasets in the Linked Data Cloud (LOD). However, when linking data, ontologies would allow for deeper interoperability. Because different ontologies have been used when annotating different datasets, we argue that the LOD cloud needs to be complemented by developing a lightweight “Linked Open Ontology Cloud” (LOO). Aligning the ontologies requires more refined techniques than mapping data instances for the LOD.

Despite their success in general applications, social networks also present a series of challenges like attracting user signups, keeping a healthy contribution level, user privacy concerns, etc. This paper introduces the Social Core – a social network engine that adds semantic-based functionalities like semantic annotations, semantic search and semantic-enhanced access control; as a way to enhance and answer to the current challenges of social applications. The Social Core was integrated as part of the SmartCampus mobile platform, which is currently being live tested by around one hundred students.

We present an ontology for managing the scientific and common names of birds. The ontology is based on the TaxMeOn meta-ontology model for biological names. The ontology is in use as an ontology service and it has been applied in a bird watching system.

Little is known about the dynamics of Linked Data, primarily because there have been few, if any, suitable collections of data made available for analysis of how Linked Data documents evolve over time. We aim to address this issue. We propose the Dynamic Linked Data Observatory, which provides the community with such a collection, monitoring a fixed set of Linked Data documents at weekly intervals. We have now collected eight months of raw data comprising weekly snapshots of eighty thousand Linked Data documents. Having published results characterising the high-level dynamics of Linked Data, we now wish to disseminate results: we wish to investigate how results from our experiment might benefit the community and what online services and statistics (relating to Linked Data dynamics) would be most useful for us to provide.

This paper describes our work towards the implementation of

Mad Swan

, an open-source web-based application that comprises a web service registry, an XML editor, as well as manual and automatic web service composition modules.

Mad Swan

supports various stages of web service composition and tackles the inherent non-determinism of the domain.

This paper discusses the use of semantic technologies to increase quality, machine-processability, format translatability and cross-querying of complex tabular datasets. Our interest is to enable longitudinal studies of social processes in the past, and we use the historical Dutch censuses as case-study. Census data is notoriously difficult to compare, aggregate and query in a uniform fashion. We describe an approach to achieve this, discussing results, trade-offs and open problems.

Mobile user location data has been commercially exploited and studied due to the commoditization of GPS position sensors and the popularity of Location Based Services (LBS). Context researchers have already studied how to understand human mobility using location histories [1], and how to model location context using ontologies [2]. However, these studies make surprisingly little use of rich geospatial data and knowledge about the world to a) explicitly describe user locations, and b) possibly infer implicit contexts. In this paper, we demonstrate that openly accessible geospatial data can facilitate both a) and b), thus resulting in improved understanding of mobile user location context.

In the context of web-scale taxonomies such as Directory Mozilla(

www.dmoz.org

), previous works have shown the existence of power law distribution in the size of the categories for every level in the taxonomy. In this work, we analyse how such high-level semantics can be leveraged to evaluate accuracy of hierarchical classifiers which automatically assign the unseen documents to leaf-level categories. The proposed method offers computational advantages over

k

-fold cross-validation.

Recently, Machine Translation (MT) has become a quite popular technology in everyday use through Web services such as Google Translate. Although the different MT approaches provide good results, none of them exploits contextual information like Named Entity (NE) to help user comprehension.

In this paper, we present NERITS, a machine translation mashup system using semantic annotation from Wikimeta and Linked Open Data (LOD) provided by DBpedia. The goal of the application is to propose a cross-lingual translation by providing detailed information extracted from DBpedia about persons, locations and organizations in the mother tongue of the user. This helps at scaling the traditional multilingual task of machine translation to cross-lingual applications.

The Internet has created a paradigm shift in how we consume and disseminate information. Data nowadays is spread over heterogeneous silos of archived and live data. People willingly share data on social media by posting news, views, presentations, pictures and videos. SNARC is a service that uses semantic web technology and combines services available on the web to aggregate social news. SNARC brings live and archived information to the user that is directly related to his active page. The key advantage is an instantaneous access to complementary information without the need to dig for it. Information appears when it is relevant enabling the user to focus on what is really important.

Fuorisalone during Milano Design Week, with almost three thousands events spread around more than six hundreds venues, attracts half a million visitors: what do they say and feel about those events? Twindex Fuorisalone is a mash-up that listens what all those visitors posted on Twitter and Instragram in that week. In this paper, we briefly report on how Twindex Fuorisalone works and on its ability to listen in real-time the pulse of Fuorisalone on social media.

This paper describes DataConf, a mobile Web mashup application that mixes Linked Data and Web APIs to provide access to different kinds of data. It relies on a widely used JavaScript framework and on a component-based approach to manage different datasources. It only requires static server-side contents and performs all processing on the client side.

DataConf aggregates conference metadata. It allows browsing conference publications, publication authors, authors’ organizations, but also authors’ other publications, publications related to the same keywords, conference schedule or resources related to the conference publications. For this, it queries the SPARQL endpoint that serves the conference dataset, as well as other open or custom endpoints and Web APIs that enrich these data.