In this paper, we propose a model to operationalise serendipity in content-based recommender systems. The model, called SIRUP, is inspired by the Silvia's curiosity theory, based on the fundamental theory of Berlyne, aims at (1) measuring the novelty of an item with respect to the user profile, and (2) assessing whether the user is able to manage such level of novelty (coping potential). The novelty of items is calculated with cosine similarities between items, using Linked Open Data paths. The coping potential of users is estimated by measuring the diversity of the items in the user profile. We deployed and evaluated the SIRUP model in a use case with TV recommender using BBC programs dataset. Results show that the SIRUP model allows us to identify serendipitous recommendations, and, at the same time, to have 71% precision.

Spreadsheets models are frequently used by scientists to analyze research data. These models are typically described in a paper or a report, which serves as single source of information on the underlying research project. As the calculation workflow in these models is not made explicit, readers are not able to fully understand how the research results are calculated, and trace them back to the underlying spreadsheets. This paper proposes a methodology for semi-automatically deriving the calculation workflow underlying a set of spreadsheets. The starting point of our methodology is the cell dependency graph, representing all spreadsheet cells and connections. We automatically aggregate all cells in the graph that represent instances and duplicates of the same quantities, based on analysis of the formula syntax. Subsequently, we use a set of heuristics, incorporating knowledge on spreadsheet design, computational procedures and domain knowledge, to select those quantities, that are relevant for understanding the calculation workflow. We explain and illustrate our methodology by actually applying it on three sets of spreadsheets from existing research projects in the domains of environmental and life science. Results from these case studies show that our constructed calculation models approximate the ground truth calculation workflows, both in terms of content and size, but are not a perfect match.

No abstract available.

Simple Knowledge Organization System (SKOS) provides a data model and vocabulary for expressing Knowledge Organization Systems (KOSs) such as thesauri and classification schemes in Semantic Web applications. This paper presents the main components of SKOS and their formal expression in Web Ontology Language (OWL), providing an extensive account of the design decisions taken by the Semantic Web Deployment (SWD) Working Group of the World Wide Web Consortium (W3C), which between 2006 and 2009 brought SKOS to the status of W3C Recommendation. The paper explains key design principles such as “minimal ontological commitment” and systematically cites the requirements and issues that influenced the design of SKOS components. By reconstructing the discussion around alternative features and design options and presenting the rationale for design decisions, the paper aims at providing insight into how SKOS turned out as it did, and why. Assuming that SKOS, like any other successful technology, may eventually be subject to revision and improvement, the critical account offered here may help future editors approach such a task with deeper understanding.

Bridging the gap between low-level features and semantics is a problem commonly acknowledged in the Multimedia community. Event modeling can fill this gap by representing knowledge about the data at different level of abstraction. In this paper we present the Simple Event Model (SEM) and its application in a Maritime Safety and Security use case about Situational Awareness, where the data also come as low-level features (of ship trajectories). We show how we abstract over these low-level features, recognize simple behavior events using a Piecewise Linear Segmentation algorithm, and model the resulting events as instances of SEM. We aggregate web data from different sources, apply deduction rules, spatial proximity reasoning, and semantic web reasoning in SWI-Prolog to derive abstract events from the recognized simple events. The use case described in this paper comes from the Dutch Poseidon project.

Recently, various crowdsourcing initiatives showed that targeted efforts of \nuser communities result in massive amounts of tags. For example, the \nNetherlands Institute for Sound and Vision collected a large number of tags \nwith the video labeling game \\emph{Waisda?}. To successfully utilize these \ntags, a better understanding of their characteristics is required. \nThe goal of this paper is twofold: (i) to investigate the vocabulary that \nusers employ when describing videos and compare it to the vocabularies used by \nprofessionals; and (ii) to establish which aspects of the video are typically \ndescribed and what type of tags are used for this. We report on an analysis of \nthe tags collected with \\emph{Waisda?}. With respect to the first goal, we \ncompared the the tags with a typical domain thesaurus used by professionals, \nas well as with a more general vocabulary. With respect to the second goal, we \ncompare the tags to the video subtitles to determine how many tags are derived \nfrom the audio signal. In addition, we perform a qualitative study in which a \ntag sample is interpreted in terms of an existing annotation classification \nframework. The results suggest that the tags complement the metadata provided \nby professional cataloguers, the tags describe both the audio and the visual \naspects of the video, and the users primarily describe objects in the video \nusing general descriptions.

There is an abundance of semi-structured reports on events being written and made available on the World Wide Web on a daily basis. These reports are primarily meant for human use. A recent movement is the addition of RDF metadata to make automatic processing by computers easier. A fine example of this movement is the Open Government Data initiative which, by adding RDF meta-data to spreadsheets and textual reports, strives to speed up the creation of geographical mashups and visual analytics applications. In this paper, we present a new Open Linked Data RDF dataset and a method for automatically adding such RDF metadata to semi-structured reports. We showcase our method on piracy attack reports issued on the web by the International Chamber of Commerce's International Maritime Bureau (ICC-CCS IMB). We create a Semantic Web representation with the Simple Event Model (SEM) from screen scrapes of the ICC-CCS website. We show how the event layer makes it possible to easily analyze and visualize the aggregated reports to answer domain questions. Our pipeline includes conversion of the reports to RDF, linking their parts to external resources from the Linked Open Data cloud and exposing them to the Web through a ClioPatria web server that hosts the RDF.

We present a Linked Data analysis method which relies on knowledge patterns for constructing a logical architecture of the knowledge in a dataset. This can then be exploited to compare heterogeneous datasets, enhance interoperability between them and make implicit knowledge emerge.

Within cultural heritage collections, objects are often grounded in a particular historical setting. This setting can currently not be made explicit, as structured descriptions of events are either missing or not marked up explicitly. This paper reports a study on automatic extraction of an historical event thesaurus from unstructured texts. We show how this preliminary thesaurus accommodates event- and object-driven search and browsing of two cultural heritage collections.

Gold standard mappings created by experts are at the core of alignment evaluation. At the same time, the process of manual evaluation is rarely discussed. While the practice of having multiple raters evaluate results is accepted, their level of agreement is often not measured. In this paper we describe three experiments in manual evaluation and study the way different raters evaluate mappings. We used alignments generated using different techniques and between vocabularies of different type. In each experiment, five raters evaluated alignments and talked through their decisions using the think aloud method. In all three experiments we found that inter-rater agreement was low and analyzed our data to find the reasons for it. Our analysis shows which variables can be controlled to affect the level of agreement including the mapping relations, the evaluation guidelines and the background of the raters. On the other hand, differences in the perception of raters, and the complexity of the relations between often ill-defined natural language concepts remain inherent sources of disagreement. Our results indicate that the manual evaluation of ontology alignments is by no means an easy task and that the ontology alignment community should be careful in the construction and use of reference alignments.

It is common practice in audiovisual archives to disclose documents using metadata from a structured vocabulary or thesaurus. Many of these thesauri have limited or no structure. The objective of this paper is to find out whether retrieval of audiovisual resources from a collection indexed with an in-house thesaurus can be improved by enriching the thesaurus structure. We propose a method to add structure to a thesaurus by anchoring it to an external, semantically richer thesaurus. We investigate the added value of this enrichment for retrieval purposes. We first anchor the thesaurus to an external resource, WordNet. From this anchoring we infer relations between pairs of terms in the thesaurus that were previously unrelated. We employ the enriched thesaurus in a retrieval experiment on a TRECVID 2007 dataset. The results are promising: with simple techniques we are able to enrich a thesaurus in such a way that it adds to retrieval performance.

Identifying alignments between vocabularies has become a central knowledge engineering activity. A plethora of alignment techniques has been developed over the past years. In this paper we present a case study in which we examine and evaluate the practical use of three typical alignment techniques. The study involves the alignment of two vocabularies used in a semantic-search engine for cultural-heritage objects. We show that a sequence can be beneficial. The case study gives insight into evaluation issues, such as techniques for identification of false positives. We see this work as a step to a badly-needed methodology for alignment.

No abstract available.

In this paper we describe a methodological approach for porting cultural repositories to the Semantic Web, focusing on the global picture of the required mappings and alignments.

In this paper, we propose an automatic video retrieval method based on high-level concept detectors. Research in video analysis has reached the point where over 100 concept detectors can be learned in a generic fashion, albeit with mixed performance. Such a set of detectors is very small still compared to ontologies aiming to capture the full vocabulary a user has. We aim to throw a bridge between the two fields by building a multimedia thesaurus, i.e., a set of machine learned concept detectors that is enriched with semantic descriptions and semantic structure obtained from WordNet. Given a multimodal user query, we identify three strategies to select a relevant detector from this thesaurus, namely: text matching, ontology querying, and semantic visual querying. We evaluate the methods against the automatic search task of the TRECVID 2005 video retrieval benchmark, using a news video archive of 85 h in combination with a thesaurus of 363 machine learned concept detectors. We assess the influence of thesaurus size on video search performance, evaluate and compare the multimodal selection strategies for concept detectors, and finally discuss their combined potential using oracle fusion. The set of queries in the TRECVID 2005 corpus is too small for us to be definite in our conclusions, but the results suggest promising new lines of research.

The six papers in this special section focus on semantic image and video indexing in broad domains. To bring semantics to the user in broad domains both the indexing and retrieval step have to be considered. The papers here address both steps and the relation to ontologies.

Thesauri such as the Art and Architecture Thesaurus (AAT) provide structured vocabularies for describing art objects. However, if we want to create a knowledge-rich description of an (image of an) art object, such as required by the "semantic web", thesauri turn out to provide only part of the knowledge needed. In this paper we look at problems related to capturing background knowledge for art resources. We describe a case study in which we attempt to construct an ontology for a subset of art-object descriptions, namely antique furniture, using AAT as well as metadata standards as input. We discuss the representation requirements for such an ontology as well as representational problems for our sample ontology with respect to the emerging web standards for knowledge representation (RDF, RDFS, OIL).