Willem Robert van Hage

Design and use of the Simple Event Model (SEM)

Events have become central elements in the representation of data from domains such as history, cultural heritage, multimedia and geography. The Simple Event Model (SEM) is created to model events in these various domains, without making assumptions about the domain-specific vocabularies used. SEM is designed with a minimum of semantic commitment to guarantee maximal interoperability. In this paper, we discuss the general requirements of an event model for web data and give examples from two use cases: historic events and events in the maritime safety and security domain. The advantages and disadvantages of several existing event models are discussed in the context of the historic example. We discuss the design decisions underlying SEM. SEM is coupled with a Prolog API that enables users to create instances of events without going into the details of the implementation of the model. By a tight coupling to existing Prolog packages, the API facilitates easy integration of event instances to Linked Open Data. We illustrate use of the API with examples from the maritime domain.

A method to combine linguistic ontology-mapping techniques

We discuss four linguistic ontology-mapping techniques and evaluate them on real-life ontologies in the domain of food. Furthermore we propose a method to combine ontology-mapping techniques with high Precision and Recall to reduce the necessary amount of manual labor and computation.

Mobile cultural heritage guide: location-aware semantic search

In this paper we explore the use of location aware mobile devices for searching and browsing a large number of general and cultural heritage information repositories. Based on GPS positioning we can determine a users location and context, composed of physical nearby locations, historic events that have taken place there, artworks that were created at or inspired by those locations and artists that have lived or worked there. Based on a geolocation, the user has three levels of refinement: pointing to a specific heading and selection and facets and subfacets of cultural heritage objects. In our approach two types of knowledge are combined: general knowledge about geolocations and points of interest and specialized knowledge about a particular domain, i.e. cultural heritage. We use a number of Linked Open Data sources and a number of general sources from the cultural heritage domain (including Art and Architecture Thesaurus, Union List of Artist Names) as well as data from several Dutch cultural institutions. We show three concrete scenarios where a tourist accesses localized information on his iPhone about the current environment, events, artworks or persons, which are enriched by Linked Open Data sources. We show that Linked Open Data sources in isolation are currently too limited to provide interesting semantic information but combined with each other and with a number of other sources a really informative location-based service can be created.

A method for learning part-whole relations

Part-whole relations are important in many domains, but typically receive less attention than subsumption relation. In this paper we describe a method for finding part-whole relations. The method consists of two steps: (i) finding phrase patterns for both explicit and implicit part-whole relations, and (ii) applying these patterns to find part-whole relation instances. We show results of applying this method to a domain of finding sources of carcinogens.

Finding your way through the rijksmuseum with an adaptive mobile museum guide

This paper describes a real-time routing system that implements a mobile museum tour guide for providing personalized tours tailored to the user position inside the museum and interests. The core of this tour guide originates from the CHIP (Cultural Heritage Information Personalization) Web-based tools set for personalized access to the Rijksmuseum Amsterdam collection. In a number of previous papers we presented these tools for interactive discovery of users interests, semantic recommendations of artworks and art-related topics, and the (semi-)automatic generation of personalized museum tours. Typically, a museum visitor could wander around the museum and get attracted by artworks outside of the current tour he is following. To support a dynamic adaptation of the tour to the current user position and changing interests, we have extended the existing CHIP mobile tour guide with a routing mechanism based on the SWI-Prolog Space package. The package uses (1) the CHIP user profile containing users preferences and current location; (2) the semantically enriched Rijksmuseum collection and (3) the coordinates of the artworks and rooms in the museum. This is a joint work between the Dutch nationally funded CHIP and Poseidon projects and the prototype demonstrator can be found at http://www.chip-project.org/spacechip.

MultiFarm: A benchmark for multilingual ontology matching

In this paper we present the MultiFarm dataset, which has been designed as a benchmark for multilingual ontology matching. The MultiFarm dataset is composed of a set of ontologies translated in different languages and the corresponding alignments between these ontologies. It is based on the OntoFarm dataset, which has been used successfully for several years in the Ontology Alignment Evaluation Initiative (OAEI). By translating the ontologies of the OntoFarm dataset into eight different languages-Chinese, Czech, Dutch, French, German, Portuguese, Russian, and Spanish-we created a comprehensive set of realistic test cases. Based on these test cases, it is possible to evaluate and compare the performance of matching approaches with a special focus on multilingualism.

An integrated approach for visual analysis of a multisource moving objects knowledge base

We present an integrated and multidisciplinary approach for analyzing the behavior of moving objects. The results originate from an ongoing research of four different partners from the Dutch Poseidon project (Embedded Systems Institute (2007)), which aims to develop new methods for Maritime Safety and Security (MSS) systems to monitor vessel traffic in coastal areas. Our architecture enables an operator to visually test hypotheses about vessels with time-dependent sensor data and on-demand external knowledge. The system includes the following components: abstraction and simulation of trajectory sensor data, fusion of multiple heterogenous data sources, reasoning, and visual analysis of the combined data sources. We start by extracting segments of consistent movement from simulated or real-world trajectory data, which we store as instances of the Simple Event Model (SEM), an event ontology represented in the Resource Description Framework (RDF). Next, we add data from the web about vessels and geography to enrich the sensor data. This additional information is integrated with the representation of the vessels (actors) and places in SEM. The enriched trajectory data are stored in a knowledge base, which can be further annotated by reasoning and is queried by a visual analytics tool to search for spatiotemporal patterns. Although our approach is dedicated to MSS systems, we expect it to be useful in other domains.

Combining ship trajectories and semantics with the simple event model (SEM)

Bridging the gap between low-level features and semantics is a problem commonly acknowledged in the Multimedia community. Event modeling can fill the gap. In this paper we present the Simple Event Model (SEM) and its application in a Maritime Safety and Security use case about Situational Awareness. We show how we abstract over low-level features, recognize simple behavior events using a Piecewise Linear Segmentation algorithm, and model the events as instances of SEM. We 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 come from the Dutch Poseidon project.

The Space Package: Tight Integration between Space and Semantics

Interpretation of spatial features often requires combined reasoning over geometry and semantics. We introduce the Space package, an open source SWI-Prolog extension that provides spatial indexing capabilities. Together with the existing semantic web reasoning capabilities of SWI-Prolog, this allows efficient integration of spatial and semantic queries and provides an infrastructure for declarative programming with space and semantics. There are few systems that provide indexing and reasoning facilities for both spatial and semantic data. A common solution is to combine separate semantic reasoning and geospatial services. Such loose coupling has the disadvantage that each service cannot make use of the statistics of the other. This makes optimization of such a service-oriented architecture hard. The SWI-Prolog Space and Semantic web packages provide a native Prolog interface to both spatial and semantic indexing and reasoning, which makes it easy to write combined query optimizers. Another advantage of the Space package is that it allows declarative logic programming, which means in practice that you say what you want to compute instead of how to compute it. The actual indexing machinery is encapsulated inside Prolog predicates. In this article we describe the interface of the Space package, compare its functionality to alternative software libraries, and show how to work with it using three example applications. These example illustrations include reasoning over movement patterns, dynamically loading geospatial linked data off the semantic web, and setting up a simple KML server.

Abstracting and reasoning over ship trajectories and web data with the Simple Event Model (SEM)

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.