Visualisation of semantic enrichment

Automatically creating semantic enrichments for text may lead to annotations that allow for excellent recall but poor precision. Manual enrichment is potentially more targeted, leading to greater precision. We aim to support nonexperts in manually enriching texts with semantic annotations. Neither the visualisation of semantic enrichment nor the process of manually enriching texts has been evaluated before. This paper presents the results of our user study on visualisation of text enrichment during the annotation process. We performed extensive analysis of work related to the visualisation of semantic annotations. In a prototype implementation, we then explored two layout alternatives for visualising semantic annotations and their linkage to the text atoms. Here we summarise and discuss our results and their design implications for tools creating semantic annotations

BIM semantic-enrichment for built heritage representation

In the built heritage context, BIM has shown difficulties in representing and managing the large and complex knowledge related to non-geometrical aspects of the heritage. Within this scope, this paper focuses on a domain-specific semantic-enrichment of BIM methodology, aimed at fulfilling semantic representation requirements of built heritage through Semantic Web technologies. To develop this semantic-enriched BIM approach, this research relies on the integration of a BIM environment with a knowledge base created through information ontologies. The result is knowledge base system - and a prototypal platform - that enhances semantic representation capabilities of BIM application to architectural heritage processes. It solves the issue of knowledge formalization in cultural heritage informative models, favouring a deeper comprehension and interpretation of all the building aspects. Its open structure allows future research to customize, scale and adapt the knowledge base different typologies of artefacts and heritage activities

Semi-automatic semantic enrichment of raw sensor data

One of the more recent sources of large volumes of generated data is sensor devices, where dedicated sensing equipment is used to monitor events and happenings in a wide range of domains, including monitoring human biometrics. In recent trials to examine the effects that key moments in movies have on the human body, we fitted fitted with a number of biometric sensor devices and monitored them as they watched a range of dierent movies in groups. The purpose of these experiments was to examine the correlation between humans' highlights in movies as observed from biometric sensors, and highlights in the same movies as identified by our automatic movie analysis techniques. However,the problem with this type of experiment is that both the analysis of the video stream and the sensor data readings are not directly usable in their raw form because of the sheer volume of low-level data values generated both from the sensors and from the movie analysis. This work describes the semi-automated enrichment of both video analysis and sensor data and the mechanism used to query the data in both centralised environments, and in a peer-to-peer architecture when the number of sensor devices grows to large numbers. We present and validate a scalable means of semi-automating the semantic enrichment of sensor data, thereby providing a means of large-scale sensor management

Bridging the gap between folksonomies and the semantic web: an experience report

Abstract. While folksonomies allow tagging of similar resources with a variety of tags, their content retrieval mechanisms are severely hampered by being agnostic to the relations that exist between these tags. To overcome this limitation, several methods have been proposed to find groups of implicitly inter-related tags. We believe that content retrieval can be further improved by making the relations between tags explicit. In this paper we propose the semantic enrichment of folksonomy tags with explicit relations by harvesting the Semantic Web, i.e., dynamically selecting and combining relevant bits of knowledge from online ontologies. Our experimental results show that, while semantic enrichment needs to be aware of the particular characteristics of folksonomies and the Semantic Web, it is beneficial for both.

Semantic enrichment of GPS trajectories

Semantic annotation of GPS trajectories helps us to recognize the interests of the creator of the GPS trajectories. Automating this trajectory annotation circumvents the requirement of additional user input. To annotate the GPS traces automatically, two types of automated input are required: 1) a collection of possible annotations, and 2) a collection of GPS trajectories to annotate.\ud \ud The first type of input can be a set of points of interest (POIs), activities, weather types, etc. This collection is to be provided by an application developer, and can originate from the web, an external knowledge base, or an existing database, for example.\ud \ud The type of annotation that we are interested in, is annotation with visited locations, in order to create a user profile at a later stage. We have collected POIs by scraping the web, using a self-configuring data harvester. This harvester is based on workflows, enabling us to add or remove certain steps for different goals of harvesting.\ud \ud The result of our harvesting approach consists of a set of 27,384 POIs, origining from the Dutch Yellow Pages \cite{goudengids2012, and contains an address and a geographical point representation for each POI. These point representations cannot be used to overlay the GPS trajectories directly, and therefore need to be converted into a polygon before providing useful input for the annotation process.\ud \ud Several different approaches to this problem can be thought of, including Voronoi diagrams, nearest-neighbors, and geocoding the addresses of the assumed neighbors. For each of the POI footprint size estimation approaches, the output consists of two parts: 1) a polygon representing the estimated parcel, and 2) an uncertainty function based on the distance to the center of the polygon. These approaches are being validated with cadastral data for the region of Enschede, The Netherlands, and the result of the best approach is used as the input for the GPS trajectory enrichment.\ud \ud The other type of input for the enrichment process is GPS trajectories. This data is generally not smooth, containing outliers, and interruptions of the data stream. Analyzing these imperfections however, may provide valuable information on users entering a rural area, or buildings, respectively.\ud \ud Combining the results of the footprint size estimation with the analyzed GPS trajectory then provides us with uncertain annotated GPS traces

Semantic Enrichment of Ontology Mappings

Schema and ontology matching play an important part in the field of data integration and semantic web. Given two heterogeneous data sources, meta data matching usually constitutes the first step in the data integration workflow, which refers to the analysis and comparison of two input resources like schemas or ontologies. The result is a list of correspondences between the two schemas or ontologies, which is often called mapping or alignment. Many tools and research approaches have been proposed to automatically determine those correspondences. However, most match tools do not provide any information about the relation type that holds between matching concepts, for the simple but important reason that most common match strategies are too simple and heuristic to allow any sophisticated relation type determination. Knowing the specific type holding between two concepts, e.g., whether they are in an equality, subsumption (is-a) or part-of relation, is very important for advanced data integration tasks, such as ontology merging or ontology evolution. It is also very important for mappings in the biological or biomedical domain, where is-a and part-of relations may exceed the number of equality correspondences by far. Such more expressive mappings allow much better integration results and have scarcely been in the focus of research so far. In this doctoral thesis, the determination of the correspondence types in a given mapping is the focus of interest, which is referred to as semantic mapping enrichment. We introduce and present the mapping enrichment tool STROMA, which obtains a pre-calculated schema or ontology mapping and for each correspondence determines a semantic relation type. In contrast to previous approaches, we will strongly focus on linguistic laws and linguistic insights. By and large, linguistics is the key for precise matching and for the determination of relation types. We will introduce various strategies that make use of these linguistic laws and are able to calculate the semantic type between two matching concepts. The observations and insights gained from this research go far beyond the field of mapping enrichment and can be also applied to schema and ontology matching in general. Since generic strategies have certain limits and may not be able to determine the relation type between more complex concepts, like a laptop and a personal computer, background knowledge plays an important role in this research as well. For example, a thesaurus can help to recognize that these two concepts are in an is-a relation. We will show how background knowledge can be effectively used in this instance, how it is possible to draw conclusions even if a concept is not contained in it, how the relation types in complex paths can be resolved and how time complexity can be reduced by a so-called bidirectional search. The developed techniques go far beyond the background knowledge exploitation of previous approaches, and are now part of the semantic repository SemRep, a flexible and extendable system that combines different lexicographic resources. Further on, we will show how additional lexicographic resources can be developed automatically by parsing Wikipedia articles. The proposed Wikipedia relation extraction approach yields some millions of additional relations, which constitute significant additional knowledge for mapping enrichment. The extracted relations were also added to SemRep, which thus became a comprehensive background knowledge resource. To augment the quality of the repository, different techniques were used to discover and delete irrelevant semantic relations. We could show in several experiments that STROMA obtains very good results w.r.t. relation type detection. In a comparative evaluation, it was able to achieve considerably better results than related applications. This corroborates the overall usefulness and strengths of the implemented strategies, which were developed with particular emphasis on the principles and laws of linguistics

X-ENS: Semantic Enrichment of Web Search Results at Real-Time

While more and more semantic data are published on the Web, an important question is how typical web users can access and exploit this body of knowledge. Although, existing interaction paradigms in semantic search hide the complexity behind an easy-to-use interface, they have not managed to cover common search needs. In this paper, we present X-ENS (eXplore ENtities in Search), a web search application that enhances the classical, keyword-based, web searching with semantic information, as a means to combine the pros of both Semantic Web standards and common Web Searching. X-ENS identifies entities of interest in the snippets of the top search results which can be further exploited in a faceted interaction scheme, and thereby can help the user to limit the - often very large - search space to those hits that contain a particular piece of information. Moreover, X-ENS permits the exploration of the identified entities by exploiting semantic repositories