A General Framework for Representing, Reasoning and Querying with Annotated Semantic Web Data

We describe a generic framework for representing and reasoning with annotated Semantic Web data, a task becoming more important with the recent increased amount of inconsistent and non-reliable meta-data on the web. We formalise the annotated language, the corresponding deductive system and address the query answering problem. Previous contributions on specific RDF annotation domains are encompassed by our unified reasoning formalism as we show by instantiating it on (i) temporal, (ii) fuzzy, and (iii) provenance annotations. Moreover, we provide a generic method for combining multiple annotation domains allowing to represent, e.g. temporally-annotated fuzzy RDF. Furthermore, we address the development of a query language -- AnQL -- that is inspired by SPARQL, including several features of SPARQL 1.1 (subqueries, aggregates, assignment, solution modifiers) along with the formal definitions of their semantics

A survey of large-scale reasoning on the Web of data

As more and more data is being generated by sensor networks, social media and organizations, the Webinterlinking this wealth of information becomes more complex. This is particularly true for the so-calledWeb of Data, in which data is semantically enriched and interlinked using ontologies. In this large anduncoordinated environment, reasoning can be used to check the consistency of the data and of asso-ciated ontologies, or to infer logical consequences which, in turn, can be used to obtain new insightsfrom the data. However, reasoning approaches need to be scalable in order to enable reasoning over theentire Web of Data. To address this problem, several high-performance reasoning systems, whichmainly implement distributed or parallel algorithms, have been proposed in the last few years. Thesesystems differ significantly; for instance in terms of reasoning expressivity, computational propertiessuch as completeness, or reasoning objectives. In order to provide afirst complete overview of thefield,this paper reports a systematic review of such scalable reasoning approaches over various ontologicallanguages, reporting details about the methods and over the conducted experiments. We highlight theshortcomings of these approaches and discuss some of the open problems related to performing scalablereasoning

Linked Data Entity Summarization

On the Web, the amount of structured and Linked Data about entities is constantly growing. Descriptions of single entities often include thousands of statements and it becomes difficult to comprehend the data, unless a selection of the most relevant facts is provided. This doctoral thesis addresses the problem of Linked Data entity summarization. The contributions involve two entity summarization approaches, a common API for entity summarization, and an approach for entity data fusion

Methods for Matching of Linked Open Social Science Data

In recent years, the concept of Linked Open Data (LOD), has gained popularity and acceptance across various communities and domains. Science politics and organizations claim that the potential of semantic technologies and data exposed in this manner may support and enhance research processes and infrastructures providing research information and services. In this thesis, we investigate whether these expectations can be met in the domain of the social sciences. In particular, we analyse and develop methods for matching social scientific data that is published as Linked Data, which we introduce as Linked Open Social Science Data. Based on expert interviews and a prototype application, we investigate the current consumption of LOD in the social sciences and its requirements. Following these insights, we first focus on the complete publication of Linked Open Social Science Data by extending and developing domain-specific ontologies for representing research communities, research data and thesauri. In the second part, methods for matching Linked Open Social Science Data are developed that address particular patterns and characteristics of the data typically used in social research. The results of this work contribute towards enabling a meaningful application of Linked Data in a scientific domain

User interfaces supporting entity search for linked data

One of the main goals of semantic search is to retrieve and connect information related to queries, offering users rich structured information about a topic instead of a set of documents relevant to the topic. Previous work reports that searching for information about individual entities such as persons, places and organisations is the most common form of Web search. Since the Semantic Web was first proposed, the amount of structured data on the Web has increased dramatically. This is particularly the case for what is known as Linked Data, information that has been published using Semantic Web standards such as RDF and OWL. Such structured data opens up new possibilities for improving entity search on the Web, integrating facts from independent sources, and presenting users with contextually-rich information about entities. This research focuses on entity search of Linked Data in terms of three different forms of search: structured queries, where users can use the SPARQL query language for manipulating data sources; exploratory search, where users can browse from one entity to another; and focused search, where users can input an entity query as a free text keyword search. We undertake a comparative study between two distinct information architectures for structured querying to manipulate Linked Data over the Web. Specifically, we evaluate some of the main operators in SPARQL using several datasets of Linked Data. We introduce a framework of five criteria to evaluate 15 current state-of-the-art semantic tools available for exploratory search of Linked Data, in order to establish how well these browsers make available the benefits of Linked Data and entity search for human users. We also use the criteria to determine the browsers that are best suited to entity exploration. Further, we propose a new model, the Attribute Importance Model, for entity-aggregated search, with the purpose of improving user experience when finding information about entities. The model develops three techniques: (1) presenting entity type-based query suggestions; (2) clustering aggregated attributes; and (3) ranking attributes based on their importance to a given query. Together these constitute a model for developing more informative views and enhancing users’ understanding of entity descriptions on the Web. We then use our model to provide an interactive approach, with the Information Visualisation toolkit InfoVis, that enables users to visualise entity clusters generated by our Attribute Importance Model. Thus this thesis addresses two challenges of searching Linked Data. The first challenge concerns the specific issue of information resolution during the search: the reduction of query ambiguity and redundant results that contain irrelevant descriptions when searching for information about an entity. The second challenge concerns the more general problem of technical complexity, and addresses to the limited adoption of Linked Data that we ascribe to the lack of understanding of Semantic Web technologies and data structures among general users. These technologies pose new design problems for human interaction such as overloading data, navigation styles, and browsing mechanisms. The Attribute Importance Model addresses both these challenges

Automated Knowledge Base Extension Using Open Information

Open Information Extractions (OIE) (like Nell, Reverb) frameworks provide us with domain independent facts in natural language forms containing knowledge from varied sources. Extraction mechanisms for structured knowledge bases (KB) (like DBpedia, Yago) often fail to retrieve such facts due to its resource specific extraction schemes. Hence, the structured KBs can extend themselves by augmenting their coverage with the facts discovered by OIE systems. This possibility motivates us to integrate these two genres of extractions into one interactive framework. In this work, we present a complete, ontology independent, generalized architecture for achieving this integration. Our proposed solution is modularized which solves a specific set of tasks: (1) mapping subject and object terms from OIE facts to KB instances (2) mapping the OIE relational phrases to object properties defined in the KB. Furthermore, in an open extraction setting identical semantic relationships can be represented by different surface forms, making it necessary to group them together. To solve this problem, (3) we propose the use of markov clustering to cluster OIE relations. Key to our approach lies in exploiting the inherent dependancies between relations and its arguments. This makes our approach completely context agnostic and generally applicable. We evaluated our method on the two state of the art extraction systems, achieving over 85% precision on instance mappings and over 90% for the relation mappings. We also created a distant supervision based gold standard for the purpose and the data has been released as part of this work. Furthermore, we analyze the effect of clustering and empirically show its effectiveness as a relation mapping technique over other techniques. Overall, our work positions itself on the intersection of information extraction, ontology mapping and reasoning

Adaptive and Reactive Rich Internet Applications

In this thesis we present the client-side approach of Adaptive and Reactive Rich Internet Applications as the main result of our research into how to bring in time adaptivity to Rich Internet Applications. Our approach leverages previous work on adaptive hypermedia, event processing and other research disciplines. We present a holistic framework covering the design-time as well as the runtime aspects of Adaptive and Reactive Rich Internet Applications focusing especially on the run-time aspects