The use of narratives as a means of fusing information from knowledge graphs (KGs) into a coherent line of argumentation has been the subject of recent investigation. Narratives are especially useful in event-centric knowledge graphs in that they provide a means to connect different real-world events and categorize them by well-known narrations. However, specifically for controversial events, a problem in information fusion arises, namely, multiple viewpoints regarding the validity of certain event aspects, e.g., regarding the role a participant takes in an event, may exist. Expressing those viewpoints in KGs is challenging because disputed information provided by different viewpoints may introduce inconsistencies . Hence, most KGs only feature a single view on the contained information, hampering the effectiveness of narrative information access. This paper is an extension of our original work and introduces attributions , i.e., parameterized predicates that allow for the representation of facts that are only valid in a specific viewpoint. For this, we develop a conceptual model that allows for the representation of viewpoint-dependent information. As an extension, we enhance the model by a conception of viewpoint-compatibility. Based on this, we deepen our original deliberations on the model’s effects on information fusion and provide additional grounding in the literature.

Abstract State-of-the-art approaches in the field of neural embedding models (NEMs) enable progress in the automatic extraction and prediction of semantic relations between important entities like active substances, diseases, and genes. In particular, the prediction property is making them valuable for important research-related tasks such as hypothesis generation and drug repositioning. A core challenge in the biomedical domain is to have interpretable semantics from NEMs that can distinguish, for instance, between the following two situations: (a) drug x induces disease y and (b) drug x treats disease y. However, NEMs alone cannot distinguish between associations such as treats or induces. Is it possible to develop a model to learn a latent representation from the NEMs capable of such disambiguation? To what extent do we need domain knowledge to succeed in the task? In this paper, we answer both questions and show that our proposed approach not only succeeds in the disambiguation task but also advances current growing research efforts to find real predictions using a sophisticated retrospective analysis. Furthermore, we investigate which type of associations is generally better contextualized and therefore probably has a stronger influence in our disambiguation task. In this context, we present an approach to extract an interpretable latent semantic subspace from the original embedding space in which therapeutic drug–disease associations are more likely .

Graph database query languages feature expressive yet computationally expensive pattern matching capabilities. Answering optional query clauses in SPARQL for instance renders the query evaluation problem immediately PSPACE-complete. Light-weight graph pattern matching relations, such as simulation, have recently been investigated as promising alternatives to more expensive query mechanisms like, e.g., computing subgraph isomorphism. Still, pattern matching alone lacks expressive query capabilities: graph patterns may be combined by usual inner joins. However, including more sophisticated operators is inevitable to make solutions more useful for emerging applications. In this paper we bridge this gap by introducing a new dual simulation process operating on SPARQL queries. In addition to supporting the full syntactic structure of SPARQL queries, it features polynomial-time pattern matching to compute an overapproximation of the query results. Moreover, to achieve running times competing with state-of-the-art database systems, we develop a novel algorithmic solution to dual simulation graph pattern matching, based on a system of inequalities that allows for several optimization heuristics. Finally, we achieve soundness of our process for SPARQL queries including UNION, AND and OPTIONAL operators not restricted to well-designed patterns. Our experiments on synthetic and real-world graph data promise a clear gain for graph database systems when incorporating the new dual simulation techniques.

Skyline queries are a well-established technique for database query personalization and are widely acclaimed for their intuitive query formulation mechanisms. However, when operating on incomplete datasets, skylines queries are severely hampered and often have to resort to highly error-prone heuristics. Unfortunately, incomplete datasets are a frequent phenomenon, especially when datasets are generated automatically using various information extraction or information integration approaches. Here, the recent trend of crowd-enabled databases promises a powerful solution: during query execution, some database operators can be dynamically outsourced to human workers in exchange for monetary compensation, therefore enabling the elicitation of missing values during runtime. Unfortunately, this powerful feature heavily impacts query response times and (monetary) execution costs. In this paper, we present an innovative hybrid approach combining dynamic crowd-sourcing with heuristic techniques in order to overcome current limitations. We will show that by assessing the individual risk a tuple poses with respect to the overall result quality, crowd-sourcing efforts for eliciting missing values can be narrowly focused on only those tuples that may degenerate the expected quality most strongly. This leads to an algorithm for computing skyline sets on incomplete data with maximum result quality, while optimizing crowd-sourcing costs.

By incorporating human workers into the query execution process crowd-enabled databases facilitate intelligent, social capabilities like completing missing data at query time or performing cognitive operators. But despite all their flexibility, crowd-enabled databases still maintain rigid schemas. In this paper, we extend crowd-enabled databases by flexible query-driven schema expansion, allowing the addition of new attributes to the database at query time. However, the number of crowd-sourced mini-tasks to fill in missing values may often be prohibitively large and the resulting data quality is doubtful. Instead of simple crowd-sourcing to obtain all values individually, we leverage the usergenerated data found in the Social Web: By exploiting user ratings we build perceptual spaces , i.e., highly-compressed representations of opinions, impressions, and perceptions of large numbers of users. Using few training samples obtained by expert crowd sourcing, we then can extract all missing data automatically from the perceptual space with high quality and at low costs. Extensive experiments show that our approach can boost both performance and quality of crowd-enabled databases, while also providing the flexibility to expand schemas in a query-driven fashion.

The Web has become the primary source of information containing both structured and unstructured information. A good example is e-commerce where products are usually described by technical specifications (structured data) and textual user reviews (unstructured data). Both sources of information complement each other, covering quantifiable as well as perceived aspects of each product. In fact, for most searches users will have more or less abstract concepts in mind, as opposed to clear cut categorical information. In this paper we develop a novel approach to reveal implicit product features for querying by combining structured product data with natural-language product reviews. Using a self-supervised learning technique we progressively build a query-aware representation of the product domain under consideration. This representation can then effectively be used for intuitive querying. We performed extensive experiments confirming the effectiveness of our approach over real world product data. In particular, our evaluations show vastly improved precision and recall over the respective IR techniques.

Today, Web pages are usually accessed using text search engines, whereas documents stored in the deep Web are accessed through domain-specific Web portals. These portals rely on external knowledge bases, respectively ontologies, mapping documents to more general concepts allowing for suitable classifications and navigational browsing. Since automatically generated ontologies are still not satisfactory for advanced information retrieval tasks, most portals heavily rely on hand-crafted domain-specific ontologies. This, however, also leads to high creation and maintaining costs. On the other hand, a freely available community maintained, if somewhat general, knowledge base is offered by Wikipedia. During the last years the coverage of Wikipedia has reached a large pool of information including articles from almost all domains. In this paper, we investigate the use of Wikipedia categories to describe the content of chemical documents in a compact form. We compare the results to the domain-specific ChEBI ontology and the results show that Wikipedia categories indeed allow useful descriptions for chemical documents that are even better than descriptions from the ChEBI ontology.

When selecting alternatives from large amounts of data, trade-offs play a vital role in everyday decision making. In databases this is primarily reflected by the top-k retrieval paradigm. But recently it has been convincingly argued that it is almost impossible for users to provide meaningful scoring functions for top-k retrieval, subsequently leading to the adoption of the skyline paradigm. Here users just specify the relevant attributes in a query and all suboptimal alternatives are filtered following the Pareto semantics. Up to now the intuitive concept of compensation, however, cannot be used in skyline queries, which also contributes to the often unmanageably large result set sizes. In this paper we discuss an innovative and efficient method for computing skylines allowing the use of qualitative trade-offs. Such trade-offs compare examples from the database on a focused subset of attributes. Thus, users can provide information on how much they are willing to sacrifice to gain an improvement in some other attribute(s). Our contribution is the design of the first skyline algorithm allowing for qualitative compensation across attributes. Moreover, we also provide an novel trade-off representation structure to speed up retrieval. Indeed our experiments show efficient performance allowing for focused skyline sets in practical applications. Moreover, we show that the necessary amount of object comparisons can be sped up by an order of magnitude using our indexing techniques.

Efficient query processing using XPath or XQuery has inspired a lot of research. In contrast to classical exact match retrieval, in today's systems, specifying preferences rather than simple hard constraints is essential. As the structure of XML documents plays a major part in retrieval, recently approximate query matching on structure has received attention. However, query processing of structural user preferences has not yet been considered. In this paper we enable users to express structural preferences and consider the problem of optimizing XML twig queries while preserving the ordering induced on the result set by such user preferences. Evaluating such queries generally needs a rewriting into a set of queries, where each leaf node can be expanded by combinations of structural elements derived from the preference information. Since such structure expansions typically contain redundancies and the efficiency of query evaluation strongly depends on the size of the set of rewritten queries, it is important to identify and simplify necessary expansions. We give a detailed analysis of this process and present an optimization algorithm that determines a minimal set of queries, which in turn are minimal in their expanded nodes, while maintaining the ordering induced by the preference structure. Finally, we provide a comprehensive practical evaluation of our optimization against the XMark benchmark dataset.

In contrast to classical databases and IR systems, real-world information systems have to deal increasingly with very vague and diverse structures for information management and storage that cannot be adequately handled yet. While current object-relational database systems require clear and unified data schemas, IR systems usually ignore the structured information completely. Malleable schemas, as recently introduced, provide a novel way to deal with vagueness, ambiguity and diversity by incorporating imprecise and overlapping definitions of data structures. In this paper, we propose a novel query relaxation scheme that enables users to find best matching information by exploiting malleable schemas to effectively query vaguely structured information. Our scheme utilizes duplicates in differently described data sets to discover the correlations within a malleable schema, and then uses these correlations to appropriately relax the users’ queries. In addition, it ranks results of the relaxed query according to their respective probability of satisfying the original query’s intent. We have implemented the scheme and conducted extensive experiments with real-world data to confirm its performance and practicality.

Unlike numerical preferences, preferences on attribute values do not show an inherent total order, but skyline computation has to rely on partial orderings explicitly stated by the user. In such orders many object values are incomparable, hence skylines sizes become unpractical. However, the Pareto semantics can be modified to benefit from indifferences: skyline result sizes can be essentially reduced by allowing the user to declare some incomparable values as equally desirable. A major problem of adding such equivalences is that they may result in intransitivity of the aggregated Pareto order and thus efficient query processing is hampered. In this paper we analyze how far the strict Pareto semantics can be relaxed while always retaining transitivity of the induced Pareto aggregation. Extensive practical tests show that skyline sizes can indeed be reduced about two orders of magnitude when using the maximum possible relaxation still guaranteeing the consistency with all user preferences

Query processing in traditional information management systems has moved from an exact match model to more flexible paradigms allowing cooperative retrieval by aggregating the database objects' degree of match for each different query predicate and returning the best matching objects only. In peer-to-peer systems such strategies are even more important, given the potentially large number of peers, which may contribute to the results. Yet current peer-to-peer research has barely started to investigate such approaches. In this paper we discuss the benefits of best match/top-k queries in the context of distributed peer-to-peer information infrastructures and show how to extend the limited query processing in current peer-to-peer networks by allowing the distributed processing of top-k queries, while maintaining a minimum of data traffic. Relying on a super-peer backbone organized in the HyperCuP topology we show how to use local indexes for optimizing the necessary query routing and how to process intermediate results in inner network nodes at the earliest possible point in time cutting down the necessary data traffic within the network. Our algorithm is based on dynamically collected query statistics only, no continuous index update processes are necessary, allowing it to scale easily to large numbers of peers, as well as dynamic additions/deletions of peers. We show our approach to always deliver correct result sets and to be optimal in terms of necessary object accesses and data traffic. Finally, we present simulation results for both static and dynamic network environments.

Enabling mobility in urban and populous areas needs innovative tools and novel techniques for individual traffic planning. Represent a prototype of a traffic information system enabling personalized route planning plus advanced services like traffic jam alerting. The best routes are efficiently computed using the SR-Combine algorithm, subject to various user preferences and current traffic situation gathered dynamically from several Internet sources. We implemented a J2EE application server which smoothly adapts to distributed online processing, once high bandwidth networks like UTMS are available.