Question answering (QA) has been the subject of a resurgence over the past years. The said resurgence has led to a multitude of question answering (QA) systems being developed both by companies and research facilities. While a few components of QA systems get reused across implementations, most systems do not leverage the full potential of component reuse. Hence, the development of QA systems is currently still a tedious and time-consuming process. We address the challenge of accelerating the creation of novel or tailored QA systems by presenting a concept for a self-wiring approach to composing QA systems. Our approach will allow the reuse of existing, web-based QA systems or modules while developing new QA platforms. To this end, it will rely on QA modules being described using the Web Ontology Language. Based on these descriptions, our approach will be able to automatically compose QA systems using a data-driven approach automatically.
@cite_8 described a first semantic approach towards coupling components together via RDF to tailor search pipelines using semantic, geospatial and full text search modules. Here, modules add semantic information to a query until the search intend can be solved.
@cite_8
2296121499 2112671715 2521441617 2741223254
Resource Description Framework (RDF) is the commonly used format for Semantic Web data. Nowadays, huge amounts of data on the Internet in the RDF format are used by search engines for providing answers to the queries of users. Querying through big data needs suitable searching methods supported by a very high processing power, because the traditional, sequential keyword matching on a semantic web server may take a prohibitively long time. In this paper, we aim at accelerating the search in big RDF data by exploiting modern many-core architectures based on Graphics Processing Units (GPUs). We develop several implementations of the RDF search for many-core architectures using two programming approaches: OpenMP for systems with CPUs and CUDA for systems comprising CPUs and GPUs. Experiments show that our approach is 20.5 times faster than the sequential search. Traditional text mining techniques transform free text into flat bags of words representation, which does not preserve sufficient semantics for the purpose of knowledge discovery. In this paper, we present a two-step procedure to mine generalized associations of semantic relations conveyed by the textual content of Web documents. First, RDF (resource description framework) metadata representing semantic relations are extracted from raw text using a myriad of natural language processing techniques. The relation extraction process also creates a term taxonomy in the form of a sense hierarchy inferred from WordNet. Then, a novel generalized association pattern mining algorithm (GP-Close) is applied to discover the underlying relation association patterns on RDF metadata. For pruning the large number of redundant overgeneralized patterns in relation pattern search space, the GP-Close algorithm adopts the notion of generalization closure for systematic overgeneralization reduction. The efficacy of our approach is demonstrated through empirical experiments conducted on an online database of terrorist activities With the success of Open Data a huge amount of tabular data sources became available that could potentially be mapped and linked into the Web of (Linked) Data. Most existing approaches to “semantically label” such tabular data rely on mappings of textual information to classes, properties, or instances in RDF knowledge bases in order to link – and eventually transform – tabular data into RDF. However, as we will illustrate, Open Data tables typically contain a large portion of numerical columns and or non-textual headers; therefore solutions that solely focus on textual “cues” are only partially applicable for mapping such data sources. We propose an approach to find and rank candidates of semantic labels and context descriptions for a given bag of numerical values. To this end, we apply a hierarchical clustering over information taken from DBpedia to build a background knowledge graph of possible “semantic contexts” for bags of numerical values, over which we perform a nearest neighbour search to rank the most likely candidates. Our evaluation shows that our approach can assign fine-grained semantic labels, when there is enough supporting evidence in the background knowledge graph. In other cases, our approach can nevertheless assign high level contexts to the data, which could potentially be used in combination with other approaches to narrow down the search space of possible labels. Semi-structured data models like the Resource Description Framework (RDF), naturally allow for modeling the same real-world entity in various ways. For example, different RDF vocabularies enable the definition of various RDF graphs representing the same drug in Bio2RDF or Drugbank. Albeit semantically equivalent, these RDF graphs may be syntactically different, i.e., they have distinctive graph structure or entity identifiers and properties. Existing data-driven integration approaches only consider syntactic matching criteria or similarity measures to solve the problem of integrating RDF graphs. However, syntactic-based approaches are unable to semantically integrate heterogeneous RDF graphs. We devise SJoin, a semantic similarity join operator to solve the problem of matching semantically equivalent RDF graphs, i.e., syntactically different graphs corresponding to the same real-world entity. Two physical implementations are proposed for SJoin which follow blocking or non-blocking data processing strategies, i.e., RDF graphs can be merged in a batch or incrementally. We empirically evaluate the effectiveness and efficiency of the SJoin physical operators with respect to baseline similarity join algorithms. Experimental results suggest that SJoin outperforms baseline approaches, i.e., non-blocking SJoin incrementally produces results faster, while the blocking SJoin accurately matches all semantically equivalent RDF graphs.