We present the architecture and the evaluation of a new system for recognizing textual entailment (RTE). In RTE we want to identify automatically the type of a logical relation between two input texts. In particular, we are interested in proving the existence of an entailment between them. We conceive our system as a modular environment allowing for a high-coverage syntactic and semantic text analysis combined with logical inference. For the syntactic and semantic analysis we combine a deep semantic analysis with a shallow one supported by statistical models in order to increase the quality and the accuracy of results. For RTE we use logical inference of first-order employing model-theoretic techniques and automated reasoning tools. The inference is supported with problem-relevant background knowledge extracted automatically and on demand from external sources like, e.g., WordNet, YAGO, and OpenCyc, or other, more experimental sources with, e.g., manually defined presupposition resolutions, or with axiomatized general and common sense knowledge. The results show that fine-grained and consistent knowledge coming from diverse sources is a necessary condition determining the correctness and traceability of results.
Finally, the inference process of our system applies ontological knowledge coming from different external sources like, e.g., WordNet or YAGO @cite_4 . In the last few years we have been observing a reasonable growth of interest in huge ontologies and their applications. There exists also a number of huge ontology integration projects like, e.g., YAGO together with the Suggested Upper Model Ontology (SUMO, see melo2008integrating ), DBpedia @cite_17 , or the Linking Open Data Project @cite_26 which aim is to extract and to combine ontological data from many sources. Since YAGO is a part of those ontology projects, it should be possible to integrate them (at least partly) into the RTE application by applying the integration procedure from .
@cite_26 @cite_4 @cite_17
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We present YAGO, a light-weight and extensible ontology with high coverage and quality. YAGO builds on entities and relations and currently contains more than 1 million entities and 5 million facts. This includes the Is-A hierarchy as well as non-taxonomic relations between entities (such as HASONEPRIZE). The facts have been automatically extracted from Wikipedia and unified with WordNet, using a carefully designed combination of rule-based and heuristic methods described in this paper. The resulting knowledge base is a major step beyond WordNet: in quality by adding knowledge about individuals like persons, organizations, products, etc. with their semantic relationships - and in quantity by increasing the number of facts by more than an order of magnitude. Our empirical evaluation of fact correctness shows an accuracy of about 95 . YAGO is based on a logically clean model, which is decidable, extensible, and compatible with RDFS. Finally, we show how YAGO can be further extended by state-of-the-art information extraction techniques. Vast amounts of structured information have been published in the Semantic Web's Linked Open Data (LOD) cloud and their size is still growing rapidly. Yet, access to this information via reasoning and querying is sometimes difficult, due to LOD's size, partial data inconsistencies and inherent noisiness. Machine Learning offers an alternative approach to exploiting LOD's data with the advantages that Machine Learning algorithms are typically robust to both noise and data inconsistencies and are able to efficiently utilize non-deterministic dependencies in the data. From a Machine Learning point of view, LOD is challenging due to its relational nature and its scale. Here, we present an efficient approach to relational learning on LOD data, based on the factorization of a sparse tensor that scales to data consisting of millions of entities, hundreds of relations and billions of known facts. Furthermore, we show how ontological knowledge can be incorporated in the factorization to improve learning results and how computation can be distributed across multiple nodes. We demonstrate that our approach is able to factorize the YAGO 2 core ontology and globally predict statements for this large knowledge base using a single dual-core desktop computer. Furthermore, we show experimentally that our approach achieves good results in several relational learning tasks that are relevant to Linked Data. Once a factorization has been computed, our model is able to predict efficiently, and without any additional training, the likelihood of any of the 4.3 ⋅ 1014 possible triples in the YAGO 2 core ontology. DBpedia contains millions of untyped entities, either if we consider the native DBpedia ontology, or Yago plus WordNet. Is it possible to automatically classify those entities? Based on previous work on wikilink invariances, we wondered if wikilinks convey a knowledge rich enough for their classication. In this paper we give three contributions. Concerning the DBpedia link structure, we describe some measurements and notice both problems (e.g. the bias that could be induced by the incomplete ontological coverage of the DBpedia ontology), and potentials existing in current type coverage. Concerning classication, we present two techniques that exploit wikilinks, one based on induction from machine learning techniques, and the other on abduction. Finally, we discuss the limited results of classication, which conrmed our fears expressed in the description of general gures from the measurement. We also suggest some new possible directions to entity classication that could be taken. This paper gives an overview on the YAGO-NAGA approach to information extraction for building a conveniently searchable, large-scale, highly accurate knowledge base of common facts. YAGO harvests infoboxes and category names of Wikipedia for facts about individual entities, and it reconciles these with the taxonomic backbone of WordNet in order to ensure that all entities have proper classes and the class system is consistent. Currently, the YAGO knowledge base contains about 19 million instances of binary relations for about 1.95 million entities. Based on intensive sampling, its accuracy is estimated to be above 95 percent. The paper presents the architecture of the YAGO extractor toolkit, its distinctive approach to consistency checking, its provisions for maintenance and further growth, and the query engine for YAGO, coined NAGA. It also discusses ongoing work on extensions towards integrating fact candidates extracted from natural-language text sources.