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In this position paper we present a new approach for discovering some special classes of assertional knowledge in the text by using large RDF repositories, resulting in the extraction of new non-taxonomic ontological relations. Also we use…

Withdrawn by arXiv administration because authors have forged affiliations and acknowledgements, and have not adequately responded to charges [hep-th/9912039] of unattributed use of verbatim material.

We report an evaluation of the effectiveness of the existing knowledge base embedding models for relation prediction and for relation extraction on a wide range of benchmarks. We also describe a new benchmark, which is much larger and…

arXiv admin note: This version has been removed as the user did not have the right to agree to the license at the time of submission

There are massive amounts of textual data residing in databases, valuable for many machine learning (ML) tasks. Since ML techniques depend on numerical input representations, word embeddings are increasingly utilized to convert symbolic…

In the near future, a technological revolution will involve our cities, where a variety of smart services based on the Internet of Things will be developed to facilitate the needs of the citizens. Sensing devices are already being deployed…

This paper has been withdrawn by the author due to an extended and largely modified version of the paper was published in arXiv (see arXiv:0807.3694, Disjoint minimal graphs).

Admin note: withdrawn by arXiv admin because of the use of a pseudonym, in violation of arXiv policy.

This paper has been withdrawn by the author, as it is now incorporated in 0901.4506 (v4)

Deep learning based techniques have been recently used with promising results for data integration problems. Some methods directly use pre-trained embeddings that were trained on a large corpus such as Wikipedia. However, they may not…

In this paper, we consider advancing web-scale knowledge extraction and alignment by integrating OpenIE extractions in the form of (subject, predicate, object) triples with Knowledge Bases (KB). Traditional techniques from universal schema…

This paper has been withdrawn by the author, due to a significant error in section 4.3.1.

We propose Cognitive Databases, an approach for transparently enabling Artificial Intelligence (AI) capabilities in relational databases. A novel aspect of our design is to first view the structured data source as meaningful unstructured…

Removed by arXiv administrators because submission violated the terms of arXiv's license agreement.

Unsupervised relation extraction (URE) extracts relations between named entities from raw text without manually-labelled data and existing knowledge bases (KBs). URE methods can be categorised into generative and discriminative approaches,…

This paper has been withdrawn by the author(s) in the light of several other works available and due to a misunderstanding in the authorships.

Attribute-Based Access Control (ABAC) and Relationship-based access control (ReBAC) provide a high level of expressiveness and flexibility that promote security and information sharing, by allowing policies to be expressed in terms of…

Feature extraction becomes increasingly important as data grows high dimensional. Autoencoder as a neural network based feature extraction method achieves great success in generating abstract features of high dimensional data. However, it…

In order to assist security analysts in obtaining information pertaining to their network, such as novel vulnerabilities, exploits, or patches, information retrieval methods tailored to the security domain are needed. As labeled text data…

This paper was withdrawn by arXiv admin due to authors' misrepresentation of identity/affiliation.