YAGO Ontology Research Articles

Aligning and linking entity mentions in image, text, and knowledge base

A picture is worth a thousand words, the adage reads. However, pictures cannot replace words in terms of their ability to efficiently convey clear (mostly) unambiguous and concise knowledge. Images and text, indeed reveal different and complementary information that, if combined will result in more information than the sum of that contained in a single media. The combination of visual and textual information can be obtained by linking the entities mentioned in the text with those shown in the pictures. To further integrate this with the agent’s background knowledge, an additional step is necessary. That is, either finding the entities in the agent knowledge base that correspond to those mentioned in the text or shown in the picture or, extending the knowledge base with the newly discovered entities. We call this complex task Visual-Textual-Knowledge Entity Linking (VTKEL). In this article, after providing a precise definition of the VTKEL task, we present two datasets called VTKEL1k* and VTKEL30k. These datasets consisting of images and corresponding captions, in which the image and textual mentions are both annotated with the corresponding entities typed according to the YAGO ontology. The datasets can be used for training and evaluating algorithms of the VTKEL task. Successively, we introduce a baseline algorithm called VT-LinKEr (Visual-Textual-Knowledge Entity Linker) for the solution of the VTKEL task. We evaluate the performances of VT-LinKEr on both datasets. We then contribute a supervised algorithm called ViTKan (Visual-Textual-Knowledge Alignment Network). We trained the ViTKan algorithm using features data of the VTKEL1k* dataset. The experimental results on VTKEL1k* and VTKEL30k datasets show that ViTKan substantially outperforms the baseline algorithm.

Parallel Tractability of Ontology Materialization: Technique and Practice

Materialization is an important reasoning service for many ontology-based applications, but the rapid growth of semantic data poses the challenge to efficiently perform materialization on largescale ontologies. Parallel materialization algorithms work well for some ontologies, although the reasoning problem for the used ontology language is not in NC, i.e., the theoretical complexity class for parallel tractability. This motivates us to study the problem of parallel tractability of ontology materialization from a theoretical perspective. We focus on the datalog rewritable ontology languages DL-Lite and Description Horn Logic (DHL) and propose algorithms, called NC algorithms, to identify classes of ontologies for which materialization is tractable in parallel. To verify the practical usability of the above results, we analyze different benchmarks and realworld datasets, including LUBM and the YAGO ontology, and show that for many ontologies expressed in DHL materialization is tractable in parallel. The implementation of our optimized parallel algorithm shows performance improvements over the highly optimized state-of-the-art reasoner RDFox on ontologies for which materialization is tractable in parallel.

Parallel tractability of ontology materialization: Technique and practice

Materialization is an important reasoning service for many ontology-based applications, but the rapid growth of semantic data poses the challenge to efficiently perform materialization on large-scale ontologies. Parallel materialization algorithms work well for some ontologies, although the reasoning problem for the used ontology language is not in NC, i.e., the theoretical complexity class for parallel tractability. This motivates us to study the problem of parallel tractability of ontology materialization from a theoretical perspective. We focus on the datalog rewritable ontology languages DL-Lite and Description Horn Logic (DHL) and propose algorithms, called NC algorithms, to identify classes of ontologies for which materialization is tractable in parallel. To verify the practical usability of the above results, we analyze different benchmarks and real-world datasets, including LUBM and the YAGO ontology, and show that for many ontologies expressed in DHL materialization is tractable in parallel. The implementation of our optimized parallel algorithm shows performance improvements over the highly optimized state-of-the-art reasoner RDFox on ontologies for which materialization is tractable in parallel.

Multi-document summarization based on the Yago ontology

Sentence-based multi-document summarization is the task of generating a succinct summary of a document collection, which consists of the most salient document sentences. In recent years, the increasing availability of semantics-based models (e.g., ontologies and taxonomies) has prompted researchers to investigate their usefulness for improving summarizer performance. However, semantics-based document analysis is often applied as a preprocessing step, rather than integrating the discovered knowledge into the summarization process.This paper proposes a novel summarizer, namely Yago-based Summarizer, that relies on an ontology-based evaluation and selection of the document sentences. To capture the actual meaning and context of the document sentences and generate sound document summaries, an established entity recognition and disambiguation step based on the Yago ontology is integrated into the summarization process.The experimental results, which were achieved on the DUC’04 benchmark collections, demonstrate the effectiveness of the proposed approach compared to a large number of competitors as well as the qualitative soundness of the generated summaries.

Entity Disambiguation in Text by YAGO Ontology

Entity Disambiguation in Text by YAGO Ontology

Personal ontologies: Generation of user profiles based on the YAGO ontology

Personalized search is aimed at tailoring the search outcome to users; to this aim user profiles play an important role: the more faithfully a user profile represents the user interests and preferences, the higher is the probability to improve the search process.In the approaches proposed in the literature, user profiles are formally represented as bags of words, as vectors, or as conceptual taxonomies, generally defined based on external knowledge resources (such as the WordNet and the ODP – Open Directory Project). Ontologies have been more recently considered as a powerful expressive means for knowledge representation. The advantage offered by ontological languages is that they allow a more structured and expressive knowledge representation with respect to the above mentioned approaches. A challenging research activity consists in defining user profiles by a knowledge extraction process from an existing ontology, with the main aim of producing a semantically rich representation of the user interests.In this paper a method to automatically define a personal ontology via a knowledge extraction process from the general purpose ontology YAGO is presented; starting from a set of keywords, which are representatives of the user interests, the process is aimed to define a structured and semantically coherent representation of the user topical interests. In the paper the proposed method is described, as well as some evaluations that show its effectiveness.

From Text to Facts: Recognizing Ontological Facts for a New Application

Fact extraction methods are used for various aims. Here, a new method is introduced for a new application - computing semantic relatedness of texts. Ontological facts are the facts about real world that are available in a knowledgebase called ontology. There are many systems to extract ontological facts from a text. State-of-the-art of these systems is SOFIE that is not appropriate to compute semantic relatedness of texts. In this paper, this problem will be explained and a new ontological facts extraction system is introduced that is appropriate for computing semantic relatedness of texts. For this aim, YAGO ontology will be used as a background knowledge and facts resource. In final it will be suggested that using YAGO relations can optimize ontological facts extraction system.