The DBpedia community project extracts structured, multilingual knowledge from Wikipedia and makes it freely available on the Web using Semantic Web and Linked Data technologies. The project extracts knowledge from 111 different language editions of Wikipedia. The largest DBpedia knowledge base which is extracted from the English edition of Wikipedia consists of over 400 million facts that describe 3.7 million things. The DBpedia knowledge bases that are extracted from the other 110 Wikipedia editions together consist of 1.46 billion facts and describe 10 million additional things. The DBpedia project maps Wikipedia infoboxes from 27 different language editions to a single shared ontology consisting of 320 classes and 1,650 properties. The mappings are created via a world-wide crowd-sourcing effort and enable knowledge from the different Wikipedia editions to be combined. The project publishes releases of all DBpedia knowledge bases for download and provides SPARQL query access to 14 out of the 111 language editions via a global network of local DBpedia chapters. In addition to the regular releases, the project maintains a live knowledge base which is updated whenever a page in Wikipedia changes. DBpedia sets 27 million RDF links pointing into over 30 external data sources and thus enables data from these sources to be used together with DBpedia data. Several hundred data sets on the Web publish RDF links pointing to DBpedia themselves and make DBpedia one of the central interlinking hubs in the Linked Open Data (LOD) cloud. In this system report, we give an overview of the DBpedia community project, including its architecture, technical implementation, maintenance, internationalisation, usage statistics and applications.

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DBpedia - A Large-scale, Multilingual Knowledge Base Extracted from Wikipedia

Summary) The abstract should follow the structure of the article (relevance, degree of exploration of the problem, the goal, the main results, conclusion) and characterize the theoretical and practical significance of the study results. The abstract should not contain wording echoing the title, cumbersome grammatical structures and abbreviations. The text should be written in scientific style. The volume of abstracts (summaries) depends on the content of the article, but should not be less than 250 words. All abbreviations must be disclosed in the summary (in spite of the fact that they will be disclosed in the main text of the article), references to the numbers of publications from reference list should not be made. The sentences of the abstract should constitute an integral text, which can be made by use of the words “consequently”, “for example”, “as a result”. Avoid the use of unnecessary introductory phrases (eg, “the author of the article considers...”, “The article presents...” and so on.)

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Ministry of Education and Science of the Russian Federation

Thank you for downloading constructions a construction grammar approach to argument structure. As you may know, people have search numerous times for their favorite readings like this constructions a construction grammar approach to argument structure, but end up in harmful downloads. Rather than enjoying a good book with a cup of tea in the afternoon, instead they cope with some harmful virus inside their computer.

Constructions A Construction Grammar Approach To Argument Structure

Distant supervision for relation extraction (RE) -- gathering training data by aligning a database of facts with text -- is an efficient approach to scale RE to thousands of different relations. However, this introduces a challenging learning scenario where the relation expressed by a pair of entities found in a sentence is unknown. For example, a sentence containing Balzac and France may express BornIn or Died, an unknown relation, or no relation at all. Because of this, traditional supervised learning, which assumes that each example is explicitly mapped to a label, is not appropriate. We propose a novel approach to multi-instance multi-label learning for RE, which jointly models all the instances of a pair of entities in text and all their labels using a graphical model with latent variables. Our model performs competitively on two difficult domains.

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Multi-instance Multi-label Learning for Relation Extraction

Relation triples produced by open domain information extraction (open IE) systems are useful for question answering, inference, and other IE tasks. Traditionally these are extracted using a large set of patterns; however, this approach is brittle on out-of-domain text and long-range dependencies, and gives no insight into the substructure of the arguments. We replace this large pattern set with a few patterns for canonically structured sentences, and shift the focus to a classifier which learns to extract self-contained clauses from longer sentences. We then run natural logic inference over these short clauses to determine the maximally specific arguments for each candidate triple. We show that our approach outperforms a state-of-the-art open IE system on the end-to-end TAC-KBP 2013 Slot Filling task.

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Leveraging Linguistic Structure For Open Domain Information Extraction

We describe Abstract Meaning Representation (AMR), a semantic representation language in which we are writing down the meanings of thousands of English sentences. We hope that a sembank of simple, whole-sentence semantic structures will spur new work in statistical natural language understanding and generation, like the Penn Treebank encouraged work on statistical parsing. This paper gives an overview of AMR and tools associated with it.

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Abstract Meaning Representation for Sembanking

In this paper we give an overview of the Knowledge Base Population (KBP) track at TAC 2010. The main goal of KBP is to promote research in discovering facts about entities and expanding a structured knowledge base with this information. A large source collection of newswire and web documents is provided for systems to discover information. Attributes (a.k.a. “slots”) derived from Wikipedia infoboxes are used to create the reference knowledge base (KB). KBP2010 includes the following four tasks: (1) Regular Entity Linking, where names must be aligned to entities in the KB; (2) Optional Entity linking, without using Wikipedia texts; (3) Regular Slot Filling, which requires a system to automatically discover the attributes of specified entities from the source document collection and use them to expand the KB; (4) Surprise Slot Filling, which requires a system to return answers regarding new slot types within a short time period. KBP2010 has attracted many participants (over 45 teams registered for KBP 2010 (not including the RTEKBP Validation Pilot task), among which 23 teams submitted results). In this paper we provide an overview of the task definition and annotation challenges associated with KBP2010. Then we summarize the evaluation results and discuss the lessons that we have learned based on detailed analysis.

Overview of the TAC 2010 Knowledge Base Population Track

There are few corpora that endeavor to represent the semantic content of entire documents. We present a corpus that accomplishes one way of capturing document level semantics, by annotating coreference and similar phenomena (bridging and implicit roles) on top of gold Abstract Meaning Representations of sentence-level semantics. We present a new corpus of this annotation, with analysis of its quality, alongside a plausible baseline for comparison. It is hoped that this Multi-Sentence AMR corpus (MS-AMR) may become a feasible method for developing rich representations of document meaning, useful for tasks such as information extraction and question answering.

AMR Beyond the Sentence: the Multi-sentence AMR corpus

Knowledge Base Population (KBP) is an evaluation track of the Text Analysis Conference (TAC), a workshop series organized by the National Institute of Standards and Technology (NIST). The KBP evaluation includes three tasks that target information extraction and question answering technologies: Entity Linking, Slot Filling, and Cold Start. The Cold Start task was introduced in 2012 in an effort to combine and enhance technologies developed for Slot Filling and Entity Linking. Linguistic Data Consortium (LDC) has supported the TAC KBP evaluation since 2009, each year producing new linguistic resources including data, annotations, system assessments, tools and specifications. This paper describes the resource creation efforts in support of TAC KBP 2012, with an emphasis on procedures and methodologies for query selection, annotation, and assessment.

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Linguistic Resources for 2012 Knowledge Base Population Evaluations

Knowledge Base Population (KBP) is an evaluation track of the Text Analysis Conference (TAC), a workshop series organized by the National Institute of Standards and Technology (NIST). In 2013, the KBP evaluations included five tasks targeting information extraction and question answering technologies: Entity Linking, Slot Filling, Temporal Slot Filling, Sentiment Slot Filling, and Cold Start. The Sentiment and Temporal Slot Filling tasks were introduced in 2013 in an effort to move the KBP challenges into new domains, specifically beliefs and events. Linguistic Data Consortium (LDC) has supported the TAC KBP evaluation since 2009, each year producing new linguistic resources including data, annotations, system assessments, tools and specifications. This paper describes the resource creation efforts in support of TAC KBP 2013, with an emphasis on procedures and methodologies for query selection, annotation, and assessment.

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Kira Griffitt

Papers

Abstract Meaning Representation for Sembanking

Overview of the TAC 2010 Knowledge Base Population Track

AMR Beyond the Sentence: the Multi-sentence AMR corpus

Linguistic Resources for 2012 Knowledge Base Population Evaluations

Linguistic Resources for 2013 Knowledge Base Population Evaluations