Vector space word representations are learned from distributional information of words in large corpora. Although such statistics are semantically informative, they disregard the valuable information that is contained in semantic lexicons such as WordNet, FrameNet, and the Paraphrase Database. This paper proposes a method for refining vector space representations using relational information from semantic lexicons by encouraging linked words to have similar vector representations, and it makes no assumptions about how the input vectors were constructed. Evaluated on a battery of standard lexical semantic evaluation tasks in several languages, we obtain substantial improvements starting with a variety of word vector models. Our refinement method outperforms prior techniques for incorporating semantic lexicons into word vector training algorithms.

/pdf/retrofitting-word-vectors-to-semantic-lexicons-3kgdf0h80b.pdf

Retrofitting Word Vectors to Semantic Lexicons

The central idea of Linked Data is that data publishers support applications in discovering and integrating data by complying to a set of best practices in the areas of linking, vocabulary usage, and metadata provision. In 2011, the State of the LOD Cloud report analyzed the adoption of these best practices by linked datasets within different topical domains. The report was based on information that was provided by the dataset publishers themselves via the datahub.io Linked Data catalog. In this paper, we revisit and update the findings of the 2011 State of the LOD Cloud report based on a crawl of the Web of Linked Data conducted in April 2014. We analyze how the adoption of the different best practices has changed and present an overview of the linkage relationships between datasets in the form of an updated LOD cloud diagram, this time not based on information from dataset providers, but on data that can actually be retrieved by a Linked Data crawler. Among others, we find that the number of linked datasets has approximately doubled between 2011 and 2014, that there is increased agreement on common vocabularies for describing certain types of entities, and that provenance and license metadata is still rarely provided by the data sources.

/pdf/adoption-of-the-linked-data-best-practices-in-different-1is6doyvvf.pdf

Adoption of the Linked Data Best Practices in Different Topical Domains

In this paper, we propose a novel embedding model, named ConvKB, for knowledge base completion. Our model ConvKB advances state-of-the-art models by employing a convolutional neural network, so that it can capture global relationships and transitional characteristics between entities and relations in knowledge bases. In ConvKB, each triple (head entity, relation, tail entity) is represented as a 3-column matrix where each column vector represents a triple element. This 3-column matrix is then fed to a convolution layer where multiple filters are operated on the matrix to generate different feature maps. These feature maps are then concatenated into a single feature vector representing the input triple. The feature vector is multiplied with a weight vector via a dot product to return a score. This score is then used to predict whether the triple is valid or not. Experiments show that ConvKB achieves better link prediction performance than previous state-of-the-art embedding models on two benchmark datasets WN18RR and FB15k-237.

/pdf/a-novel-embedding-model-for-knowledge-base-completion-based-2smw2gurqz.pdf

A novel embedding model for knowledge base completion based on convolutional neural network

Semantic Web in data mining and knowledge discovery

/pdf/a-novel-embedding-model-for-knowledge-base-completion-based-432e1z958u.pdf

A Novel Embedding Model for Knowledge Base Completion Based on Convolutional Neural Network

We propose a graph-based semantic model for representing document content. Our method relies on the use of a semantic network, namely the DBpedia knowledge base, for acquiring fine-grained information about entities and their semantic relations, thus resulting in a knowledge-rich document model. We demonstrate the benefits of these semantic representations in two tasks: entity ranking and computing document semantic similarity. To this end, we couple DBpedia's structure with an information-theoretic measure of concept association, based on its explicit semantic relations, and compute semantic similarity using a Graph Edit Distance based measure, which finds the optimal matching between the documents' entities using the Hungarian method. Experimental results show that our general model outperforms baselines built on top of traditional methods, and achieves a performance close to that of highly specialized methods that have been tuned to these specific tasks.

/pdf/knowledge-based-graph-document-modeling-2e3zvxf6b6.pdf

Knowledge-based graph document modeling

More and more websites embed structured data describing for instance products, reviews, blog posts, people, organizations, events, and cooking recipes into their HTML pages using markup standards such as Microformats, Microdata and RDFa. This development has accelerated in the last two years as major Web companies, such as Google, Facebook, Yahoo!, and Microsoft, have started to use the embedded data within their applications. In this paper, we analyze the adoption of RDFa, Microdata, and Microformats across the Web. Our study is based on a large public Web crawl dating from early 2012 and consisting of 3 billion HTML pages which originate from over 40 million websites. The analysis reveals the deployment of the different markup standards, the main topical areas of the published data as well as the different vocabularies that are used within each topical area to represent data. What distinguishes our work from earlier studies, published by the large Web companies, is that the analyzed crawl as well as the extracted data are publicly available. This allows our findings to be verified and to be used as starting points for further domain-specific investigations as well as for focused information extraction endeavors.

/pdf/deployment-of-rdfa-microdata-and-microformats-on-the-web-a-1fox8etpnj.pdf

Deployment of RDFa, Microdata, and Microformats on the Web A Quantitative Analysis

When humans explain complex topics, they naturally talk about involved entities, such as people, locations, or events. In this paper, we aim at automating this process by retrieving and ranking entities that are relevant to understand free-text web-style queries like Argentine British relations, which typically demand a set of heterogeneous entities with no specific target type like, for instance, Falklands_-War} or Margaret-_Thatcher, as answer. Standard approaches to entity retrieval rely purely on features from the knowledge base. We approach the problem from the opposite direction, namely by analyzing web documents that are found to be query-relevant. Our approach hinges on entity linking technology that identifies entity mentions and links them to a knowledge base like Wikipedia. We use a learning-to-rank approach and study different features that use documents, entity mentions, and knowledge base entities -- thus bridging document and entity retrieval. Since established benchmarks for this problem do not exist, we use TREC test collections for document ranking and collect custom relevance judgments for entities. Experiments on TREC Robust04 and TREC Web13/14 data show that: i) single entity features, like the frequency of occurrence within the top-ranke documents, or the query retrieval score against a knowledge base, perform generally well; ii) the best overall performance is achieved when combining different features that relate an entity to the query, its document mentions, and its knowledge base representation.

/pdf/ranking-entities-for-web-queries-through-text-and-knowledge-ck87yelvia.pdf

Ranking Entities for Web Queries Through Text and Knowledge

YouPorn is one of the largest providers of adult content on the
web. Being free of charge, the video portal allows users - besides
watching - to upload, categorize, and comment on pornographic
videos. With this position paper, we point out the challenges of
analyzing the textual data offered with the videos. We report on
first experiments and problems with our
YouPorn dataset
, which we
extracted from the non-graphical content of the YP website. To gain
some insights, we performed association rule mining on the video
categories and tags, and investigated preferences of users based on
their nickname. Hoping that future research will be able to build
upon our initial experiences, we make the ready-to-use
YP dataset
publicly available.

Exploring youporn categories, tags, and nicknames for pleasant recommendations

We present a knowledge-rich approach to Web search result clustering which exploits the output of an open-domain entity linker, as well as the types and topical concepts encoded within a wide-coverage ontology. Our results indicate that, thanks to an accurate and compact semantification of the search result snippets, we are able to achieve a competitive performance on a benchmarking dataset for this task.

/pdf/exploiting-dbpedia-for-web-search-results-clustering-3zrkauckpj.pdf

Michael Schuhmacher

Papers

Knowledge-based graph document modeling

Deployment of RDFa, Microdata, and Microformats on the Web A Quantitative Analysis

Ranking Entities for Web Queries Through Text and Knowledge

Exploring youporn categories, tags, and nicknames for pleasant recommendations

Exploiting DBpedia for web search results clustering