Abstract DBpedia is a community effort to extract structured information from Wikipedia and to make this information available on the Web. DBpedia allows you to ask sophisticated queries against datasets derived from Wikipedia and to link other datasets on the Web to Wikipedia data. We describe the extraction of the DBpedia datasets, and how the resulting information is published on the Web for human- and machineconsumption. We describe some emerging applications from the DBpedia community and show how website authors can facilitate DBpedia content within their sites. Finally, we present the current status of interlinking DBpedia with other open datasets on the Web and outline how DBpedia could serve as a nucleus for an emerging Web of open data. 1

This paper describes how to automatically cross-reference documents with Wikipedia: the largest knowledge base ever known. It explains how machine learning can be used to identify significant terms within unstructured text, and enrich it with links to the appropriate Wikipedia articles. The resulting link detector and disambiguator performs very well, with recall and precision of almost 75%. This performance is constant whether the system is evaluated on Wikipedia articles or “real world ” documents. This work has implications far beyond enriching documents with explanatory links. It can provide structured knowledge about any unstructured fragment of text. Any task that is currently addressed with bags of words—indexing, clustering, retrieval, and summarization to name a few—could use the techniques described here to draw on a vast network of concepts and semantics.

This article presents YAGO, a large ontology with high coverage and precision. YAGO has been automatically derived from Wikipedia and WordNet. It comprises entities and relations, and currently contains more than 1.7 million entities and 15 million facts. These include the taxonomic Is-A hierarchy as well as semantic relations between entities. The facts for YAGO have been extracted from the category system and the infoboxes of Wikipedia and have been combined with taxonomic relations from WordNet. Type checking techniques help us keep YAGO’s precision at 95% – as proven by an extensive evaluation study. YAGO is based on a clean logical model with a decidable consistency. Furthermore, it allows representing n-ary relations in a natural way while maintaining compatibility with RDFS. A powerful query model facilitates access to YAGO’s data.

We take the category system in Wikipedia as a conceptual network. We label the semantic relations between categories using methods based on connectivity in the network and lexicosyntactic matching. As a result we are able to derive a large scale taxonomy containing a large amount of subsumption, i.e. isa, relations. We evaluate the quality of the created resource by comparing it with ResearchCyc, one of the largest manually annotated ontologies, as well as computing semantic similarity between words in benchmarking datasets.

We present ESTER, a modular and highly efficient system for combined full-text and ontology search. ESTER builds on a query engine that supports two basic operations: prefix search and join. Both of these can be implemented very efficiently with a compact index, yet in combination provide powerful querying capabilities. We show how ESTER can answer basic SPARQL graphpattern queries on the ontology by reducing them to a small number of these two basic operations. ESTER further supports a natural blend of such semantic queries with ordinary full-text queries. Moreover, the prefix search operation allows for a fully interactive and proactive user interface, which after every keystroke suggests to the user possible semantic interpretations of his or her query, and speculatively executes the most likely of these interpretations. As a proof of concept, we applied ESTER to the English Wikipedia, which contains about 3 million documents, combined with the recent YAGO ontology, which contains about 2.5 million facts. For a variety of complex queries, ESTER achieves worst-case query processing times of a fraction of a second, on a single machine, with an index size of about 4 GB.

Abstract — The Web has the potential to become the world’s largest knowledge base. In order to unleash this potential, the wealth of information available on the Web needs to be extracted and organized. There is a need for new querying techniques that are simple and yet more expressive than those provided by standard keyword-based search engines. Searching for knowledge rather than Web pages needs to consider inherent semantic structures like entities (person, organization, etc.) and relationships (isA, locatedIn, etc.). In this paper, we propose NAGA, a new semantic search engine. NAGA builds on a knowledge base, which is organized as a graph with typed edges, and consists of millions of entities and relationships extracted from Web-based corpora. A graph-based query language enables the formulation of queries with additional semantic information. We introduce a novel scoring model, based on the principles of generative language models, which formalizes several notions such as confidence, informativeness and compactness and uses them to rank query results. We demonstrate NAGA’s superior result quality over state-of-the-art search engines and question answering systems. I.

In this paper, we describe current efforts towards interlinking music-related datasets on the Web. We first explain some initial interlinking experiences, and the poor results obtained by taking a naïve approach. We then detail a particular interlinking algorithm, taking into account both the similarities of web resources and of their neighbours. We detail the application of this algorithm in two contexts: to link a Creative Commons music dataset to an editorial one, and to link a personal music collection to corresponding web identifiers. The latter provides a user with personally meaningful entry points for exploring the web of data, and we conclude by describing some concrete tools built to generate and use such links.

This paper presents SOFIE, a system for automated ontology extension. SOFIE can parse natural language documents, extract ontological facts from them and link the facts into an ontology. SOFIE uses logical reasoning on the existing knowledge and on the new knowledge in order to disambiguate words to their most probable meaning, to reason on the meaning of text patterns and to take into account world knowledge axioms. This allows SOFIE to check the plausibility of hypotheses and to avoid inconsistencies with the ontology. The framework of SOFIE unites the paradigms of pattern matching, word sense disambiguation and ontological reasoning in one unified model. Our experiments show that SOFIE delivers high-quality output, even from unstructured Internet documents.

Abstract — Databases of text and text-annotated data constitute a significant fraction of the information available in electronic form. Searching and browsing are the typical ways that users locate items of interest in such databases. Faceted interfaces represent a new powerful paradigm that proved to be a successful complement to keyword searching. Thus far, the identification of the facets was either a manual procedure, or relied on apriori knowledge of the facets that can potentially appear in the underlying collection. In this paper, we present an unsupervised technique for automatic extraction of facets useful for browsing text databases. In particular, we observe, through a pilot study, that facet terms rarely appear in text documents, showing that we need external resources to identify useful facet terms. For this, we first identify important phrases in each document. Then, we expand each phrase with “context ” phrases using external resources, such as WordNet and Wikipedia, causing facet terms to appear in the expanded database. Finally, we compare the term distributions in the original database and the expanded database to identify the terms that can be used to construct browsing facets. Our extensive user studies, using the Amazon Mechanical Turk service, show that our techniques produce facets with high precision and recall that are superior to existing approaches and help users locate interesting items faster. I.

The increasing availability of electronic text has made it possible to acquire information using a variety of techniques that leverage the expertise of both humans and machines. In particular, the field of Information Extraction (IE), in which knowledge is extracted automatically from text, has shown promise for large-scale knowledge acquisition. While IE systems can uncover assertions about individual entities with an increasing level of sophistication, text understanding – the formation of a coherent theory from a textual corpus – involves representation and learning abilities not currently achievable by today’s IE systems. Compared to individual relational assertions outputted by IE systems, a theory includes coherent knowledge of abstract concepts and the relationships among them. We believe that the ability to fully discover the richness of knowledge present within large, unstructured and heterogeneous corpora will require a lifelong learning process in which earlier learned knowledge is used to guide subsequent learning. This paper introduces Alice, a lifelong learning agent whose goal is to automatically discover a collection of concepts, facts and generalizations that describe a particular topic of interest directly from a large volume of Web text. Building upon recent advances in unsupervised information extraction, we demonstrate that Alice can iteratively discover new concepts and compose general domain knowledge with a precision of 78%.