Interlinking text documents with Linked Open Data enables the Web of Data to be used as background knowledge within document-oriented applications such as search and faceted browsing. As a step towards interconnecting the Web of Documents with the Web of Data, we developed DBpedia Spotlight, a system for automatically annotating text documents with DBpedia URIs. DBpedia Spotlight allows users to configure the annotations to their specific needs through the DBpedia Ontology and quality measures such as prominence, topical pertinence, contextual ambiguity and disambiguation confidence. We compare our approach with the state of the art in disambiguation, and evaluate our results in light of three baselines and six publicly available annotation systems, demonstrating the competitiveness of our system. DBpedia Spotlight is shared as open source and deployed as a Web Service freely available for public use.

Tables are a universal idiom to present relational data. Billions of tables on Web pages express entity references, attributes and relationships. This representation of relational world knowledge is usually considerably better than completely unstructured, free-format text. At the same time, unlike manually-created knowledge bases, relational information mined from “organic ” Web tables need not be constrained by availability of precious editorial time. Unfortunately, in the absence of any formal, uniform schema imposed on Web tables, Web search cannot take advantage of these high-quality sources of relational information. In this paper we propose new machine learning techniques to annotate table cells with entities that they likely mention, table columns with types from which entities are drawn for cells in the column, and relations that pairs of table columns seek to express. We propose a new graphical model for making all these labeling decisions for each table simultaneously, rather than make separate local decisions for entities, types and relations. Experiments using the YAGO catalog, DB-Pedia, tables from Wikipedia, and over 25 million HTML tables from a 500 million page Web crawl uniformly show the superiority of our approach. We also evaluate the impact of better annotations on a prototype relational Web search tool. We demonstrate clear benefits of our annotations beyond indexing tables in a purely textual manner. 1.

Twitter and other microblogs have rapidly become a significant means by which people communicate with the world and each other in near realtime. There has been a large number of studies surrounding these social media, focusing on areas such as information spread, various centrality measures, topic detection and more. However, one area which has not received much attention is trying to better understand what information is being spread and why it is being spread. This work looks to get a better understanding of what makes people spread information in tweets or microblogs through the use of retweeting. Several retweet behavior models are presented and evaluated on a Twitter data set consisting of over 768,000 tweets gathered from monitoring over 30,000 users for a period of one month. We evaluate the proposed models against each user and show how people use different retweet behavior models. For example, we find that although users in the majority of cases do not retweet information on topics that they themselves Tweet about as or from people who are “like them ” (hence anti-homophily), we do find that models which do take homophily, or similarity, into account fits the observed retweet behaviors much better than other more general models which do not take this into account. We further find that, not surprisingly, people’s retweeting behavior is better explained through multiple different models rather than one model. 1

In many entity extraction applications, the entities to be recognized are constrained to be from a list of “target entities”. In many cases, these target entities are (i) ad-hoc, i.e., do not exist in a knowledge base and (ii) homogeneous (e.g., all the entities are IT companies). We study the following novel disambiguation problem in this unique setting: given the candidate mentions of all the target entities, determine which ones are true mentions of a target entity. Prior techniques only consider target entities present in a knowledge base and/or having a rich set of attributes. In this paper, we develop novel techniques that require no knowledge about the entities excepttheir names. Ourmain insight is to leverage the homogeneity constraint and disambiguate the candidate mentions collectively across all documents. We propose a graph-based model, called MentionRank, for that purpose. Furthermore, if additional knowledge is available for some or all of the entities, our model can leverage it to further improve quality. Our experiments demonstrate the effectiveness of our model. To the best of our knowledge, this is the first work on targeted entity disambiguation for ad-hoc entities.

Search engines today offer a rich user experience, no longer restricted to“ten blue links”. For example, the query“Canon EOS Digital Camera ” returns a photo of the digital camera, and a list of suitable merchants and prices. Similar results are offered in other domains like food, entertainment, travel, etc. All these experiences are fueled by the availability of structured data about the entities of interest. To obtain this structured data, it is necessary to solve the following problem: given a category of entities with its schema, and a set of Web pages that mention and describe entities belonging to the category, build a structured representation for the entity under the given schema. Specifically, collect structured numerical or discrete attributes of the entities. Most previous approaches regarded this as an information extraction problem on individual documents, and made no special use of numerical attributes. In contrast, we present an end-to-end framework which leverages signals not only from the Web page context, but also from a collective analysis of all the pages corresponding to an entity, and from constraints related to the actual values within the domain. Our current implementation uses a general and flexible Integer Linear Program (ILP) to integrate all these signals into holistic decisions over all attributes. There is one ILP per entity and it is small enough to be solved in under 38 milliseconds in our experiments. We apply the new framework to a setting of significant practical importance: catalog expansion for Commerce search engines, using data from Bing Shopping. Finally, we present experiments that validate the effectiveness of the framework and its superiority to local extraction.

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We present a class of web queries whose result is a multi-column relation instead of a collection of unstructured documents as in standard web search. The user specifies the query either via a few example records, or a text description of columns of the relation. Starting from this seed, we show how to compile the result from several, possibly overlapping, tables and lists on the web. Many challenges arise in the process. First, we need to be able to extract structured records from HTML pages with little user supervision. We present algorithms for jointly aligning arbitrary record sets on the web with the query table. We adapt state of the art extraction models like Conditional Random Fields to exploit inter and intra source regularity in a unified framework. Second, we need to be able to consolidate the results from several sources in the face of missing columns, noisy extractions, and zero human supervision. We show how a suitably designed Bayesian networks allows us to compose a resolver from a library of type-specific similarity functions and table statistics. Finally, we discuss the problem of ranking the result rows by their estimated membership in the hidden target relation.

Wikipedia is the largest user-generated knowledge base. We propose a structured query mechanism, entity-relationship query, for searching entities in Wikipedia corpus by their properties and inter-relationships. An entity-relationship query consists of arbitrary number of predicates on desired entities. The semantics of each predicate is specified with keywords. Entity-relationship query searches entities directly over text rather than pre-extracted structured data stores. This characteristic brings two benefits: (1) Query semantics can be intuitively expressed by keywords; (2) It avoids information loss that happens during extraction. We present a ranking framework for general entity-relationship queries and a position-based BoundedCumulative Model for accurate ranking of query answers. Experiments on INEX benchmark queries and our own crafted queries show the effectiveness and accuracy of our ranking method.

Twitter, a micro-blogging service, provides users with a framework for writing brief, often-noisy postings about their lives. These posts are called “Tweets. ” In this paper we present early results on discovering Twitter users ’ topics of interest by examining the entities they mention in their Tweets. Our approach leverages a knowledge base to disambiguate and categorize the entities in the Tweets. We then develop a “topic profile, ” which characterizes users ’ topics of interest, by discerning which categories appear frequently and cover the entities. We demonstrate that even in this early work we are able to successfully discover the main topics of interest for the users in our study.

Disambiguating concepts and entities in a context sensitive way is a fundamental problem in natural language processing. The comprehensiveness of Wikipedia has made the online encyclopedia an increasingly popular target for disambiguation. Disambiguation to Wikipedia is similar to a traditional Word Sense Disambiguation task, but distinct in that the Wikipedia link structure provides additional information about which disambiguations are compatible. In this work we analyze approaches that utilize this information to arrive at coherent sets of disambiguations for a given document (which we call “global ” approaches), and compare them to more traditional (local) approaches. We show that previous approaches for global disambiguation can be improved, but even then the local disambiguation provides a baseline which is very hard to beat. 1