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Completeness is a crucial data quality aspect that deals with the question: do we have all the data we need? The lack of awareness on the completeness state of a knowledge graph (KG) may result in bias or even falsity for any decisions made based on the KG. Given a KG, one may be wondering how its completeness may vary across different topics. In this paper, we present ProWD, a framework and tool for profiling the completeness of Wikidata, a central KG on the (Semantic) Web that is open and free to use. ProWD measures the degree of completeness based on the Class-Facet-Attribute (CFA) profiles. A class denotes a collection of entities, which can be of multiple facets, allowing attribute completeness to be analyzed and compared, e.g., how does the completeness of the attribute "educated at" and "date of birth" compare between male, German computer scientists, and female, Indonesian computer scientists? ProWD generates summaries and visualizations for such analysis, giving insights into the KG completeness. ProWD is available online at~\urlhttp://prowd.id.

Most news summarization techniques are static, and thus do not satisfy user needs in having summaries with specific structures or details. Meanwhile, existing dynamic techniques such as query-based summarization fail to handle content-independent queries that target the type of summary information such as time, location, reasons, and consequences of reported events. The NEWS system supports multi-granular summarization along two dimensions: the level of detail and type of information. The system employs fine-grained information extraction to extract facts and their facets with type tagging. The extracted information is then modeled as a graph used to create summaries. The system incrementally expands summaries based on the nodes visited by users, folding related events into the search space.

Recent knowledge extraction methods are moving towards ternary and higher-arity relations to capture more information about binary facts. An example is to include the time, the location, and the duration of a specific fact. These relations can be even more complex to extract in advanced domains such as news, where events typically come with different facets including reasons, consequences, purposes, involved parties, and related events. The main challenge consists in first finding the set of facets related to each fact, and second tagging those facets to the relevant category. In this paper, we tackle the above problems by proposing StuffIE, a fine-grained information extraction approach which is facet-centric. We exploit the Stanford dependency parsing enhanced by lexical databases such as WordNet to extract nested triple relations. Then, we exploit the syntactical dependencies to semantically tag facets using distant learning based on Oxford dictionary. We have tested the accuracy of the extracted facets and their semantic tags using DUC'04 dataset. The results show the high accuracy and coverage of our approach with respect to ClausIE, OLLIE, SEMAFOR SRL and Illinois SRL.

News websites give their users the opportunity to participate in discussions about published articles, by writing comments. Typically, these comments are unstructured making it hard to understand the flow of user discussions. Thus, there is a need for organizing comments to help users to (1) gain more insights about news topics, and (2) have an easy access to comments that trigger their interests. In this work, we address the above problem by organizing comments around the entities and the aspects they discuss. More specifically, we propose an approach for entity and aspect extraction from user comments through the following contributions. First, we extend traditional Named-Entity Recognition approaches, using coreference resolution and external knowledge bases, to detect more occurrences of entities in comments. Second, we exploit part-of-speech tag, dependency tag, and lexical databases to extract explicit and implicit aspects around discussed entities. Third, we evaluate our entity and aspect extraction approach, on manually annotated data, showing that it highly increases precision and recall compared to baseline approaches.

Data completeness is commonly regarded as one of the key aspects of data quality. With this paper we make two main contributions: (i) we develop techniques to reason about the completeness of a query answer over a partially complete database, taking into account con- straints that hold over the database, and (ii) we implement them by an encoding into logic programming paradigms. As constraints we consider primary and foreign keys as well as finite domain con- straints. In this way we can identify more situations in which a query is complete than was possible with previous work. For each combination of constraints, we establish characterizations of the completeness reasoning and we show how to translate them into logic programs. To deal with the case when a query is incomplete, we compute a more general query, which contains all answers of the original query, but that is complete. As a proof of concept we ran our encodings against test cases that capture characteristics of a real-world scenario. ispartof: pages:733-742 ispartof: International Conference on Information and Knowledge Management vol:19-23-Oct-2015 pages:733-742 ispartof: Information and Knowledge Management Conference location:Melbourne date:19 Oct - 23 Oct 2015 status: published

Many kinds of information, such as addresses, crawls of webpages, or academic affiliations, are prone to becoming outdated over time. Therefore, in some applications, updates are performed periodically in order to keep the correctness and usefulness of such information high. As refreshing information usually has a cost, e.g. computation time, network bandwidth or human work time, a problem is to find the right update frequency depending on the benefit gained from the information and on the speed with which the information is expected to get outdated.This is especially important since often entities exhibit a different speed of getting outdated, as, e.g., addresses of students change more frequently than addresses of pensionists, or news portals change more frequently than personal homepages. Thus, there is no uniform best update frequency for all entities.Previous work [5] on data freshness has focused on the question of how to best distribute a fixed budget for updates among various entities, which is of interest in the short-term, when resources are fixed and cannot be adjusted.In the long-term, many businesses are able to adjust their resources in order to optimize their gain. Then, the problem is not one of distributing a fixed number of updates but one of determining the frequency of updates that optimizes the overall gain from the information.In this paper, we investigate how the optimal update frequency for decaying information can be determined. We show that the optimal update frequency is independent for each entity, and how simple iteration can be used to find the optimal update frequency. An implementation of our solution for exponential decay is available online.

Real-life spatial databases are inherently incomplete. This is in particular the case when data from different sources are combined. An extreme example are volunteered geographical information systems like OpenStreetMap. When querying such databases the question arises how reliable are the retrieved answers. For instance, for positive queries, which ask for existing patterns of objects, further answers could show up if the data is completed. For queries with negation, it is furthermore possible that after data completion objects cease to satisfy a query. On the OpenStreetMap wiki, contributors have started to record for some areas which object types have been mapped completely. Given a query, we show how such metainformation can be used to classify objects in the database as certain answers, which are certainly answers in reality, impossible answers, which in reality are definitely not answers, and possible answers, for which it is not known whether they are answers in reality. In addition, we compute the completeness area of a query, that is the maximal area for which it is certain that no further answer objects exist in reality. All this additional information can be computed with standard operations on spatial data. Experiments suggest that the computation of such completeness information is feasible.

Data completeness is an important aspect of data quality. We consider a setting, where databases can be incomplete in two ways: records may be missing and records may contain null values. We (i) formalize when the answer set of a query is complete in spite of such incompleteness, and (ii) we introduce table completeness statements, by which one can express that certain parts of a database are complete. We then study how to deduce from a set of table-completeness statements that a query can be answered completely. Null values as used in SQL are ambiguous. They can indicate either that no attribute value exists or that a value exists, but is unknown. We study completeness reasoning for the different interpretations. We show that in the combined case it is necessary to syntactically distinguish between different kinds of null values and present an encoding for doing that in standard SQL databases. With this technique, any SQL DBMS evaluates complete queries correctly with respect to the different meanings that nulls can carry. We study the complexity of completeness reasoning and provide algorithms that in most cases agree with the worst-case lower bounds.

MAGIK demonstrates how to use meta-information about the completeness of a database to assess the quality of the answers returned by a query. The system holds so-called table-completeness (TC) statements, by which one can express that a table is partially complete, that is, it contains all facts about some aspect of the domain. Given a query, MAGIK determines from such meta-information whether the database contains sufficient data for the query answer to be complete. If, according to the TC statements, the database content is not sufficient for a complete answer, MAGIK explains which further TC statements are needed to guarantee completeness. MAGIK extends and complements theoretical work on modeling and reasoning about data completeness by providing the first implementation of a reasoner. The reasoner operates by translating completeness reasoning tasks into logic programs, which are executed by an answer set engine. © 2012 Authors. ispartof: pages:2725-2727 ispartof: Proceedings of the 21st ACM International Conference on Information and Knowledge Management pages:2725-2727 ispartof: CIKM location:Maui, Hawaii, USA date:29 Oct - 2 Nov 2012 status: published

We study the problem of query completeness (QC) over partially complete databases. Query completeness is an important goal of data quality assurance. Incomplete databases can occur in many contexts, especially whenever many users are supposed to insert data into the same database. Although the overall data can often not be guaranteed to be complete, some information may exist about completeness of parts of the data, e.g., completeness of parts of tables. Given a query, the question arises whether that information implies that the query returns the same set of answers over the available data as it would over the complete database.

Biological scientists have made large amounts of data available on the Web, which can be accessed by canned or precomputed queries presented via web forms. To satisfy further information needs, users currently have to have a good understanding of SQL and how the data is stored in the database. While accessing information at the ontological layer seems more appropriate, this poses two challenges: (1) to query data in databases and triple stores through an ontology with little performance overhead, and (2) to provide an intuitive web-based access to users that are not IT experts. To address these issues, we draw upon the theory and technology developed for Ontology-Based Data Access for DL-Lite. With an OWL ontology and the DIG-QuOnto reasoner as building blocks, we have developed an application that allows for graphical ontology browsing, query formulation, and answer retrieval via a Web browser. We have evaluated our system for Web-ONtology baseD Extraction of Relational data (Wonder) with an existing large genomics database about horizontal gene transfer and found that it meets both the scalability and the usability requirements.

International audience; We investigate the complexity of performing updates on probabilistic XML data for various classes of probabilistic XML documents of different succinctness. We consider two elementary kinds of updates, insertions and deletions, that are defined with the help of a locator query that specifies the nodes where the update is to be performed. For insertions, two semantics are considered, depending on whether a node is to be inserted once or for every match of the query. We first discuss deterministic updates over probabilistic XML, and then extend the algorithms and complexity bounds to probabilistic updates. In addition to a number of intractability results, our main result is an efficient algorithm for insertions defined with branching-free queries over probabilistic models with local dependencies. Finally, we discuss the problem of updating probabilistic XML databases with continuous probability distributions.

Answering queries over ontologies is an important issue for the Semantic Web. Aggregate queries were widely studied for relational databases but almost no results are known for aggregate queries over ontologies. In this work we investigate the latter problem. We propose syntax and semantics for epistemic aggregate queries over ontologies and study query answering for MAX, MIN, COUNT, CNTD, SUM, AVG queries for the ontology language DL-LiteA.