4,839 research outputs found
On-the-fly Table Generation
Many information needs revolve around entities, which would be better
answered by summarizing results in a tabular format, rather than presenting
them as a ranked list. Unlike previous work, which is limited to retrieving
existing tables, we aim to answer queries by automatically compiling a table in
response to a query. We introduce and address the task of on-the-fly table
generation: given a query, generate a relational table that contains relevant
entities (as rows) along with their key properties (as columns). This problem
is decomposed into three specific subtasks: (i) core column entity ranking,
(ii) schema determination, and (iii) value lookup. We employ a feature-based
approach for entity ranking and schema determination, combining deep semantic
features with task-specific signals. We further show that these two subtasks
are not independent of each other and can assist each other in an iterative
manner. For value lookup, we combine information from existing tables and a
knowledge base. Using two sets of entity-oriented queries, we evaluate our
approach both on the component level and on the end-to-end table generation
task.Comment: The 41st International ACM SIGIR Conference on Research and
Development in Information Retrieva
A Coordination Language for Databases
We present a coordination language for the modeling of distributed database
applications. The language, baptized Klaim-DB, borrows the concepts of
localities and nets of the coordination language Klaim but re-incarnates the
tuple spaces of Klaim as databases. It provides high-level abstractions and
primitives for the access and manipulation of structured data, with integrity
and atomicity considerations. We present the formal semantics of Klaim-DB and
develop a type system that avoids potential runtime errors such as certain
evaluation errors and mismatches of data format in tables, which are monitored
in the semantics. The use of the language is illustrated in a scenario where
the sales from different branches of a chain of department stores are
aggregated from their local databases. Raising the abstraction level and
encapsulating integrity checks in the language primitives have benefited the
modeling task considerably
Scalable Data Integration for Linked Data
Linked Data describes an extensive set of structured but heterogeneous datasources where entities are connected by formal semantic descriptions. In thevision of the Semantic Web, these semantic links are extended towards theWorld Wide Web to provide as much machine-readable data as possible forsearch queries. The resulting connections allow an automatic evaluation to findnew insights into the data. Identifying these semantic connections betweentwo data sources with automatic approaches is called link discovery. We derivecommon requirements and a generic link discovery workflow based on similaritiesbetween entity properties and associated properties of ontology concepts. Mostof the existing link discovery approaches disregard the fact that in times ofBig Data, an increasing volume of data sources poses new demands on linkdiscovery. In particular, the problem of complex and time-consuming linkdetermination escalates with an increasing number of intersecting data sources.To overcome the restriction of pairwise linking of entities, holistic clusteringapproaches are needed to link equivalent entities of multiple data sources toconstruct integrated knowledge bases. In this context, the focus on efficiencyand scalability is essential. For example, reusing existing links or backgroundinformation can help to avoid redundant calculations. However, when dealingwith multiple data sources, additional data quality problems must also be dealtwith. This dissertation addresses these comprehensive challenges by designingholistic linking and clustering approaches that enable reuse of existing links.Unlike previous systems, we execute the complete data integration workflowvia a distributed processing system. At first, the LinkLion portal will beintroduced to provide existing links for new applications. These links act asa basis for a physical data integration process to create a unified representationfor equivalent entities from many data sources. We then propose a holisticclustering approach to form consolidated clusters for same real-world entitiesfrom many different sources. At the same time, we exploit the semantic typeof entities to improve the quality of the result. The process identifies errorsin existing links and can find numerous additional links. Additionally, theentity clustering has to react to the high dynamics of the data. In particular,this requires scalable approaches for continuously growing data sources withmany entities as well as additional new sources. Previous entity clusteringapproaches are mostly static, focusing on the one-time linking and clustering ofentities from few sources. Therefore, we propose and evaluate new approaches for incremental entity clustering that supports the continuous addition of newentities and data sources. To cope with the ever-increasing number of LinkedData sources, efficient and scalable methods based on distributed processingsystems are required. Thus we propose distributed holistic approaches to linkmany data sources based on a clustering of entities that represent the samereal-world object. The implementation is realized on Apache Flink. In contrastto previous approaches, we utilize efficiency-enhancing optimizations for bothdistributed static and dynamic clustering. An extensive comparative evaluationof the proposed approaches with various distributed clustering strategies showshigh effectiveness for datasets from multiple domains as well as scalability on amulti-machine Apache Flink cluster
Reasoning & Querying – State of the Art
Various query languages for Web and Semantic Web data, both for practical use and as an area of research in the scientific community, have emerged in recent years. At the same time, the broad adoption of the internet where keyword search is used in many applications, e.g. search engines, has familiarized casual users with using keyword queries to retrieve information on the internet. Unlike this easy-to-use querying, traditional query languages require knowledge of the language itself as well as of the data to be queried. Keyword-based query languages for XML and RDF bridge the gap between the two, aiming at enabling simple querying of semi-structured data, which is relevant e.g. in the context of the emerging Semantic Web. This article presents an overview of the field of keyword querying for XML and RDF
Holistic Statistical Open Data Integration Based On Integer Linear Programming
International audienceIntegrating several Statistical Open Data (SOD) tables is a very promising issue. Various analysis scenarios are hidden behind these statistical data, which makes it important to have a holistic view of them. However, as these data are scattered in several tables, it is a slow and costly process to use existing pairwise schema matching approaches to integrate several schemas of the tables. Hence, we need automatic tools that rapidly converge to a holistic integrated view of data and give a good matching quality. In order to accomplish this objective, we propose a new 0-1 linear program, which automatically resolves the problem of holistic OD integration. It performs global optimal solutions maximizing the profit of similarities between OD graphs. The program encompasses different constraints related to graph structures and matching setup, in particular 1:1 matching. It is solved using a standard solver (CPLEX) and experiments show that it can handle several input graphs and good matching quality compared to existing tools
Structure-Grounded Pretraining for Text-to-SQL
Learning to capture text-table alignment is essential for tasks like
text-to-SQL. A model needs to correctly recognize natural language references
to columns and values and to ground them in the given database schema. In this
paper, we present a novel weakly supervised Structure-Grounded pretraining
framework (StruG) for text-to-SQL that can effectively learn to capture
text-table alignment based on a parallel text-table corpus. We identify a set
of novel prediction tasks: column grounding, value grounding and column-value
mapping, and leverage them to pretrain a text-table encoder. Additionally, to
evaluate different methods under more realistic text-table alignment settings,
we create a new evaluation set Spider-Realistic based on Spider dev set with
explicit mentions of column names removed, and adopt eight existing text-to-SQL
datasets for cross-database evaluation. STRUG brings significant improvement
over BERT-LARGE in all settings. Compared with existing pretraining methods
such as GRAPPA, STRUG achieves similar performance on Spider, and outperforms
all baselines on more realistic sets. All the code and data used in this work
is public available at https://aka.ms/strug.Comment: Accepted to NAACL 2021. Please contact the first author for questions
regarding the spider-realistic datase
- …