5,498 research outputs found
Ranking Archived Documents for Structured Queries on Semantic Layers
Archived collections of documents (like newspaper and web archives) serve as
important information sources in a variety of disciplines, including Digital
Humanities, Historical Science, and Journalism. However, the absence of
efficient and meaningful exploration methods still remains a major hurdle in
the way of turning them into usable sources of information. A semantic layer is
an RDF graph that describes metadata and semantic information about a
collection of archived documents, which in turn can be queried through a
semantic query language (SPARQL). This allows running advanced queries by
combining metadata of the documents (like publication date) and content-based
semantic information (like entities mentioned in the documents). However, the
results returned by such structured queries can be numerous and moreover they
all equally match the query. In this paper, we deal with this problem and
formalize the task of "ranking archived documents for structured queries on
semantic layers". Then, we propose two ranking models for the problem at hand
which jointly consider: i) the relativeness of documents to entities, ii) the
timeliness of documents, and iii) the temporal relations among the entities.
The experimental results on a new evaluation dataset show the effectiveness of
the proposed models and allow us to understand their limitation
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
EntiTables: Smart Assistance for Entity-Focused Tables
Tables are among the most powerful and practical tools for organizing and
working with data. Our motivation is to equip spreadsheet programs with smart
assistance capabilities. We concentrate on one particular family of tables,
namely, tables with an entity focus. We introduce and focus on two specific
tasks: populating rows with additional instances (entities) and populating
columns with new headings. We develop generative probabilistic models for both
tasks. For estimating the components of these models, we consider a knowledge
base as well as a large table corpus. Our experimental evaluation simulates the
various stages of the user entering content into an actual table. A detailed
analysis of the results shows that the models' components are complimentary and
that our methods outperform existing approaches from the literature.Comment: Proceedings of the 40th International ACM SIGIR Conference on
Research and Development in Information Retrieval (SIGIR '17), 201
User-centered semantic dataset retrieval
Finding relevant research data is an increasingly important but time-consuming task in daily research practice. Several studies report on difficulties in dataset search, e.g., scholars retrieve only partial pertinent data, and important information can not be displayed in the user interface. Overcoming these problems has motivated a number of research efforts in computer science, such as text mining and semantic search. In particular, the emergence of the Semantic Web opens a variety of novel research perspectives. Motivated by these challenges, the overall aim of this work is to analyze the current obstacles in dataset search and to propose and develop a novel semantic dataset search. The studied domain is biodiversity research, a domain that explores the diversity of life, habitats and ecosystems. This thesis has three main contributions: (1) We evaluate the current situation in dataset search in a user study, and we compare a semantic search with a classical keyword search to explore the suitability of semantic web technologies for dataset search. (2) We generate a question corpus and develop an information model to figure out on what scientific topics scholars in biodiversity research are interested in. Moreover, we also analyze the gap between current metadata and scholarly search interests, and we explore whether metadata and user interests match. (3) We propose and develop an improved dataset search based on three components: (A) a text mining pipeline, enriching metadata and queries with semantic categories and URIs, (B) a retrieval component with a semantic index over categories and URIs and (C) a user interface that enables a search within categories and a search including further hierarchical relations. Following user centered design principles, we ensure user involvement in various user studies during the development process
Toward Entity-Aware Search
As the Web has evolved into a data-rich repository, with the standard "page view," current search engines are becoming increasingly inadequate for a wide range of query tasks. While we often search for various data "entities" (e.g., phone number, paper PDF, date), today's engines only take us indirectly to pages. In my Ph.D. study, we focus on a novel type of Web search that is aware of data entities inside pages, a significant departure from traditional document retrieval. We study the various essential aspects of supporting entity-aware Web search. To begin with, we tackle the core challenge of ranking entities, by distilling its underlying conceptual model Impression Model and developing a probabilistic ranking framework, EntityRank, that is able to seamlessly integrate both local and global information in ranking. We also report a prototype system built to show the initial promise of the proposal. Then, we aim at distilling and abstracting the essential computation requirements of entity search. From the dual views of reasoning--entity as input and entity as output, we propose a dual-inversion framework, with two indexing and partition schemes, towards efficient and scalable query processing. Further, to recognize more entity instances, we study the problem of entity synonym discovery through mining query log data. The results we obtained so far have shown clear promise of entity-aware search, in its usefulness, effectiveness, efficiency and scalability
Entity Ranking on Graphs: Studies on Expert Finding
Todays web search engines try to offer services for finding various information in addition to simple web pages, like showing locations or answering simple fact queries. Understanding the association of named entities and documents is one of the key steps towards such semantic search tasks. This paper addresses the ranking of entities and models it in a graph-based relevance propagation framework. In particular we study the problem of expert finding as an example of an entity ranking task. Entity containment graphs are introduced that represent the relationship between text fragments on the one hand and their contained entities on the other hand. The paper shows how these graphs can be used to propagate relevance information from the pre-ranked text fragments to their entities. We use this propagation framework to model existing approaches to expert finding based on the entity's indegree and extend them by recursive relevance propagation based on a probabilistic random walk over the entity containment graphs. Experiments on the TREC expert search task compare the retrieval performance of the different graph and propagation models
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