24,355 research outputs found
On Type-Aware Entity Retrieval
Today, the practice of returning entities from a knowledge base in response
to search queries has become widespread. One of the distinctive characteristics
of entities is that they are typed, i.e., assigned to some hierarchically
organized type system (type taxonomy). The primary objective of this paper is
to gain a better understanding of how entity type information can be utilized
in entity retrieval. We perform this investigation in an idealized "oracle"
setting, assuming that we know the distribution of target types of the relevant
entities for a given query. We perform a thorough analysis of three main
aspects: (i) the choice of type taxonomy, (ii) the representation of
hierarchical type information, and (iii) the combination of type-based and
term-based similarity in the retrieval model. Using a standard entity search
test collection based on DBpedia, we find that type information proves most
useful when using large type taxonomies that provide very specific types. We
provide further insights on the extensional coverage of entities and on the
utility of target types.Comment: Proceedings of the 3rd ACM International Conference on the Theory of
Information Retrieval (ICTIR '17), 201
The State-of-the-arts in Focused Search
The continuous influx of various text data on the Web requires search engines to improve their retrieval abilities for more specific information. The need for relevant results to a userās topic of interest has gone beyond search for domain or type specific documents to more focused result (e.g. document fragments or answers to a query). The introduction of XML provides a format standard for data representation, storage, and exchange. It helps focused search to be carried out at different granularities of a structured document with XML markups. This report aims at reviewing the state-of-the-arts in focused search, particularly techniques for topic-specific document retrieval, passage retrieval, XML retrieval, and entity ranking. It is concluded with highlight of open problems
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
Entity Query Feature Expansion Using Knowledge Base Links
Recent advances in automatic entity linking and knowledge base
construction have resulted in entity annotations for document and
query collections. For example, annotations of entities from large
general purpose knowledge bases, such as Freebase and the Google
Knowledge Graph. Understanding how to leverage these entity
annotations of text to improve ad hoc document retrieval is an open
research area. Query expansion is a commonly used technique to
improve retrieval effectiveness. Most previous query expansion
approaches focus on text, mainly using unigram concepts. In this
paper, we propose a new technique, called entity query feature
expansion (EQFE) which enriches the query with features from
entities and their links to knowledge bases, including structured
attributes and text. We experiment using both explicit query entity
annotations and latent entities. We evaluate our technique on TREC
text collections automatically annotated with knowledge base entity
links, including the Google Freebase Annotations (FACC1) data.
We find that entity-based feature expansion results in significant
improvements in retrieval effectiveness over state-of-the-art text
expansion approaches
Improving Entity Retrieval on Structured Data
The increasing amount of data on the Web, in particular of Linked Data, has
led to a diverse landscape of datasets, which make entity retrieval a
challenging task. Explicit cross-dataset links, for instance to indicate
co-references or related entities can significantly improve entity retrieval.
However, only a small fraction of entities are interlinked through explicit
statements. In this paper, we propose a two-fold entity retrieval approach. In
a first, offline preprocessing step, we cluster entities based on the
\emph{x--means} and \emph{spectral} clustering algorithms. In the second step,
we propose an optimized retrieval model which takes advantage of our
precomputed clusters. For a given set of entities retrieved by the BM25F
retrieval approach and a given user query, we further expand the result set
with relevant entities by considering features of the queries, entities and the
precomputed clusters. Finally, we re-rank the expanded result set with respect
to the relevance to the query. We perform a thorough experimental evaluation on
the Billions Triple Challenge (BTC12) dataset. The proposed approach shows
significant improvements compared to the baseline and state of the art
approaches
On the Impact of Entity Linking in Microblog Real-Time Filtering
Microblogging is a model of content sharing in which the temporal locality of
posts with respect to important events, either of foreseeable or unforeseeable
nature, makes applica- tions of real-time filtering of great practical
interest. We propose the use of Entity Linking (EL) in order to improve the
retrieval effectiveness, by enriching the representation of microblog posts and
filtering queries. EL is the process of recognizing in an unstructured text the
mention of relevant entities described in a knowledge base. EL of short pieces
of text is a difficult task, but it is also a scenario in which the information
EL adds to the text can have a substantial impact on the retrieval process. We
implement a start-of-the-art filtering method, based on the best systems from
the TREC Microblog track realtime adhoc retrieval and filtering tasks , and
extend it with a Wikipedia-based EL method. Results show that the use of EL
significantly improves over non-EL based versions of the filtering methods.Comment: 6 pages, 1 figure, 1 table. SAC 2015, Salamanca, Spain - April 13 -
17, 201
Target Type Identification for Entity-Bearing Queries
Identifying the target types of entity-bearing queries can help improve
retrieval performance as well as the overall search experience. In this work,
we address the problem of automatically detecting the target types of a query
with respect to a type taxonomy. We propose a supervised learning approach with
a rich variety of features. Using a purpose-built test collection, we show that
our approach outperforms existing methods by a remarkable margin. This is an
extended version of the article published with the same title in the
Proceedings of SIGIR'17.Comment: Extended version of SIGIR'17 short paper, 5 page
Word-Entity Duet Representations for Document Ranking
This paper presents a word-entity duet framework for utilizing knowledge
bases in ad-hoc retrieval. In this work, the query and documents are modeled by
word-based representations and entity-based representations. Ranking features
are generated by the interactions between the two representations,
incorporating information from the word space, the entity space, and the
cross-space connections through the knowledge graph. To handle the
uncertainties from the automatically constructed entity representations, an
attention-based ranking model AttR-Duet is developed. With back-propagation
from ranking labels, the model learns simultaneously how to demote noisy
entities and how to rank documents with the word-entity duet. Evaluation
results on TREC Web Track ad-hoc task demonstrate that all of the four-way
interactions in the duet are useful, the attention mechanism successfully
steers the model away from noisy entities, and together they significantly
outperform both word-based and entity-based learning to rank systems
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