2,301 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
Graph-Embedding Empowered Entity Retrieval
In this research, we improve upon the current state of the art in entity
retrieval by re-ranking the result list using graph embeddings. The paper shows
that graph embeddings are useful for entity-oriented search tasks. We
demonstrate empirically that encoding information from the knowledge graph into
(graph) embeddings contributes to a higher increase in effectiveness of entity
retrieval results than using plain word embeddings. We analyze the impact of
the accuracy of the entity linker on the overall retrieval effectiveness. Our
analysis further deploys the cluster hypothesis to explain the observed
advantages of graph embeddings over the more widely used word embeddings, for
user tasks involving ranking entities
Relationship based Entity Recommendation System
With the increase in usage of the internet as a place to search for information, the importance of the level of relevance of the results returned by search engines have increased by many folds in recent years. In this paper, we propose techniques to improve the relevance of results shown by a search engine, by using the kinds of relationships between entities a user is interested in. We propose a technique that uses relationships between entities to recommend related entities from a knowledge base which is a collection of entities and the relationships with which they are connected to other entities. These relationships depict more real world relationships between entities, rather than just simple “is-a” or “has-a” relationships. The system keeps track of relationships on which user is clicking and uses this click count as a preference indicator to recommend future entities. This approach is very useful in modern day semantic web searches for recommending entities of user’s interests
A Semantic Graph-Based Approach for Mining Common Topics From Multiple Asynchronous Text Streams
In the age of Web 2.0, a substantial amount of unstructured
content are distributed through multiple text streams in an
asynchronous fashion, which makes it increasingly difficult
to glean and distill useful information. An effective way to
explore the information in text streams is topic modelling,
which can further facilitate other applications such as search,
information browsing, and pattern mining. In this paper, we
propose a semantic graph based topic modelling approach
for structuring asynchronous text streams. Our model in-
tegrates topic mining and time synchronization, two core
modules for addressing the problem, into a unified model.
Specifically, for handling the lexical gap issues, we use global
semantic graphs of each timestamp for capturing the hid-
den interaction among entities from all the text streams.
For dealing with the sources asynchronism problem, local
semantic graphs are employed to discover similar topics of
different entities that can be potentially separated by time
gaps. Our experiment on two real-world datasets shows that
the proposed model significantly outperforms the existing
ones
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
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
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SemTab 2019: Resources to Benchmark Tabular Data to Knowledge Graph Matching Systems
Tabular data to Knowledge Graph matching is the process of assigning semantic tags from knowledge graphs (e.g., Wikidata or DBpedia) to the elements of a table. This task is a challenging problem for various reasons, including the lack of metadata (e.g., table and column names), the noisiness, heterogeneity, incompleteness and ambiguity in the data. The results of this task provide significant insights about potentially highly valuable tabular data, as recent works have shown, enabling a new family of data analytics and data science applications. Despite significant amount of work on various flavors of this problem, there is a lack of a common framework to conduct a systematic evaluation of state-of-the-art systems. The creation of the Semantic Web Challenge on Tabular Data to Knowledge Graph Matching (SemTab) aims at filling this gap. In this paper, we report about the datasets, infrastructure and lessons learned from the first edition of the SemTab challenge
Supervised Typing of Big Graphs using Semantic Embeddings
We propose a supervised algorithm for generating type embeddings in the same
semantic vector space as a given set of entity embeddings. The algorithm is
agnostic to the derivation of the underlying entity embeddings. It does not
require any manual feature engineering, generalizes well to hundreds of types
and achieves near-linear scaling on Big Graphs containing many millions of
triples and instances by virtue of an incremental execution. We demonstrate the
utility of the embeddings on a type recommendation task, outperforming a
non-parametric feature-agnostic baseline while achieving 15x speedup and
near-constant memory usage on a full partition of DBpedia. Using
state-of-the-art visualization, we illustrate the agreement of our
extensionally derived DBpedia type embeddings with the manually curated domain
ontology. Finally, we use the embeddings to probabilistically cluster about 4
million DBpedia instances into 415 types in the DBpedia ontology.Comment: 6 pages, to be published in Semantic Big Data Workshop at ACM, SIGMOD
2017; extended version in preparation for Open Journal of Semantic Web (OJSW
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