176 research outputs found
Entity-Oriented Search
This open access book covers all facets of entity-oriented search—where “search” can be interpreted in the broadest sense of information access—from a unified point of view, and provides a coherent and comprehensive overview of the state of the art. It represents the first synthesis of research in this broad and rapidly developing area. Selected topics are discussed in-depth, the goal being to establish fundamental techniques and methods as a basis for future research and development. Additional topics are treated at a survey level only, containing numerous pointers to the relevant literature. A roadmap for future research, based on open issues and challenges identified along the way, rounds out the book. The book is divided into three main parts, sandwiched between introductory and concluding chapters. The first two chapters introduce readers to the basic concepts, provide an overview of entity-oriented search tasks, and present the various types and sources of data that will be used throughout the book. Part I deals with the core task of entity ranking: given a textual query, possibly enriched with additional elements or structural hints, return a ranked list of entities. This core task is examined in a number of different variants, using both structured and unstructured data collections, and numerous query formulations. In turn, Part II is devoted to the role of entities in bridging unstructured and structured data. Part III explores how entities can enable search engines to understand the concepts, meaning, and intent behind the query that the user enters into the search box, and how they can provide rich and focused responses (as opposed to merely a list of documents)—a process known as semantic search. The final chapter concludes the book by discussing the limitations of current approaches, and suggesting directions for future research. Researchers and graduate students are the primary target audience of this book. A general background in information retrieval is sufficient to follow the material, including an understanding of basic probability and statistics concepts as well as a basic knowledge of machine learning concepts and supervised learning algorithms
IntentsKB: A Knowledge Base of Entity-Oriented Search Intents
We address the problem of constructing a knowledge base of entity-oriented
search intents. Search intents are defined on the level of entity types, each
comprising of a high-level intent category (property, website, service, or
other), along with a cluster of query terms used to express that intent. These
machine-readable statements can be leveraged in various applications, e.g., for
generating entity cards or query recommendations. By structuring
service-oriented search intents, we take one step towards making entities
actionable. The main contribution of this paper is a pipeline of components we
develop to construct a knowledge base of entity intents. We evaluate
performance both component-wise and end-to-end, and demonstrate that our
approach is able to generate high-quality data.Comment: Proceedings of the 27th ACM International Conference on Information
and Knowledge Management (CIKM'18), 2018. 4 pages. 2 figure
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
How to Search the Internet Archive Without Indexing It
Significant parts of cultural heritage are produced on the web during the
last decades. While easy accessibility to the current web is a good baseline,
optimal access to the past web faces several challenges. This includes dealing
with large-scale web archive collections and lacking of usage logs that contain
implicit human feedback most relevant for today's web search. In this paper, we
propose an entity-oriented search system to support retrieval and analytics on
the Internet Archive. We use Bing to retrieve a ranked list of results from the
current web. In addition, we link retrieved results to the WayBack Machine;
thus allowing keyword search on the Internet Archive without processing and
indexing its raw archived content. Our search system complements existing web
archive search tools through a user-friendly interface, which comes close to
the functionalities of modern web search engines (e.g., keyword search, query
auto-completion and related query suggestion), and provides a great benefit of
taking user feedback on the current web into account also for web archive
search. Through extensive experiments, we conduct quantitative and qualitative
analyses in order to provide insights that enable further research on and
practical applications of web archives
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
知識グラフ内の微細な情報を用いたエンティティ指向検索の支援
京都大学0048新制・課程博士博士(情報学)甲第22806号情博第736号新制||情||126(附属図書館)京都大学大学院情報学研究科社会情報学専攻(主査)教授 吉川 正俊, 教授 森 信介, 教授 田島 敬史学位規則第4条第1項該当Doctor of InformaticsKyoto UniversityDFA
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