Entity-oriented Search Research Articles

Answering Topical Information Needs Using Neural Entity-Oriented Information Retrieval and Extraction

In the modern world, search engines are an integral part of human lives. The field of Information Retrieval (IR) is concerned with finding material (usually documents) of an unstructured nature (usually text) that satisfies an information need (query) from within large collections (usually stored on computers). The search engine then displays a ranked list of results relevant to our query. Traditional document retrieval algorithms match a query to a document using the overlap of words in both. However, the last decade has seen the focus shifting to leveraging the rich semantic information available in the form of entities. Entities are uniquely identifiable objects or things such as places, events, diseases, etc. that exist in the real or fictional world. Entity-oriented search systems leverage the semantic information associated with entities (e.g., names, types, etc.) to better match documents to queries. Web search engines would provide better search results if they understand the meaning of a query. This dissertation advances the state-of-the-art in IR by developing novel algorithms that understand text (query, document, question, sentence, etc.) at the semantic level. To this end, this dissertation aims to understand the fine-grained meaning of entities from the context in which the entities have been mentioned, for example, "oysters" in the context of food versus ecosystems. Further, this dissertation aims to automatically learn (vector) representations of entities that incorporate this fine-grained knowledge and knowledge about the query. This dissertation refines the automatic understanding of text passages using deep learning, a modern artificial intelligence paradigm. This dissertation utilizes the semantic information extracted from entities to retrieve materials (text and entities) relevant to a query. The interplay between text and entities in the text is studied by addressing three related prediction problems: (1) Identify entities that are relevant for the query, (2) Understand an entity's meaning in the context of the query, and (3) Identify text passages that elaborate the connection between the query and an entity. The research presented in this dissertation may be integrated into a larger system designed for answering complex topical queries such as dark chocolate health benefits which require the search engine to automatically understand the connections between the query and the relevant material, thus transforming the search engine into an answering engine. Awarded by: University of New Hampshire, Durham, USA on 1 September 2022. Supervised by: Laura Dietz. Available at: https://scholars.unh.edu/dissertation/2714/.

WebGIS approach of entity-oriented search to visualize historical and cultural events

Abstract The chronological biography or ‘Nianpu’ is a special style of Chinese historical writing formed by a person’s life events in time order. It provides significant records for the research of historical events. However, from syntactic collation, structural, and semantic aspects, the heterogeneousness of Nianpu materials caused the lack of systematical information collation, which could hinder the research of historical events. The article presents a semantic WebGIS platform based on an entity-oriented search and visualization system to reorganize unstructured information. It delivers: (1) a Chinese historical event database to store and manage Chinese historical information derived from the chronological biography; (2) an ontology to integrate different historical datasets; and (3) a web geographic information system to visualize and analyze information of historical events. This platform achieves the reorganization of historical event information at spatial and temporal levels by the Web. It enables humanities researchers to explore the relationships of historical entities and their evolution visually and interactively. The platform is available at https://nianpu.pkudh.org/.

A Review of Graph-Based Models for Entity-Oriented Search

A Review of Graph-Based Models for Entity-Oriented Search

Graph-based entity-oriented search

Entity-oriented search has revolutionized search engines. In the era of Google Knowledge Graph and Microsoft Satori, users demand an effortless process of search. Whether they express an information need through a keyword query, expecting documents and entities, or through a clicked entity, expecting related entities, there is an inherent need for the combination of corpora and knowledge bases to obtain an answer. Such integration frequently relies on independent signals extracted from inverted indexes, and from quad indexes indirectly accessed through queries to a triplestore. However, relying on two separate representation models inhibits the effective cross-referencing of information, discarding otherwise available relations that could lead to a better ranking. Moreover, different retrieval tasks often demand separate implementations, although the problem is, at its core, the same. With the goal of harnessing all available information to optimize retrieval, we explore joint representation models of documents and entities, while taking a step towards the definition of a more general retrieval approach. Specifically, we propose that graphs should be used to incorporate explicit and implicit information derived from the relations between text found in corpora and entities found in knowledge bases. We also take advantage of this framework to elaborate a general model for entity-oriented search, proposing a universal ranking function for the tasks of ad hoc document retrieval (leveraging entities), ad hoc entity retrieval, and entity list completion. At a conceptual stage, we begin by proposing the graph-of-entity, based on the relations between combinations of term and entity nodes. We introduce the entity weight as the corresponding ranking function, relying on the idea of seed nodes for representing the query, either directly through term nodes, or based on the expansion to adjacent entity nodes. The score is computed based on a series of geodesic distances to the remaining nodes, providing a ranking for the documents (or entities) in the graph. In order to improve on the low scalability of the graph-of-entity, we then redesigned this model in a way that reduced the number of edges in relation to the number of nodes, by relying on the hypergraph data structure. The resulting model, which we called hypergraph-of-entity, is the main contribution of this thesis. The obtained reduction was achieved by replacing binary edges with n -ary relations based on sets of nodes and entities (undirected document hyperedges), sets of entities (undirected hyperedges, either based on cooccurrence or a grouping by semantic subject), and pairs of a set of terms and a set of one entity (directed hyperedges, mapping text to an object). We introduce the random walk score as the corresponding ranking function, relying on the same idea of seed nodes, similar to the entity weight in the graph-of-entity. Scoring based on this function is highly reliant on the structure of the hypergraph, which we call representation-driven retrieval. As such, we explore several extensions of the hypergraph-of-entity, including relations of synonymy, or contextual similarity, as well as different weighting functions per node and hyperedge type. We also propose TF-bins as a discretization for representing term frequency in the hypergraph-of-entity. For the random walk score, we propose and explore several parameters, including length and repeats, with or without seed node expansion, direction, or weights, and with or without a certain degree of node and/or hyperedge fatigue, a concept that we also propose. For evaluation, we took advantage of TREC 2017 OpenSearch track, which relied on an online evaluation process based on the Living Labs API, and we also participated in TREC 2018 Common Core track, which was based on the newly introduced TREC Washington Post Corpus. Our main experiments were supported on the INEX 2009 Wikipedia collection, which proved to be a fundamental test collection for assessing retrieval effectiveness across multiple tasks. At first, our experiments solely focused on ad hoc document retrieval, ensuring that the model performed adequately for a classical task. We then expanded the work to cover all three entity-oriented search tasks. Results supported the viability of a general retrieval model, opening novel challenges in information retrieval, and proposing a new path towards generality in this area.

Task-based support in search engines

Darío Garigliotti is a postdoctoral researcher at Aalborg University, Denmark, where he works in collaboration with Prof. Katja Hose and Assoc. Prof. Johannes Bjerva, in the areas of information extraction, information retrieval and natural language processing. He completed his doctoral degree in Computer Science at University of Stavanger, Norway, under the supervision of Prof. Krisztian Balog. The website at https://dariogarigliotti.github.io/ contains more information about Garigliotti's work. The research conducted by Garigliotti focused on utilizing and extending methods and techniques from semantic search within an information access paradigm that aims to support users in achieving their tasks. More specifically, to enhance search engines with functionalities for recognizing the underlying tasks in searches and providing support for task completion. The work presented in this thesis is organized in three grand themes: entity type information for entity retrieval, entity-oriented search intents, and task-based search. Alongside the theoretical and empirical contributions, a number of resource contributions were developed, including several corpora and test collections, and a knowledge base of entity-oriented search intents.

Characterizing the hypergraph-of-entity and the structural impact of its extensions

The hypergraph-of-entity is a joint representation model for terms, entities and their relations, used as an indexing approach in entity-oriented search. In this work, we characterize the structure of the hypergraph, from a microscopic and macroscopic scale, as well as over time with an increasing number of documents. We use a random walk based approach to estimate shortest distances and node sampling to estimate clustering coefficients. We also propose the calculation of a general mixed hypergraph density measure based on the corresponding bipartite mixed graph. We analyze these statistics for the hypergraph-of-entity, finding that hyperedge-based node degrees are distributed as a power law, while node-based node degrees and hyperedge cardinalities are log-normally distributed. We also find that most statistics tend to converge after an initial period of accentuated growth in the number of documents. We then repeat the analysis over three extensions—materialized through synonym, context, and tf_bin hyperedges—in order to assess their structural impact in the hypergraph. Finally, we focus on the application-specific aspects of the hypergraph-of-entity, in the domain of information retrieval. We analyze the correlation between the retrieval effectiveness and the structural features of the representation model, proposing ranking and anomaly indicators, as useful guides for modifying or extending the hypergraph-of-entity.

Task-based support in search engines

Web search has become a key technology on which people rely daily for getting information about almost everything. The evolution of the search experience has also shaped the expectations of people about it. Many users seem to expect today's web search engines to behave like a kind of "wise interpreter," capable of understanding the meaning behind a search query, realizing its current context, and responding to it directly and appropriately. Search by meaning, or semantic search, encompasses a large portion of information retrieval (IR) research devoted to study more meaningful representations of the information need expressed by the user query. Entity cards, direct displays, and verticals are examples of how major commercial search engines have indeed responded to user expectations, capitalizing on query understanding. Search is usually performed with a specific goal underlying the query. In many cases, this goal consists of a nontrivial task to be completed. Current search engines support a small set of basic tasks, and most of the knowledge-intensive workload for supporting more complex tasks is left to the user. Task-based search can be viewed as an information access paradigm that aims to enhance search engines with functionalities for recognizing the underlying tasks in searches and providing support for task completion. The research presented in this thesis focuses on utilizing and extending methods and techniques from semantic search in the next stage of the evolution of search engines, namely, to support users in achieving their tasks. Our work can be grouped in three grand themes: (1) Entity type information for entity retrieval : we conduct a systematic evaluation and analysis of methods for type-aware entity retrieval, in terms of three main dimensions. Also, we revisit the problem of hierarchical target type identification, present a state-of-the-art supervised learning method, and analyze the usage of automatically identified target entity types for type-aware entity retrieval; (2) Entity-oriented search intents : we propose a categorization scheme for entity-oriented search intents, and study the distributions of entity intent categories per entity type. We further develop a method for constructing a knowledge base of entity-oriented search intents; and (3) Task-based search : we design a probabilistic generative framework for task-based query suggestion, and principledly estimate each of its components. Furthermore, we introduce the problems of query-based task recommendation and mission-based task recommendation, and establish respective methods as suitable baselines.

The first joint international workshop on entity-oriented and semantic search (JIWES)

The First Joint International Workshop on Entity-oriented and Semantic Search (JIWES) workshop was held on Aug 16, 2012 in Portland, Oregon, USA, in conjunction with the 35th Annual International ACM SIGIR Conference (SIGIR 2012). The objective for the workshop was to bring together academic researchers and industry practitioners working on entity-oriented search to discuss tasks and challenges, and to uncover the next frontiers for academic research on the topic. The workshop program accommodated two invited talks, eight refereed papers divided into two technical paper sessions, and a group discussion.

The first international workshop on entity-oriented search (EOS)

The First International Workshop on Entity-Oriented Search (EOS) workshop was held on July 28, 2011 in Beijing, China, in conjunction with the 34th Annual International ACM SIGIR Conference (SIGIR 2011). The objective for the workshop was to bring together academic researchers and industry practitioners working on entity-oriented search to discuss tasks and challenges, and to uncover the next frontiers for academic research on the topic. The workshop program accommodated two invited talks, eleven refereed papers divided into three technical paper sessions, and a group discussion.

Combining Local Scoring and Global Aggregation to Rank Entities for Deep Web Queries

With the rapid growth of Web databases, it is necessary to extract and integrate large-scale data available in Deep Web automatically. But current Web search engines conduct page-level ranking, which are becoming inadequate for entity-oriented vertical search. In this paper, we present an entity-level ranking mechanism called LG-ERM for Deep Web queries based on local scoring and global aggregation. Unlike traditional approaches, LG-ERM considers more rank influencing factors including the uncertainty of entity extraction, the style information of the entities and the importance of the Web sources, as well as the entity relationship. By combining local scoring and global aggregation in ranking, the query result can be more accurate and effective to meet users' needs. The experiments demonstrate the feasibility and effectiveness of the key techniques of LG-ERM.