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

Searching for entities: When retrieval meets extraction

Retrieving entities inside documents instead of documents or web pages themselves has become an active topic in both commercial search systems and academic information retrieval research. Our method of entity retrieval is based on a two-layer retrieval and extraction probability model (TREPM) for integrating document retrieval and entity extraction together. The document retrieval layer finds supporting documents from the corpus, and the entity extraction layer extracts the right entities from those supporting documents. We theoretically demonstrate that the entity extraction problem can be represented as TREPM model. The TREPM model can reduce the overall retrieval complexity while keeping high accuracy of locating target entities. The experiment is based on the document retrieval and entity extraction as well as the overall task. The preliminary results are promising and deserve for further exploration. Keywords: entity retrieval, document retrieval, entity extraction

Finding Entities in Wikipedia using Links and Categories

Abstract. In this paper we describe our participation in the INEX Entity Ranking track. We explored the relations between Wikipedia pages, categories and links. Our approach is to exploit both category and link information. Category information is used by calculating distances between document categories and target categories. Link information is used for relevance propagation and in the form of a document link prior. Both sources of information have value, but using category information leads to the biggest improvements.

Enabling entity retrieval by exploiting Wikipedia as a semantic knowledge source

This dissertation research, PanAnthropon FilmWorld, aims to demonstrate direct retrieval of entities and related facts by exploiting Wikipedia as a semantic knowledge source, with the film domain as its proof-of-concept domain of application. To this end, a semantic knowledge base concerning the film domain has been constructed with the data extracted/derived from 10,640 Wikipedia pages on films and additional pages on film awards. The knowledge base currently contains 209,266 entities and 2,345,931 entity-centric facts. Both the knowledge base and the corresponding semantic search interface are based on the coherent classification of entities. Entity-centric facts are also consistently represented as tuples. The semantic search interface (http://dlib.ischool.drexel.edu:8080/sofia/PA/) supports multiple types of semantic search functions, which go beyond the traditional keyword-based search function, including the main General Entity Retrieval Query (GERQ) function, which is concerned with retrieving all entities that match the specified entity type, subtype, and semantic conditions and thus corresponds to the main research problem. Two types of evaluation have been performed in order to evaluate (1) the quality of information extraction and (2) the effectiveness of information retrieval using the semantic interface. The first type of evaluation has been performed by inspecting 11,495 film-centric facts concerning 100 films. The results have confirmed high data quality with 99.96% average precision and 99.84% average recall. The second type of evaluation has been performed by conducting an experiment with human subjects. The experiment involved having the subjects perform a retrieval task by using both the PanAnthropon interface and the Internet Movie Database (IMDb) interface and comparing their task performance between the two interfaces. The results have confirmed higher effectiveness of the PanAnthropon interface vs. the IMDb interface (83.11% vs. 40.78% average precision; 83.55% vs. 40.26% average recall). Moreover, the subjects’ responses to the post-task questionnaire indicate that the subjects found the PanAnthropon interface to be highly usable and easily understandable as well as highly effective. The main contribution from this research therefore consists in achieving the set research goal, namely, demonstrating the utility and feasibility of semantics-based direct entity retrieval.Ph.D., Information Studies -- Drexel University, 201

Searching for Entities: When Retrieval Meets Extraction

Retrieving entities from inside of documents, instead of searching for documents or web pages themselves, has become an active topic in both commercial search systems and academic information retrieval research area. Taking into account information needs about entities represented as descriptions with targeted answer entity types, entity search tasks are to return ranked lists of answer entities from unstructured texts, such as news or web pages. Although it works in the same environment as document retrieval, entity retrieval tasks require finer-grained answers entities which need more syntactic and semantic analyses on germane documents than document retrieval. This work proposes a two-layer probability model for addressing this task, which integrates germane document identification and answer entity extraction. Germane document identification retrieves highly related germane documents containing answer entities, while answer entity extraction finds answer entities by utilizing syntactic or linguistic information from those documents. This work theoretically demonstrates the integration of germane document identification and answer entity extraction for the entity retrieval task with the probability model. Moreover, this probability approach helps to reduce the overall retrieval complexity while maintaining high accuracy in locating answer entities. Serial studies are conducted in this dissertation on both germane document identification and answer entity extraction. The learning to rank method is investigated for germane document identification. This method first constructs a model on the training data set using query features, document features, similarity features and rank features. Then the model estimates the probability of the germane documents on testing data sets with the learned model. The experiment indicates that the learning to rank method is significantly better than the baseline systems, which treat germane document identification as a conventional document retrieval problem. The answer entity extraction method aims to correctly extract the answer entities from the germane documents. The methods of answer entity extraction without contexts (such as named entity recognition tools for extraction and knowledge base for extraction) and answer entity extraction with contexts (such as tables/lists as contexts and subject-verb-object structures as contexts) are investigated. These methods individually, however, can extract only parts of answer entities. The method of treating the answer entity extraction problem as a classification problem with the features from the above extraction methods runs significantly better than any of the individual extraction methods