Détection d'informations vitales pour la mise à jour de bases de connaissances

National audienceMettre à jour une base de connaissances est une problématique actuelle qui suit l'évolution permanente du web de données liées. De nombreuses approches ont été proposées afin d'extraire dans des documents textuels la connaissance à mettre à jour. Ces approches arrivent à maturité mais reposent sur l'hypothèse selon laquelle le corpus adéquat a déjà été constitué. Dans la majorité des cas, les documents à prendre en compte sont sélectionnés manuellement ce qui rend difficile une mise à jour exhaustive de la base. Dans cet article nous proposons une approche originale visant à identifier automatiquement dans un flux de documents du web les éléments pouvant apporter de la connaissance nouvelle sur des instances déjà représentées dans une base

Entity-Centric Stream Filtering and Ranking: Filtering and Unfilterable Documents

Cumulative Citation Recommendation (CCR) is defined as: given a stream of documents on one hand and Knowledge Base (KB) entities on the other, filter, rank and recommend citation-worthy documents. The pipeline encountered in systems that approach this problem involves four stages: filtering, classification, ranking (or scoring), and evaluation. Filtering is only an initial step that reduces the web-scale corpus into a working set of documents more manageable for the subsequent stages. Nevertheless, this step has a large impact on the recall that can be attained maximally. This study analyzes in-depth the main factors that affect recall in the filtering stage. We investigate the impact of choices for corpus cleansing, entity profile construction, entity type, document type, and relevance grade. Because failing on recall in this first step of the pipeline cannot be repaired later on, we identify and characterize the citation-worthy documents that do not pass the filtering stage by examining their contents

Detecting Vital Documents in Massive Data Streams

Existing knowledge bases, includingWikipedia, are typically written and maintained by a group of voluntary editors. Meanwhile, numerous web documents are being published partly due to the popularization of online news and social media. Some of the web documents, called "vital documents", contain novel information that should be taken into account in updating articles of the knowledge bases. However, it is practically impossible for the editors to manually monitor all the relevant web documents. Consequently, there is a considerable time lag between an edit to knowledge base and the publication dates of such vital documents. This paper proposes a realtime detection framework of web documents containing novel information flowing in massive document streams. The framework consists of twostep filter using statistical language models. Further, the framework is implemented on the distributed and faulttolerant realtime computation system, Apache Storm, in order to process the large number of web documents. On a publicly available web document data set, the TREC KBA Stream Corpus, the validity of the proposed framework is demonstrated in terms of the detection performance and processing time

Enhancing Scientific Collaboration Through Knowledge Base Population and Linking for Meetings

Recent research on scientific collaboration shows that distributed interdisciplinary collaborations report comparatively poor outcomes, and the inefficiency of the coordination mechanisms is partially responsible for the problems. To improve in-formation sharing between past collaborators and future team members, or reuse of collaboration records from one project by future researchers, this pa-per describes systems that automatically construct a knowledge base of the meetings from the calendars of participants, and that then link reference to those meetings found in email messages to the correspond-ing meeting in the knowledge base. This is work in progress in which experiments with a publicly avail-able corporate email collection with calendar entries show that the knowledge base population function achieves high precision (0.98, meaning that almost all knowledge base entities are actually meetings) and that the accuracy of the linking from email messages to knowledge base entries (0.90) is already quite good

Entity-Centric Stream Filtering and Ranking: Filtering and Unfilterable Documents

htmlabstractCumulative Citation Recommendation (CCR) is defined as: given a stream of documents on one hand and Knowledge Base (KB) entities on the other, filter, rank and recommend citation-worthy documents. The pipeline encountered in systems that approach this problem involves four stages: filtering, classification, ranking (or scoring), and evaluation. Filtering is only an initial step that reduces the web-scale corpus into a working set of documents more manageable for the subsequent stages. Nevertheless, this step has a large impact on the recall that can be at- tained maximally. This study analyzes in-depth the main factors that affect recall in the filtering stage. We investigate the impact of choices for corpus cleansing, entity profile construction, entity type, document type, and relevance grade. Because failing on recall in this first step of the pipeline cannot be repaired later on, we identify and characterize the citation-worthy documents that do not pass the filtering stage by examining their contents

Humans optional? Automatic large-scale test collections for entity, passage, and entity-passage retrieval

Manually creating test collections is a time-, effort-, and cost-intensive process. This paper describes a fully automatic alternative for deriving large-scale test collections, where no human assessments are needed. The empirical experiments confirm that automatic test collection and manual assessments agree on the best performing systems. The collection includes relevance judgments for both text passages and knowledge base entities. Since test collections with relevance data for both entity and text passages are rare, this approach provides a cost-efficient way for training and evaluating ad hoc passage retrieval, entity retrieval, and entity-aware text retrieval methods

Towards Population of Knowledge Bases from Conversational Sources

With an increasing amount of data created daily, it is challenging for users to organize and discover information from massive collections of digital content (e.g., text and speech). The population of knowledge bases requires linking information from unstructured sources (e.g., news articles and web pages) to structured external knowledge bases (e.g., Wikipedia), which has the potential to advance information archiving and access, and to support knowledge discovery and reasoning. Because of the complexity of this task, knowledge base population is composed of multiple sub-tasks, including the entity linking task, defined as linking the mention of entities (e.g., persons, organizations, and locations) found in documents to their referents in external knowledge bases and the event task, defined as extracting related information for events that should be entered in the knowledge base. Most prior work on tasks related to knowledge base population has focused on dissemination-oriented sources written in the third person (e.g., new articles) that benefit from two characteristics: the content is written in formal language and is to some degree self-contextualized, and the entities mentioned (e.g., persons) are likely to be widely known to the public so that rich information can be found from existing general knowledge bases (e.g., Wikipedia and DBpedia). The work proposed in this thesis focuses on tasks related to knowledge base population for conversational sources written in the first person (e.g., emails and phone recordings), which offers new challenges. One challenge is that most conversations (e.g., 68% of the person names and 53% of the organization names in Enron emails) refer to entities that are known to the conversational participants but not widely known. Thus, existing entity linking techniques relying on general knowledge bases are not appropriate. Another challenge is that some of the shared context between participants in first-person conversations may be implicit and thus challenging to model, increasing the difficulty, even for human annotators, of identifying the true referents. This thesis focuses on several tasks relating to the population of knowledge bases for conversational content: the population of collection-specific knowledge bases for organization entities and meetings from email collections; the entity linking task that resolves the mention of three types of entities (person, organization, and location) found in both conversational text (emails) and speech (phone recordings) sources to multiple knowledge bases, including a general knowledge base built from Wikipedia and collection-specific knowledge bases; the meeting linking task that links meeting-related email messages to the referenced meeting entries in the collection-specific meeting knowledge base; and speaker identification techniques to improve the entity linking task for phone recordings without known speakers. Following the model-based evaluation paradigm, three collections (namely, Enron emails, Avocado emails, and Enron phone recordings) are used as the representations of conversational sources, new test collections are created for each task, and experiments are conducted for each task to evaluate the efficacy of the proposed methods and to provide a comparison to existing state-of-the-art systems. This work has implications in the research fields of e-discovery, scientific collaboration, speaker identification, speech retrieval, and privacy protection

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