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

Linking Data Across Universities: An Integrated Video Lectures Dataset

This paper presents our work and experience interlinking educational information across universities through the use of Linked Data principles and technologies. More specifically this paper is focused on selecting, extracting, structuring and interlinking information of video lectures produced by 27 different educational institutions. For this purpose, selected information from several websites and YouTube channels have been scraped and structured according to well-known vocabularies, like FOAF 1, or the W3C Ontology for Media Resources 2. To integrate this information, the extracted videos have been categorized under a common classification space, the taxonomy defined by the Open Directory Project 3. An evaluation of this categorization process has been conducted obtaining a 98% degree of coverage and 89% degree of correctness. As a result of this process a new Linked Data dataset has been released containing more than 14,000 video lectures from 27 different institutions and categorized under a common classification scheme

Mining Meaning from Wikipedia

Wikipedia is a goldmine of information; not just for its many readers, but also for the growing community of researchers who recognize it as a resource of exceptional scale and utility. It represents a vast investment of manual effort and judgment: a huge, constantly evolving tapestry of concepts and relations that is being applied to a host of tasks. This article provides a comprehensive description of this work. It focuses on research that extracts and makes use of the concepts, relations, facts and descriptions found in Wikipedia, and organizes the work into four broad categories: applying Wikipedia to natural language processing; using it to facilitate information retrieval and information extraction; and as a resource for ontology building. The article addresses how Wikipedia is being used as is, how it is being improved and adapted, and how it is being combined with other structures to create entirely new resources. We identify the research groups and individuals involved, and how their work has developed in the last few years. We provide a comprehensive list of the open-source software they have produced.Comment: An extensive survey of re-using information in Wikipedia in natural language processing, information retrieval and extraction and ontology building. Accepted for publication in International Journal of Human-Computer Studie

Enabling Keyword Search on Linked Data Repositories: An Ontology-Based Approach

The Web is experiencing a continuous change that is leading to the realization of the Semantic Web. Initiatives such as Linked Data have made a huge amount of structured information publicly available, encouraging the rest of the Internet community to tag their resources with it. Unfortunately, the amount of interlinked domains and information is so big that handling it e¿ciently has become really di¿cult for ¿nal users. Thus, we have to provide them with tools to search the needed resources in an easy way. In this paper, we propose an approach to provide users with di¿erent domain views on a general data repository, enabling them to perform both keyword and re¿nement searches. Our system exploits the knowledge stored in ontologies to 1) perform e¿cient keyword searches over a speci¿ed domain, and 2) re¿ne the user’s domain searches. In this way, we enable the de¿nition of di¿erent semantic views on Linked Data datasets without having to change the original semantics. We present a prototype of our approach that focuses on the case of DBpedia, which provides a semantic way to access to Wikipedia

From logical forms to SPARQL query with GETARUNS

We present a system for Question Answering which computes a prospective answer from Logical Forms produced by a full-fledged NLP for text understanding, and then maps the result onto schemata in SPARQL to be used for accessing the Semantic Web. As an intermediate step, and whenever there are complex concepts to be mapped, the system looks for a corresponding amalgam in YAGO classes. It is just by the internal structure of the Logical Form that we are able to produce a suitable and meaningful context for concept disambiguation. Logical Forms are the final output of a complex system for text understanding - GETARUNS - which can deal with different levels of syntactic and semantic ambiguity in the generation of a final structure, by accessing computational lexical equipped with sub-categorization frames and appropriate selectional restrictions applied to the attachment of complements and adjuncts. The system also produces pronominal binding and instantiates the implicit arguments, if needed, in order to complete the required Predicate Argument structure which is licensed by the semantic component