KnowNet: A proposal for building highly connected and dense knowledge bases from the web

This paper presents a new fully automatic method for building highly dense and accurate knowledge bases from existing semantic resources. Basically, the method uses a wide-coverage and accurate nowledge-based Word Sense Disambiguation algorithm to assign the most appropriate senses to large sets of topically related words acquired from the web. KnowNet, the resulting knowledge-base which connects large sets of semantically-related concepts is a major step towards the autonomous acquisition of knowledge from raw corpora. In fact, KnowNet is several times larger than any available knowledge resource encoding relations between synsets, and the knowledge KnowNet contains outperform any other resource when is empirically evaluated in a common multilingual framework.Peer ReviewedPreprint (author's version

Empowering Knowledge Bases: a Machine Learning Perspective

The construction of Knowledge Bases requires quite often the intervention of knowledge engineering and domain experts, resulting in a time consuming task. Alternative approaches have been developed for building knowledge bases from existing sources of information such as web pages and crowdsourcing; seminal examples are NELL, DBPedia, YAGO and several others. With the goal of building very large sources of knowledge, as recently for the case of Knowledge Graphs, even more complex integration processes have been set up, involving multiple sources of information, human expert intervention, crowdsourcing. Despite signi - cant e orts for making Knowledge Graphs as comprehensive and reliable as possible, they tend to su er of incompleteness and noise, due to the complex building process. Nevertheless, even for highly human curated knowledge bases, cases of incompleteness can be found, for instance with disjointness axioms missing quite often. Machine learning methods have been proposed with the purpose of re ning, enriching, completing and possibly raising potential issues in existing knowledge bases while showing the ability to cope with noise. The talk will concentrate on classes of mostly symbol-based machine learning methods, speci cally focusing on concept learning, rule learning and disjointness axioms learning problems, showing how the developed methods can be exploited for enriching existing knowledge bases. During the talk it will be highlighted as, a key element of the illustrated solutions, is represented by the integration of: background knowledge, deductive reasoning and the evidence coming from the mass of the data. The last part of the talk will be devoted to the presentation of an approach for injecting background knowledge into numeric-based embedding models to be used for predictive tasks on Knowledge Graphs

Multilingual evaluation of KnowNet

Este artículo presenta un nuevo método totalmente automático de construcción de bases de conocimiento muy densas y precisas a partir de recursos semánticos preexistentes. Básicamente, el método usa un algoritmo de Interpretación Semántica de las palabras preciso y de amplia cobertura para asignar el sentido mas apropiado a grandes conjuntos de palabras de un mismo tópico que han sido obtenidas de la web. KnowNet, la base de conocimiento resultante que conecta grandes conjuntos de conceptos semánticamente relacionados es un paso importante hacia la adquisición automática de conocimiento a partir de corpus. De hecho, KnowNet es varias veces mas grande que cualquier otro recurso de conocimiento disponible que codifique relaciones entre sentidos, y el conocimiento que KnowNet contiene supera cualquier otro recurso cuando es empíricamente evaluado en un marco multilingüe común. This paper presents a new fully automatic method for building highly dense and accurate knowledge bases from existing semantic resources. Basically, the method uses a wide-coverage and accurate knowledge-based Word Sense Disambiguation Algorithm to assign the most appropriate senses to large sets of topically related words acquired from the web. KnowNet, the resulting knowledge-base which connects large sets of semantically-related concepts is a major step towards the autonomous acquisition of knowledge from raw corpora. In fact, KnowNet is several times larger than any available knowledge resource encoding relations between synsets, and the knowledge KnowNet contains outperform any other resource when is empirically evaluated in a common multilingual framework.Peer ReviewedPostprint (published version

ARCA. Semantic exploration of a bookstore

In this demo paper, we present ARCA, a visual-search based system that allows the semantic exploration of a bookstore. Navigating a domain-specific knowledge graph, students and researchers alike can start from any specific concept and reach any other related concept, discovering associated books and information. To achieve this paradigm of interaction we built a prototype system, flexible and adaptable to multiple contexts of use, that extracts semantic information from the contents of a books' corpus, building a dedicated knowledge graph that is linked to external knowledge bases. The web-based user interface of ARCA integrates text-based search, visual knowledge graph navigation, and linear visualization of filtered books (ordered according to multiple criteria) in a comprehensive coordinated view aimed at exploiting the underlying data while avoiding information overload and unnecessary cluttering. A proof-of-concept of ARCA is available online at http://arca.diag.uniroma1.i

Highlighting relevant concepts from Topic Signatures

This paper presents deepKnowNet, a new fully automatic method for building highly dense and accurate knowledge bases from existing semantic resources. Basically, the method applies a knowledge-based Word Sense Disambiguation algorithm to assign the most appropriate WordNet sense to large sets of topically related words acquired from the web, named TSWEB. This Word Sense Disambiguation algorithm is the personalized PageRank algorithm implemented in UKB. This new method improves by automatic means the current content of WordNet by creating large volumes of new and accurate semantic relations between synsets. KnowNet was our first attempt towards the acquisition of large volumes of semantic relations. However, KnowNet had some limitations that have been overcomed with deepKnowNet. deepKnowNet disambiguates the first hundred words of all Topic Signatures from the web (TSWEB). In this case, the method highlights the most relevant word senses of each Topic Signature and filter out the ones that are not so related to the topic. In fact, the knowledge it contains outperforms any other resource when is empirically evaluated in a common framework based on a similarity task annotated with human judgementsPostprint (published version

Knowledge will Propel Machine Understanding of Content: Extrapolating from Current Examples

Machine Learning has been a big success story during the AI resurgence. One particular stand out success relates to learning from a massive amount of data. In spite of early assertions of the unreasonable effectiveness of data, there is increasing recognition for utilizing knowledge whenever it is available or can be created purposefully. In this paper, we discuss the indispensable role of knowledge for deeper understanding of content where (i) large amounts of training data are unavailable, (ii) the objects to be recognized are complex, (e.g., implicit entities and highly subjective content), and (iii) applications need to use complementary or related data in multiple modalities/media. What brings us to the cusp of rapid progress is our ability to (a) create relevant and reliable knowledge and (b) carefully exploit knowledge to enhance ML/NLP techniques. Using diverse examples, we seek to foretell unprecedented progress in our ability for deeper understanding and exploitation of multimodal data and continued incorporation of knowledge in learning techniques.Comment: Pre-print of the paper accepted at 2017 IEEE/WIC/ACM International Conference on Web Intelligence (WI). arXiv admin note: substantial text overlap with arXiv:1610.0770