Concept maps can be used to concisely represent important information and bring structure into large document collections. Therefore, we study a variant of multi-document summarization that produces summaries in the form of concept maps. However, suitable evaluation datasets for this task are currently missing. To close this gap, we present a newly created corpus of concept maps that summarize heterogeneous collections of web documents on educational topics. It was created using a novel crowdsourcing approach that allows us to efficiently determine important elements in large document collections. We release the corpus along with a baseline system and proposed evaluation protocol to enable further research on this variant of summarization.
Other types of information representation that also model concepts and their relationships are knowledge bases, such as Freebase @cite_13 , and ontologies. However, they both differ in important aspects: Whereas concept maps follow an open label paradigm and are meant to be interpretable by humans, knowledge bases and ontologies are usually more strictly typed and made to be machine-readable. Moreover, approaches to automatically construct them from text typically try to extract as much information as possible, while we want to summarize a document.
@cite_13
1426956448 1771625187 2964130576 2526486375
Representation learning of knowledge bases aims to embed both entities and relations into a low-dimensional space. Most existing methods only consider direct relations in representation learning. We argue that multiple-step relation paths also contain rich inference patterns between entities, and propose a path-based representation learning model. This model considers relation paths as translations between entities for representation learning, and addresses two key challenges: (1) Since not all relation paths are reliable, we design a path-constraint resource allocation algorithm to measure the reliability of relation paths. (2) We represent relation paths via semantic composition of relation embeddings. Experimental results on real-world datasets show that, as compared with baselines, our model achieves significant and consistent improvements on knowledge base completion and relation extraction from text. The source code of this paper can be obtained from https: github.com mrlyk423 relation_extraction. Knowledge bases provide applications with the benefit of easily accessible, systematic relational knowledge but often suffer in practice from their incompleteness and lack of knowledge of new entities and relations. Much work has focused on building or extending them by finding patterns in large unannotated text corpora. In contrast, here we mainly aim to complete a knowledge base by predicting additional true relationships between entities, based on generalizations that can be discerned in the given knowledgebase. We introduce a neural tensor network (NTN) model which predicts new relationship entries that can be added to the database. This model can be improved by initializing entity representations with word vectors learned in an unsupervised fashion from text, and when doing this, existing relations can even be queried for entities that were not present in the database. Our model generalizes and outperforms existing models for this problem, and can classify unseen relationships in WordNet with an accuracy of 75.8 . Knowledge bases provide applications with the benefit of easily accessible, systematic relational knowledge but often suffer in practice from their incompleteness and lack of knowledge of new entities and relations. Much work has focused on building or extending them by finding patterns in large unannotated text corpora. In contrast, here we mainly aim to complete a knowledge base by predicting additional true relationships between entities, based on generalizations that can be discerned in the given knowledgebase. We introduce a neural tensor network (NTN) model which predicts new relationship entries that can be added to the database. This model can be improved by initializing entity representations with word vectors learned in an unsupervised fashion from text, and when doing this, existing relations can even be queried for entities that were not present in the database. Our model generalizes and outperforms existing models for this problem, and can classify unseen relationships in WordNet with an accuracy of 75.8 . Knowledge bases, which consist of a collection of entities, attributes, and the relations between them are widely used and important for many information retrieval tasks. Knowledge base schemas are often constructed manually using experts with specific domain knowledge for the field of interest. Once the knowledge base is generated then many tasks such as automatic content extraction and knowledge base population can be performed, which have so far been robustly studied by the Natural Language Processing community. However, the current approaches ignore visual information that could be used to build or populate these structured ontologies. Preliminary work on visual knowledge base construction only explores limited basic objects and scene relations. In this paper, we propose a novel multimodal pattern mining approach towards constructing a high-level "event" schema semi-automatically, which has the capability to extend text only methods for schema construction. We utilize a large unconstrained corpus of weakly-supervised image-caption pairs related to high-level events such as "attack" and "demonstration" to both discover visual aspects of an event, and name these visual components automatically. We compare our method with several state-of-the-art visual pattern mining approaches and demonstrate that our proposed method can achieve dramatic improvements in terms of the number of concepts discovered (33 gain), semantic consistence of visual patterns (52 gain), and correctness of pattern naming (150 gain).