End-to-End Differentiable Proving

We introduce neural networks for end-to-end differentiable proving of queries to knowledge bases by operating on dense vector representations of symbols. These neural networks are constructed recursively by taking inspiration from the backward chaining algorithm as used in Prolog. Specifically, we replace symbolic unification with a differentiable computation on vector representations of symbols using a radial basis function kernel, thereby combining symbolic reasoning with learning subsymbolic vector representations. By using gradient descent, the resulting neural network can be trained to infer facts from a given incomplete knowledge base. It learns to (i) place representations of similar symbols in close proximity in a vector space, (ii) make use of such similarities to prove queries, (iii) induce logical rules, and (iv) use provided and induced logical rules for multi-hop reasoning. We demonstrate that this architecture outperforms ComplEx, a state-of-the-art neural link prediction model, on three out of four benchmark knowledge bases while at the same time inducing interpretable function-free first-order logic rules.Comment: NIPS 2017 camera-ready, NIPS 201

Convolutional 2D Knowledge Graph Embeddings

Link prediction for knowledge graphs is the task of predicting missing relationships between entities. Previous work on link prediction has focused on shallow, fast models which can scale to large knowledge graphs. However, these models learn less expressive features than deep, multi-layer models -- which potentially limits performance. In this work, we introduce ConvE, a multi-layer convolutional network model for link prediction, and report state-of-the-art results for several established datasets. We also show that the model is highly parameter efficient, yielding the same performance as DistMult and R-GCN with 8x and 17x fewer parameters. Analysis of our model suggests that it is particularly effective at modelling nodes with high indegree -- which are common in highly-connected, complex knowledge graphs such as Freebase and YAGO3. In addition, it has been noted that the WN18 and FB15k datasets suffer from test set leakage, due to inverse relations from the training set being present in the test set -- however, the extent of this issue has so far not been quantified. We find this problem to be severe: a simple rule-based model can achieve state-of-the-art results on both WN18 and FB15k. To ensure that models are evaluated on datasets where simply exploiting inverse relations cannot yield competitive results, we investigate and validate several commonly used datasets -- deriving robust variants where necessary. We then perform experiments on these robust datasets for our own and several previously proposed models and find that ConvE achieves state-of-the-art Mean Reciprocal Rank across most datasets.Comment: Extended AAAI2018 pape

Zapotec Language Activism And Talking Dictionaries

Online dictionaries have become a key tool for some indigenous communities to promote and preserve their languages, often in collaboration with linguists. They can provide a pathway for crossing the digital divide and for establishing a first-ever presence on the internet. Many questions around digital lexicography have been explored, although primarily in relation to large and well-resourced languages. Lexical projects on small and under-resourced languages can provide an opportunity to examine these questions from a different perspective and to raise new questions (Mosel, 2011). In this paper, linguists, technical experts, and Zapotec language activists, who have worked together in Mexico and the United States to create a multimedia platform to showcase and preserve lexical, cultural, and environmental knowledge, share their experience and insight in creating trilingual online Talking Dictionaries in several Zapotec languages. These dictionaries sit opposite from big data mining and illustrate the value of dictionary projects based on small corpora, including having the flexibility to make design decisions to maximize community impact and elevate the status of marginalized languages

From calculations to reasoning: history, trends, and the potential of Computational Ethnography and Computational Social Anthropology

The domains of 'computational social anthropology' and 'computational ethnography' refer to the computational processing or computational modelling of data for anthropological or ethnographic research. In this context, the article surveys the use of computational methods regarding the production and the representation of knowledge. The ultimate goal of the study is to highlight the significance of modelling ethnographic data and anthropological knowledge by harnessing the potential of the semantic web. The first objective was to review the use of computational methods in anthropological research focusing on the last 25 years, while the second objective was to explore the potential of the semantic web focusing on existing technologies for ontological representation. For these purposes, the study explores the use of computers in anthropology regarding data processing and data modelling for more effective data processing. The survey reveals that there is an ongoing transition from the instrumentalisation of computers as tools for calculations, to the implementation of information science methodologies for analysis, deduction, knowledge representation, and reasoning, as part of the research process in social anthropology. Finally, it is highlighted that the ecosystem of the semantic web does not subserve quantification and metrics but introduces a new conceptualisation for addressing and meeting research questions in anthropology

Exposing Multi-Relational Networks to Single-Relational Network Analysis Algorithms

Many, if not most network analysis algorithms have been designed specifically for single-relational networks; that is, networks in which all edges are of the same type. For example, edges may either represent "friendship," "kinship," or "collaboration," but not all of them together. In contrast, a multi-relational network is a network with a heterogeneous set of edge labels which can represent relationships of various types in a single data structure. While multi-relational networks are more expressive in terms of the variety of relationships they can capture, there is a need for a general framework for transferring the many single-relational network analysis algorithms to the multi-relational domain. It is not sufficient to execute a single-relational network analysis algorithm on a multi-relational network by simply ignoring edge labels. This article presents an algebra for mapping multi-relational networks to single-relational networks, thereby exposing them to single-relational network analysis algorithms.Comment: ISSN:1751-157