693 research outputs found
Cross-lingual Entity Alignment with Incidental Supervision
Much research effort has been put to multilingual knowledge graph (KG)
embedding methods to address the entity alignment task, which seeks to match
entities in different languagespecific KGs that refer to the same real-world
object. Such methods are often hindered by the insufficiency of seed alignment
provided between KGs. Therefore, we propose an incidentally supervised model,
JEANS , which jointly represents multilingual KGs and text corpora in a shared
embedding scheme, and seeks to improve entity alignment with incidental
supervision signals from text. JEANS first deploys an entity grounding process
to combine each KG with the monolingual text corpus. Then, two learning
processes are conducted: (i) an embedding learning process to encode the KG and
text of each language in one embedding space, and (ii) a selflearning based
alignment learning process to iteratively induce the matching of entities and
that of lexemes between embeddings. Experiments on benchmark datasets show that
JEANS leads to promising improvement on entity alignment with incidental
supervision, and significantly outperforms state-of-the-art methods that solely
rely on internal information of KGs.Comment: EACL 202
SSP: Semantic Space Projection for Knowledge Graph Embedding with Text Descriptions
Knowledge representation is an important, long-history topic in AI, and there
have been a large amount of work for knowledge graph embedding which projects
symbolic entities and relations into low-dimensional, real-valued vector space.
However, most embedding methods merely concentrate on data fitting and ignore
the explicit semantic expression, leading to uninterpretable representations.
Thus, traditional embedding methods have limited potentials for many
applications such as question answering, and entity classification. To this
end, this paper proposes a semantic representation method for knowledge graph
\textbf{(KSR)}, which imposes a two-level hierarchical generative process that
globally extracts many aspects and then locally assigns a specific category in
each aspect for every triple. Since both aspects and categories are
semantics-relevant, the collection of categories in each aspect is treated as
the semantic representation of this triple. Extensive experiments justify our
model outperforms other state-of-the-art baselines substantially.Comment: Submitted to AAAI.201
Generating Fine-Grained Open Vocabulary Entity Type Descriptions
While large-scale knowledge graphs provide vast amounts of structured facts
about entities, a short textual description can often be useful to succinctly
characterize an entity and its type. Unfortunately, many knowledge graph
entities lack such textual descriptions. In this paper, we introduce a dynamic
memory-based network that generates a short open vocabulary description of an
entity by jointly leveraging induced fact embeddings as well as the dynamic
context of the generated sequence of words. We demonstrate the ability of our
architecture to discern relevant information for more accurate generation of
type description by pitting the system against several strong baselines.Comment: Published in ACL 201
Multimodal Attribute Extraction
The broad goal of information extraction is to derive structured information
from unstructured data. However, most existing methods focus solely on text,
ignoring other types of unstructured data such as images, video and audio which
comprise an increasing portion of the information on the web. To address this
shortcoming, we propose the task of multimodal attribute extraction. Given a
collection of unstructured and semi-structured contextual information about an
entity (such as a textual description, or visual depictions) the task is to
extract the entity's underlying attributes. In this paper, we provide a dataset
containing mixed-media data for over 2 million product items along with 7
million attribute-value pairs describing the items which can be used to train
attribute extractors in a weakly supervised manner. We provide a variety of
baselines which demonstrate the relative effectiveness of the individual modes
of information towards solving the task, as well as study human performance.Comment: AKBC 2017 Workshop Pape
Entity Embeddings with Conceptual Subspaces as a Basis for Plausible Reasoning
Conceptual spaces are geometric representations of conceptual knowledge, in
which entities correspond to points, natural properties correspond to convex
regions, and the dimensions of the space correspond to salient features. While
conceptual spaces enable elegant models of various cognitive phenomena, the
lack of automated methods for constructing such representations have so far
limited their application in artificial intelligence. To address this issue, we
propose a method which learns a vector-space embedding of entities from
Wikipedia and constrains this embedding such that entities of the same semantic
type are located in some lower-dimensional subspace. We experimentally
demonstrate the usefulness of these subspaces as (approximate) conceptual space
representations by showing, among others, that important features can be
modelled as directions and that natural properties tend to correspond to convex
regions
OpenKI: Integrating Open Information Extraction and Knowledge Bases with Relation Inference
In this paper, we consider advancing web-scale knowledge extraction and
alignment by integrating OpenIE extractions in the form of (subject, predicate,
object) triples with Knowledge Bases (KB). Traditional techniques from
universal schema and from schema mapping fall in two extremes: either they
perform instance-level inference relying on embedding for (subject, object)
pairs, thus cannot handle pairs absent in any existing triples; or they perform
predicate-level mapping and completely ignore background evidence from
individual entities, thus cannot achieve satisfying quality. We propose OpenKI
to handle sparsity of OpenIE extractions by performing instance-level
inference: for each entity, we encode the rich information in its neighborhood
in both KB and OpenIE extractions, and leverage this information in relation
inference by exploring different methods of aggregation and attention. In order
to handle unseen entities, our model is designed without creating
entity-specific parameters. Extensive experiments show that this method not
only significantly improves state-of-the-art for conventional OpenIE
extractions like ReVerb, but also boosts the performance on OpenIE from
semi-structured data, where new entity pairs are abundant and data are fairly
sparse
Incorporating GAN for Negative Sampling in Knowledge Representation Learning
Knowledge representation learning aims at modeling knowledge graph by
encoding entities and relations into a low dimensional space. Most of the
traditional works for knowledge embedding need negative sampling to minimize a
margin-based ranking loss. However, those works construct negative samples
through a random mode, by which the samples are often too trivial to fit the
model efficiently. In this paper, we propose a novel knowledge representation
learning framework based on Generative Adversarial Networks (GAN). In this
GAN-based framework, we take advantage of a generator to obtain high-quality
negative samples. Meanwhile, the discriminator in GAN learns the embeddings of
the entities and relations in knowledge graph. Thus, we can incorporate the
proposed GAN-based framework into various traditional models to improve the
ability of knowledge representation learning. Experimental results show that
our proposed GAN-based framework outperforms baselines on triplets
classification and link prediction tasks.Comment: Accepted to AAAI 201
Unsupervised Visual-Linguistic Reference Resolution in Instructional Videos
We propose an unsupervised method for reference resolution in instructional
videos, where the goal is to temporally link an entity (e.g., "dressing") to
the action (e.g., "mix yogurt") that produced it. The key challenge is the
inevitable visual-linguistic ambiguities arising from the changes in both
visual appearance and referring expression of an entity in the video. This
challenge is amplified by the fact that we aim to resolve references with no
supervision. We address these challenges by learning a joint visual-linguistic
model, where linguistic cues can help resolve visual ambiguities and vice
versa. We verify our approach by learning our model unsupervisedly using more
than two thousand unstructured cooking videos from YouTube, and show that our
visual-linguistic model can substantially improve upon state-of-the-art
linguistic only model on reference resolution in instructional videos.Comment: CVPR 201
Learning to Exploit Long-term Relational Dependencies in Knowledge Graphs
We study the problem of knowledge graph (KG) embedding. A widely-established
assumption to this problem is that similar entities are likely to have similar
relational roles. However, existing related methods derive KG embeddings mainly
based on triple-level learning, which lack the capability of capturing
long-term relational dependencies of entities. Moreover, triple-level learning
is insufficient for the propagation of semantic information among entities,
especially for the case of cross-KG embedding. In this paper, we propose
recurrent skipping networks (RSNs), which employ a skipping mechanism to bridge
the gaps between entities. RSNs integrate recurrent neural networks (RNNs) with
residual learning to efficiently capture the long-term relational dependencies
within and between KGs. We design an end-to-end framework to support RSNs on
different tasks. Our experimental results showed that RSNs outperformed
state-of-the-art embedding-based methods for entity alignment and achieved
competitive performance for KG completion.Comment: Accepted by the 36th International Conference on Machine Learning
(ICML 2019
Describing Natural Images Containing Novel Objects with Knowledge Guided Assitance
Images in the wild encapsulate rich knowledge about varied abstract concepts
and cannot be sufficiently described with models built only using image-caption
pairs containing selected objects. We propose to handle such a task with the
guidance of a knowledge base that incorporate many abstract concepts. Our
method is a two-step process where we first build a multi-entity-label image
recognition model to predict abstract concepts as image labels and then
leverage them in the second step as an external semantic attention and
constrained inference in the caption generation model for describing images
that depict unseen/novel objects. Evaluations show that our models outperform
most of the prior work for out-of-domain captioning on MSCOCO and are useful
for integration of knowledge and vision in general.Comment: 10 pages, 5 figure
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