Taxonomies are of great value to many knowledge-rich applications.
As the manual taxonomy curation costs enormous human
effects, automatic taxonomy construction is in great demand. However,
most existing automatic taxonomy construction methods can
only build hypernymy taxonomies wherein each edge is limited
to expressing the “is-a” relation. Such a restriction limits their applicability
to more diverse real-world tasks where the parent-child
may carry different relations. In this paper, we aim to construct
a task-guided taxonomy from a domain-specific corpus, and allow
users to input a “seed” taxonomy, serving as the task guidance. We
propose an expansion-based taxonomy construction framework,
namely HiExpan, which automatically generates key term list from
the corpus and iteratively grows the seed taxonomy. Specifically,
HiExpan views all children under each taxonomy node forming a
coherent set and builds the taxonomy by recursively expanding all
these sets. Furthermore, HiExpan incorporates a weakly-supervised
relation extraction module to extract the initial children of a newly expanded
node and adjusts the taxonomy tree by optimizing its
global structure. Our experiments on three real datasets from different
domains demonstrate the effectiveness of HiExpan for building
task-guided taxonomies.
more »
« less
When parents and children disagree: Diving into DNS delegation inconsistency
The Domain Name System (DNS) is a hierarchical, decentralized, and distributed database. A key mechanism that enables the DNS to be hierarchical and distributed is delegation of responsibility from parent to child zones—typically managed by different entities. RFC1034 states that authoritative nameserver (NS) records at both parent and child should be "consistent and remain so", but we find inconsistencies for over 13M second-level domains. We classify the type of inconsistencies we observe, and the behavior of resolvers in the face of such inconsistencies, using RIPE Atlas to probe our experimental domain configured for different scenarios. Our results underline the risk such inconsistencies pose to the availability of misconfigured domains.
more »
« less
- Award ID(s):
- 1724853
- NSF-PAR ID:
- 10186683
- Date Published:
- Journal Name:
- Passive and Active Measurement Conference (PAM)
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Procedures are inherently hierarchical. To “make videos”, one may need to “purchase a camera”, which in turn may require one to “set a budget”. While such hierarchical knowledge is critical for reasoning about complex procedures, most existing work has treated procedures as shallow structures without modeling the parent-child relation. In this work, we attempt to construct an open-domain hierarchical knowledge-base (KB) of procedures based on wikiHow, a website containing more than 110k instructional articles, each documenting the steps to carry out a complex procedure. To this end, we develop a simple and efficient method that links steps (e.g., “purchase a camera”) in an article to other articles with similar goals (e.g., “how to choose a camera”), recursively constructing the KB. Our method significantly outperforms several strong baselines according to automatic evaluation, human judgment, and application to downstream tasks such as instructional video retrieval.more » « less
-
null (Ed.)Network embedding aims at transferring node proximity in networks into distributed vectors, which can be leveraged in various downstream applications. Recent research has shown that nodes in a network can often be organized in latent hierarchical structures, but without a particular underlying taxonomy, the learned node embedding is less useful nor interpretable. In this work, we aim to improve network embedding by modeling the conditional node proximity in networks indicated by node labels residing in real taxonomies. In the meantime, we also aim to model the hierarchical label proximity in the given taxonomies, which is too coarse by solely looking at the hierarchical topologies. To this end, we propose TAXOGAN to co-embed network nodes and hierarchical labels, through a hierarchical network generation process. Particularly, TAXOGAN models the child labels and network nodes of each parent label in an individual embedding space while learning to transfer network proximity among the spaces of hierarchical labels through stacked network generators and embedding encoders. To enable robust and efficient model inference, we further develop a hierarchical adversarial training process. Comprehensive experiments and case studies on four real-world datasets of networks with hierarchical labels demonstrate the utility of TAXOGAN in improving network embedding on traditional tasks of node classification and link prediction, as well as novel tasks like conditional proximity search and fine-grained taxonomy layout.more » « less
-
Take-down operations aim to disrupt cybercrime involving malicious domains. In the past decade, many successful take-down operations have been reported, including those against the Conficker worm, and most recently, against VPNFilter. Although it plays an important role in fighting cybercrime, the domain take-down procedure is still surprisingly opaque. There seems to be no in-depth understanding about how the take-down operation works and whether there is due diligence to ensure its security and reliability. In this paper, we report the first systematic study on domain takedown. Our study was made possible via a large collection of data, including various sinkhole feeds and blacklists, passive DNS data spanning six years, and historical WHOIS information. Over these datasets, we built a unique methodology that extensively used various reverse lookups and other data analysis techniques to address the challenges in identifying taken-down domains, sinkhole operators, and take-down durations. Applying the methodology on the data, we discovered over 620K takendown domains and conducted a longitudinal analysis on the take-down process, thus facilitating a better understanding of the operation and its weaknesses. We found that more than 14% of domains taken-down over the past ten months have been released back to the domain market and that some of the released domains have been repurchased by the malicious actor again before being captured and seized, either by the same or different sinkholes. In addition, we showed that the misconfiguration of DNS records corresponding to the sinkholed domains allowed us to hijack a domain that was seized by the FBI. Further, we found that expired sinkholes have caused the transfer of around 30K takendown domains whose traffic is now under the control of new owners.more » « less
-
Localization of the root causes for unreproducible builds during software maintenance is an important yet challenging task, primarily due to limited runtime traces from build processes and high diversity of build environments. To address these challenges, in this paper, we propose RepTrace, a framework that leverages the uniform interfaces of system call tracing for monitoring executed build commands in diverse build environments and identifies the root causes for unreproducible builds by analyzing the system call traces of the executed build commands. Specifically, from the collected system call traces, RepTrace performs causality analysis to build a dependency graph starting from an inconsistent build artifact (across two builds) via two types of dependencies: read/write dependencies among processes and parent/child process dependencies, and searches the graph to find the processes that result in the inconsistencies. To address the challenges of massive noisy dependencies and uncertain parent/child dependencies, RepTrace includes two novel techniques: (1) using differential analysis on multiple builds to reduce the search space of read/write dependencies, and (2) computing similarity of the runtime values to filter out noisy parent/child process dependencies. The evaluation results of RepTrace over a set of real-world software packages show that RepTrace effectively finds not only the root cause commands responsible for the unreproducible builds, but also the files to patch for addressing the unreproducible issues. Among its Top-10 identified commands and files, RepTrace achieves high accuracy rate of 90.00% and 90.56% in identifying the root causes, respectively.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1007/978-3-030-44081-7_11
