Introduction

Artificial intelligence (AI) and knowledge representation in the field of temporal knowledge graphs are rapidly gaining interest. Current challenges include developing models and frameworks that can effectively form reasoning mechanisms. Temporal knowledge graphs (TKGs) refer to a specific group of knowledge representation systems that besides concepts, entities, events, and relationships, capture time information (Li et al., 2021). These graphs give so much useful material in the processes of forecasting, organizing plans, and decision-making.

TKGs are active databases that not only drive the information but also, the context of true incidents when they happen and that makes them crucial in the prediction of forthcoming trends and the understanding of complex sequences of events.

Innovative techniques termed Temporal Feature-Logic Embedding framework (TFLEX) were introduced, which is purposefully built to deal with temporal and logical issues especially to handle queries that have elements of time information, that classic static KGs would fail to deal with.

Let’s build a fundamental concept before we delve into TFLEX:

What is a Knowledge Graph?

A knowledge graph is a way of storing information that uses a graph-structured model. In this model, entities are represented as nodes and the relationship between them are represented as edges, connected to the nodes. This structure depicts complex interrelationships within data.

For example: An entity “Inception” is connected to an entity “Leonardo DiCaprio” with the relationship “acted in” and to “Christopher Nolan” entity with the relationship “directed by”.

This structure makes it easy to understand the connections between pieces of information and to use them for reasoning, searching, and knowledge discovery.

What is a Temporal Knowledge Graph?

A Temporal Knowledge Graph (TKG) enhances traditional static knowledge graphs by incorporating time data into the structure. This is achieved by adding a timestamp to the usual triple format, resulting in elements described as <head, relation, tail, timestamp>.

For instance, consider the data point <Bill Gates, makes a visit, India, 2024–04–23>; these records not only the event and the entities involved but also the exact date it occurred.

TFLEX

Recent research on Temporal Knowledge Graph Completion (TKGC) aims to infer new facts in TKGs that focus on temporal link prediction, which is simply a single hop. It tries to predict if two nodes that are not currently connected will form a single direct link in the future based on the history of the network and its evolution over time. Some TKGC methods like timestamp-based transformation, dynamic embedding, and Markov process model work by performing multi-hop reasoning. However, none of them could answer logical questions involving conjunction, negation, and disjunction operation

For instance, find all scientists who have published their research in the field of Artificial Intelligence (Conjunction), have never received an award (Negation), or have collaborated with certain universities before 2020 (Before and Disjunction). In this article, we focused on multi-hop logical reasoning to answer the temporal complex queries. To understand this method, we should know what temporal complex queries are.

Temporal Complex Queries

This type of query for Temporal Knowledge Graphs (TKGs) specifically includes both entity and temporal elements. These queries are complex because they require reasoning across multiple connections (multi-hop) and involve both entities and the times associated with their relationships. For example, a query might ask which events a person attended before a certain year and who else was present at those events. This type of query goes beyond simple fact retrieval, incorporating logical and temporal operators to extract structured information across time and entities as discussed above.

In a mathematical way, a temporal complex query can be represented as:

The above query will be discussed in more detail in the paragraphs that follow.

Data Generation Process

Researchers formulated the above temporal complex queries that involve both entities and their temporal relationships and then applied these queries to three different popular TKGs to generate three datasets (ICEWS14, ICEWS05–15, and GDELT-500) for further training and testing this model.

Multi-hop logical reasoning over knowledge graphs is a complex process that involves answering queries by traversing multiple relationships (or “hops”) between entities in a knowledge graph. In simpler terms, it’s like solving a puzzle where each piece (or hop) brings you closer to the complete answer by following a trail of interconnected data points.

1. Entity and Timestamp Representation:

• Entity Nodes (Circles): Entities such as “Xi Jinping” and “Barack Obama” are represented as blue circles.
• Timestamp Nodes (Green Triangles): The period of François Hollande’s presidency is represented as a green triangle, indicating the specific timeframe relevant to the query.

2. Entity and Time Set Projections (Arrows):

• Blue Arrows: These represent entity set projections. For instance, an arrow from the entity node “Xi Jinping” to a logical operation indicates that the action of visiting countries is being queried specifically for Xi Jinping.
• Green Arrows: These represent time set projections. They connect the timestamp node of Hollande’s presidency to other operations, applying the time filter to the activities of the entities involved.

3. Logical Operations (Red Rectangles):

• Visit Operations: Separate logical operations check which countries each leader visited during Hollande’s presidency. These operations are linked from the entity nodes (Xi Jinping, Barack Obama) through blue arrows, indicating entity-focused actions within the specified time, linked through green arrows.
• AND NOT Operation: This logical operator compares the outputs from the Xi and Obama visit operations. It filters out any overlaps, thus excluding countries visited by Obama from Xi’s list.

4. Final Output (Node):

• The output node at the end of the computation graph provides the answer to the query. It lists countries visited by Xi Jinping but not by Barack Obama during the defined timeframe. This is achieved after processing through the logical AND and NOT operation, which ensures only Xi’s unique visits are counted.

The computation graph in Figure 3 represents how these time adjustments are made within the embeddings. It shows that only the time components are modified in the highlighted colors leaving the entity-related parts unchanged in faded gray color. It focuses solely on the temporal shift implied by the query which is important for reasoning over knowledge that changes over time.

Model Prediction in temporal context before and after the event

• Pt is projection in time that shows the most likely time for the event to have occurred, which is aligned with ground truth date.
• bPt (Before Projection Time): bPt indicates time earlier than the peak for “Pt,” as highlighted by the ‘before’ region in the graph.
• aPt(After Projection Time): aPt indicates time earlier than the peak for “Pt,” as highlighted by the ‘after’ region in the graph.

Figure 4 & 5 uses the x-axis to detail the model’s performance across different types of queries, demonstrating how predictions align with ground truth data over time. In simple words, the purpose of the above visualization is to show the model’s ability to predict when events occur relative to a known point in time. “Pt” is the model’s direct prediction, while “bPt” and “aPt” show the model’s understanding of the temporal context before and after the event, respectively.

Result

Take this query example: Philippines denounced or criticized who on 2014–04–01?

Taking another query example: On 2014–04–04, who consulted the man who was appealed to or requested by the Head of Government (Latvia) on 2014–08–01?

In Table 2, the query “On 2014–04–04, who consulted the man who was appealed to or requested by the Head of Government (Latvia) on 2014–08–01?” was taken and then TFLEX was used to execute a query and get an answer. The model successfully classified the level of difficulties of the answer type into Easy and Hard. It is also able to classify which answer is correct and wrong. The table shows that TFLEX gives simple answers, “François Hollande”, “Taavi Rõivas”, and hard answers “Andris Berzins” a higher ranking than incorrect answers “Angela Merkel” and “Head of Government (Latvia)”. This demonstrates how TFLEX correctly determines the hard answer through sophisticated reasoning and separates the correct responses from incorrect ones.

Industry Applications

Temporal knowledge graphs (TKGs) have diverse applications:

1. Recommendation Systems: TKGs are used to model recommendation systems by analyzing users’ past behaviours such as liking, buying, watching, reviewing, and so on. It can also offer more personalized and timely recommendations, such as suggesting products based on recent browsing and purchasing trends.
2. Financial Application In financial markets, TKGs can help in holding behaviours, trading behaviours, and financial events that aid in better investment decisions.
3. Transportation: In the field of transportation, temporal knowledge graphs can be used to improve traffic prediction models and enable intelligent transportation systems. (Yuan & Li, 2021)

Conclusion

This post looks at how artificial intelligence is getting better by using Temporal Knowledge Graphs (TKGs) and a special method called Temporal Feature-Logic Embedding (TFLEX). TKGs are advanced versions of knowledge graphs that include time information, which helps in understanding and predicting complex patterns. TFLEX is designed to deal with these time-related challenges and improve how we can answer detailed questions about past and future events. This is useful in areas like finance, transportation, and personalised recommendations, where understanding time can make a big difference.

References

• Lin, X., Xu, C., E, H., Su, F., Zhou, G., Hu, T., Li, N., Sun, M., & Luo, H. (2022, May 28). TFLEX: Temporal Feature-Logic Embedding Framework for Complex Reasoning over Temporal Knowledge Graph. arXiv.org. https://doi.org/10.48550/arXiv.2205.14307
• Li, M., Tong, P., Li, M., Jin, Z., Huang, J., & Hua, X. S. (2021, May 18). Traffic Flow Prediction with Vehicle Trajectories. Proceedings of the . . . AAAI Conference on Artificial Intelligence. https://doi.org/10.1609/aaai.v35i1.16104
• Yuan, H., & Li, G. (2021, January 23). A Survey of Traffic Prediction: from Spatio-Temporal Data to Intelligent Transportation. Data Science and Engineering. https://doi.org/10.1007/s41019-020-00151-z